PERAN IT DALAM COMPETITIVE ADVANTAGE

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INFORMATION TECHNOLOGY INFRASTRUCTURE LIBRARY

PENDEKATAN MODEL INFORMATION TECHNOLOGY INFRASTRUCTURE LIBRARY (ITIL) DALAM REKAYASA SISTEM INFORMASI UNTUK PENCAPAIAN IT-GOVERNANCE

FRAMEWORK COBIT

MANAGING CONTROL OBJECT FOR IT (COBIT) SEBAGAI FRAMEWORK IT GOVERNANCE

CONTOH APLIKASI AI

APLIKASI ARTIFICIAL INTELLIGENCE (AI) DAN PENGERTIAN AGENT PADA AI

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Managing Knowledge Business Intelligence: A Cognitive Analytic Approach

Knowledge Management (KM) is a strategic tool for building Intellectual Capital (IC) information within an organization. Management using this tool as the most efficient mean for making people as most valuable assets. Efficiency within organisation is not possible unless organization practice BI on the right track. BI is closely associated with success of results generated by KM.An organization may have bulk of information which may arise problems when it comes to part of implementation. BI technologies play crucial rule for better management of such huge information. BI formulates smooth part for practitioners of KM to attain a competitive edge over its competitors. This competitive edge follows competitive personnel with better performance, work efficiency and better customer relationship management.The competitive nature of any organization is boosted up using work practices that involve a high degree of association. However, boosting skills within the organisations is not an easy task. It takes a while before skills cross a certain required threshold where tangible benefit can be obtained.

That is why the knowledge transfer is very important within the organisation especially to explicate the tacit knowledge so it can be learned by any entities in the future. The biggest problem for KM is in a part of people tacit knowledge. Conversely, tacit knowledge can disappear in case of mergers, reorganization’s, and downsizing. The contextual analytics which supported by a cognitive system is an advanced analytics system used to collect tacit knowledge. The contextual analytics techniques like relevancy ranking are used besides those like entity relation modeling, entity extraction, tagging of parts of speech, and so on. Thus, data is analyzed within a confined set of implicit and explicit knowledge. If implicit knowledge and various perspectives are included in this analysis, these contextual analytics may becomes cognitive analytics. This paper will explore the cognitive approach for analysing KM in BI environment.


Business Intelligence

Business Intelligence (BI) comprises important business process which collects and analyzes information for business decisions and actions. Particularly, it emphasizes upon use of information tools to enhance business performance. BI consist of technologies, processes and implications which allows acquiring, storing, retrieving and analyzing data for better decision making. On-Line Analytical Processing (OLAP) is a tool of BI which allows searching and testing relevant data along with computation and identification of relationships. Data Mining identifies trends, patterns and relationship among huge sum of data stored in Data Warehouse. It makes use of statistical and mathematical techniques along with technology. Decision Support System (DSS) is the association of man and machine for provision of authentic and useful information in order to support management in decision making.OLAP is one of the important components of BI used in process. OLAP has several other traditional forms. Some of them are classification, sequential patterns, regression and link analysis.Thus, BI process is a relevant approach to analysis knowledge data that required a proper process to capture and analysis tacit knowledge.

Knowledge Management

Knowledge Management (KM) is a technique of searching, acquiring, organizing and communicating
information and knowledge in organization. The knowledge can be implicit or explicit is relates to the
understanding of leadership, group efforts, individual experience and psyche of employees. Acquisition of relevant information is the process of identifying and capturing material closely associated with current goal. Retrieval of information is the second phase of KM process where organization takes out specific information from multiple sources. The captured knowledge of the organization will be process by using BI, and later by using cognitive approach the tacit knowledge will be used as part of analytic solution.

Cognitive Approach for Capturing Knowledge

Cognitive approach is able to record, analyze, remember, learn, and resolve the problem from the information that are available from the human knowledge and experiences. The current cognitive system also can perform the transferring of knowledge andused to be the best practices in data analysis industries. In these use cases, a cognitive system is designed to build a dialog between human and machine so that the best practices are learned by the system as opposed to traditional method that being programmed as a set of rules. As long as knowledge is probabilistic, it always be influenced by human and social factors, and required a cognitive way to be managed. Cognitive approach is suitable for the “more than one” hypotheses to be analyzed as it is a kind of decision support that allows people to explain new opportunities, which has an impact in a positive manner. Therefore, this paper will explain the used of cognitive approach for managing knowledge in BI environment.

METHODOLOGY

Qualitative research technique has been adopted for this paper. These qualitative techniques include the careful analysis of literature review of previous researches and proposed models of KM and BI. Theoretical framework of the research has also been driven from multiple models of previous researches. Knowledge management and business intelligence has the potential to strengthen the effectiveness and competitiveness of organizations. Thus there is a need of having a Business Intelligence integrated framework of BI and KM for achieving this goal is shownin Figure 1.


Figure 1. Theoretical framework of KM & BI integration to achieve competitiveness

In first phase of methodology is collecting data, where the managers were asked few questions relating to achievement of competitiveness through KM and use of BI in it. Some of the questions are as follow:

. . .


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Pencapaian Keunggulan Daya Saing Melalui Knowledge Management dan Business Intelligence

Knowledge Management (KM) adalah alat strategis dalam membangun Intellectual Capital (IC) pada sebuah organisasi. Manajemen menjalankan KM sebagai alat yang paling efisien untuk menghasilkan aset yang paling berharga bagi manusia sebagai pengambil keputusan. Para pengambil keputusan membutuhkan informasi yang paling relevan pada saat yang tepat untuk pengambilan keputusan yang berguna. Efisiensi dalam mengambil keputusan dapat dilakukan pada praktik-praktik di dalam Business Intelligence (BI) di jalur yang benar. Dalam hal ini, BI erat kaitannya dengan kesuksesan yang dihasilkan oleh KM. Organisasi memiliki informasi yang mungkin memiliki masalah ketika sampai pada bagian pelaksanaan. Teknologi BI memiliki peranan penting dalam melakukan pengelolaan informasi yang lebih baik dalam skala yang lebih besar. BI mampu merumuskan bagian yang mulus bagi para praktisi KM untuk meraih keunggulan kompetitif dibandingkan pesaingnya. Daya saing ini mengikuti tenaga kompetitif dengan kinerja, efisiensi kerja, dan manajemen hubungan pelanggan yang lebih baik. Teknologi BI yang berdampak pada KM secara signifikan umumnya meliputi OLAP, DSS, dan Data Mining. Ini adalah alat BI yang strategis yang harus selaras dengan strategi organisasi secara keseluruhan. Tools atau alat ini dapat membangun hubungan dua arah di lingkungan kerja antar karyawan sambil menyebarkan informasi dalam organisasi. Karyawan dalam organisasi manapun ingin dipertimbangkan dalam setiap keputusan tunggal. Hal ini akan memberikan lebih banyak loyalitas dan komitmen personil untuk bekerja dalam lingkup organisasi.

Knowledge Management

KM didefinisikan sebagai teknik pencarian, perolehan, pengorganisasian dan komunikasi informasi dan pengetahuan untuk memotivasi karyawan. Hal ini berhubungan dengan pemahaman kepemimpinan, usaha kelompok, pengalaman individu dan jiwa karyawan. Akuisisi informasi yang relevan adalah proses mengidentifikasi dan menangkap materi yang terkait erat dengan tujuan saat ini. Pengambilan informasi adalah tahap kedua dalam proses KM dimana organisasi mengeluarkan informasi spesifik yang didapatkan dari berbagai sumber.

Business Intelligence

BI terdiri dari proses bisnis penting yang mengumpulkan dan menganalisis informasi untuk keputusan dan tindakan bisnis, terutama menekankan pada penggunaan alat informasi untuk meningkatkan kinerja bisnis. BI terdiri dari teknologi, proses, dan implikasi yang memungkinkan perolehan, penyimpanan, pengambilan, dan analisis data untuk pengambilan keputusan yang lebih baik. On-Line Analytical Processing (OLAP) adalah merupakan salah satu alat pendukung BI yang memungkinkan pencarian dan pengujian data yang relevan beserta perhitungan dan identifikasi hubungan. Data Mining pada BI berfungsi untuk mengidentifikasi tren, pola, dan hubungan antara sejumlah besar data yang tersimpan di Data Warehouse. Ini menggunakan teknik statistik dan matematis seiring dengan teknologi. Sistem Pendukung Keputusan atau SPK (Decision Support System - DSS) adalah hubungan atau asosiasi antara manusia dan mesin untuk penyediaan informasi yang otentik dan berguna untuk mendukung manajemen dalam pengambilan keputusan. OLAP merupakan salah satu komponen penting BI yang digunakan dalam prosesnya. OLAP juga memiliki beberapa bentuk tradisional lainnya. Beberapa di antaranya adalah klasifikasi, pola sekuensial, analisis regresi, dan link.

Kerangka teoritis integrasi KM dan BI untuk mencapai daya saing

KM dan BI memiliki potensi untuk memperkuat efektifitas dan daya saing dalam sebuah organisasi. Dengan demikian ada kebutuhan untuk memiliki kerangka kerja BI dan KM yang terintegrasi untuk mencapai tujuan ini.

DSS sering digunakan sebagai alat kunci pada BI saat manajemen memilih beberapa strategi untuk KM. OLAP dan data mining adalah teknik modern pada BI yang berperan penting dalam pengambilan data. Integrasi KM dengan BI akan menghasilkan modal intelektual yang lebih baik yang merupakan aset paling berharga. BI dan KM juga dinilai dari berbagai model. Model sangat membantu dalam mengkategorikan bagian data warehouse dan membangun teknik data mining. Berbagai unsur BI juga harus berhubungan dengan unsur KM agar bisa menghasilkan keunggulan kompetitif. BI tidak dipraktekkan sebagai trend dalam organisasi tapi sebagai kebutuhan. Organisasi mengintegrasikannya sebagai alat utama secara sekaligus dalam membuat keputusan yang strategis. 

Dalam artikel ini integrasi juga dievaluasi dalam menyatakan langkah-langkah pertukaran antara BI dan KM yang mencakup konteks pengambilan keputusan. Wang dan Wang (2008), menyatakan bahwa pekerja pengetahuan memiliki kolaborasi dan kerjasama yang kuat dengan alat TI untuk kinerja dan sosialisasi yang lebih baik. Ia membahas dengan logika bagaimana data mining, alat BI dan KM dapat menghasilkan hasil yang lebih baik. Demikian pula, pemrosesan analitik on-line (OLAP) juga merupakan alat yang membantu saat mengekstrak data dari gudang data. Alat pengelolaan pengetahuan ini juga bekerja sambil mencapai BI kolaboratif, karena kolaborasi jauh lebih efektif daripada persaingan. Eksekutif di perusahaan multinasional memiliki pandangan yang dekat terhadap perubahan pola global.

Faktor sosial dan ekonomi adalah yang paling penting untuk melakukan analisis yang terkait dengan strategi bisnis. Analisis kualitatif faktor lingkungan juga memungkinkan eksekutif membuat keputusan yang tepat. Mereka mempertimbangkan beberapa faktor dari lebih dari satu disiplin kehidupan sosial. Di sisi lain analisis kuantitatif membantu mengidentifikasi hasil nyata. Ini juga dapat menciptakan keterbatasan untuk hasil yang dihasilkan ketika sampai pada tahap implementasi.

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Demographic Spatial Data Management in Indonesia with the Approach of Geographic Information System Model

Introduction

More rapid population growth in a particular area will gradually cause complex problem to the society and its environment. Indonesia becomes the 4th rank of most populated country in the world. Based on the result of population census in 2013, the number of populations in Indonesia was 240.5 million people. It means that Indonesia can be included as a country with the biggest population number among other developing countries after China and India. If it is compared with the census in 2000, it shows population expansion in Indonesia with approximate value 1.98% per year.

Based on the projection result of the population, the number of populations in Indonesia in 2050 is predicted to reach 366 million people3. Based on the data from World Population Datasheet, here it is the table of most populated countries in the world and the future projection in 2050 Table 1.

The impact of over load population is closely related to the width of the occupied area in a particular country. Big population can trigger some problems, and it can become the asset of a country as well. The most prominent issue is that big population can be the most influential asset of a country if human resource quality of the population is high. Although Indonesia becomes the 4th rank of its population number, Indonesia is in the 121st position in the world of its human resource quality (year 2014). Indonesia is still far left behind from China which has the highest number of population in the world, with its high quality population. The problem of population quality should be the government’s concern in handling the most prominent factor of prosperity and living quality to all citizens. Astronomically, Indonesia is located in 94˚ 45’ EL until 141˚ 05’ EL and 6˚ 08’ NL until 11˚ 15’ SL, in which equator area 1˚ is equivalent with 111 km. It means that Indonesian extends ±7,700,000 km2 with its land total area ±1,826,440 km2, and it is divided into 34 provinces. As the fourth country with the biggest population of the world with the population number ±238,452,952 people in the middle of 2015, the average population in every 1 km2 in Indonesia was occupied around 131 people /km2. Of course, a system to ease periodical monitoring about demography other than using census is significantly needed.

The width of Indonesia area in the map of population distribution seems uneven in 34 provinces. Based on the census result in 2010, there was 60% population occupying Java Island. However, Java Island is only 7% from the total area of Indonesia. On the other hand, Kalimantan Island which has bigger area was only occupied by 5% of Indonesia total population. Here they are some demographic problems in Indonesia:
  1. Problem of Total Fertility Rate (TFR). The increase of fertility rate will be the government’s burden in accommodating physical aspects like health facilities rather than its intellectual aspect. The increase of fertility will cause high rate of population improvement in developing countries that will negatively correlate to the prosperity of the population.
  2. Problem of Mortality Rate (MR). The high rate of life expectancy of the population requires bigger role of the government to provide any shelter facilities.
  3. Problem of Population Composition (PC). Indonesia has imbalance population composition that can cause new population problems.
By the existence of those problems, the researchers were motivated to conduct a study about demographic data management in Indonesia with the approach of geographic information system (GIS) model. Although the discussion related to demographic data management has widely been discussed in some other researches, the focus of the study, however, is to emphasize on demographic data management as a device of data monitoring and projection of population density with the approach of GIS model in order to control the population. The model of the system is expected to have a particular strength in monitoring demographic data and its control in every provincial area in Indonesia.

Proposed Method

The study was conducted to obtain a system that can be used to monitor the demographic data by using GIS model approach. The study was divided into three steps, as following:
  1. Spatial data and demographic data initiation.
  2. Spatial and non spatial data integration. It is the step in correlating spatial data and demographic data into the database.
  3. Indonesian demographic data visualization.
The system was designed as user friendly as it is expected by common people toward Information Technology (IT) to be able to access demographic data through web Figure 1.

Demography Theatrical

Some related researches have been done like the research who investigated about map making process by using Scalable Vector Graphic (SVG). In their study emphasize on SVG technology as the visualization of area mapping. In its development, SVG has become programming language to build interesting sites. SVG is a web graphic file format to present the graphics and to describe 2 dimension pictures base on eXtensible Markup Language (XML).

Another study investigated demographic problems in Indonesia. The focus of the study is demographical problem faced by the government as well as the impact of population nationally. Another problem analyzed is about employment showing that 77% employees in Indonesia are still in low education level. The impact toward per capita income will significantly influence toward the citizens’ living quality. Other demographical features also become the concern of the study such as the rate of divorce and marriage that will influence on fertility and mortality rate that can be the indicator as a country’s prosperity. The indicators of prosperity in a country can be significantly influenced by several factors such as the rate of fertility and mortality as they are noted by Statistical Bureau. In simple way it can be explained that people are the subjects as well as the objects of development. Thus, if there is no initial anticipation, it will cause national imbalance. In further, based on the literature review presented above monitoring is importantly needed toward population development in order to keep the balance of the population and the suitability of government’s program to reach national prosperity by using geographical information system that will be developed further.

Demography is a scientific study related to demographical number, population spread and composition as well as how those factors change from time to time. Demographic science can be in the form of quantitative organd qualitative data. Quantitative demography mostly uses statistical numbers and mathematical number. On the other hand, qualitative demography explains demographic aspects within the method of analytical description. In addition, demographic studies examine the development, phenomena, and problems related to demography and the social situation around its environment systematically. Demographic science that needs our attention concerns more to inter discipline studies integrated with demographic analysis that people may know as social demography. There are several opinions mentioning about the definition of demography:
  1. It is a science studying population in any particular area within its number, structure (composition) and development (change).
  2. It is a science examining the number, distribution, territorial, population composition, and the change as well as the causes that usually appear because of the rate of fertility, mortality, migration, and social mobility.
  3. It is a mathematical and statistical studies toward numbers, composition, spatial distribution of the population, and the change of the previous aspects that always happen as the impact of fertility, mortality, marriage, migration, and social mobility. 
Three important aspects in studying demography such as fertility, mortality, and migration as it can be seen in Figure 2. In addition, there are two supporting aspects in demography; those are social mobility and the rate of marriage. The data of population number can be obtained from these several ways: 
  1. Population census. It is a whole process from gathering, processing, presenting, and assessing demographic data that relate to the characters of demography, social economy, and environment.
  2. Registration of the population is the process of population data recording conducted by individual party when there is population change. It is done by domestic affair ministry through local village offices.
  3. Population survey is the process of information recording related to the population based on the specialty of wider and deeper studies.
The example is mobility survey of Yogyakarta citizens, and fertility survey of Yogyakarta citizens. Population survey was done because population census and registration have limitation and weakness. Demographic information can be obtained through census. In addition, the data used in the study is secondary data from Statistic Bureau as a simulation. The spatial data of Indonesian area is adopted from Google Maps API from www.google.com.

Result and Discussion

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Knowledge Management (KM) dan Business Intelligence (BI)

Banyak kalangan yang masih merasa ambigu dan belum begitu mengerti perbedaan andara Business Intelligence (BI) dan Knowledge Management (KM). BI merupakan sebuah aplikasi, atau umumnya disebut sistem. Sistem ini mampu menganalisa data, dan memberi sugesti dalam proses pendukung keputusan. BI adalah sepenuhnya teknologi dan berbasis aplikasi yang berfungsi untuk menganalisa data, melakukan proses terhadap data, dan memberi hasil atau sugesti untuk kemudian menjadi dasar dalam proses pengambilan keputusan oleh manusia (misal: manager dalam perusahaan).

KM pada umumnya adalah sebuah proses yang secara sistematis melakukan pencarian, memilih, mengelompokkan, dan menyusun informasi. KM sendiri memiliki proses yang panjang dalam melakukan pengambilan keputusan, dan KM mensupport aktivitas yang berbasis pembelajaran. Sehingga, KM kerap melakukan analisa dari data dan informasi yang tersedia pada masa lalu, masa sekarang, kemudian melakukan pengolahan data/informasi untuk tujuan prediksi dalam mendukung keputusan.

Lalu mengapa BI dan KM perlu untuk diintegrasikan?

Pada dasarnya BI adalah sebuah sistem berbasis aplikasi yang mengolah melakukan pengolahan data berdasarkan informasi dan data yang sudah tersedia. Data dalam hal ini adalah segala bentuk informasi yang sudah terdokumentasi. Sementara aktivitas KM sendiri berada dalam dua jenis pengetahuan (informasi); Tacit dan Explicit. Yang mana Tacit adalah pengetahuan yang bersifat intuitif, sementara Explicit adalah pengetahuan yang bersifat dokumentatif. BI berada pada layer dokumentatif, semntara KM berada pada layer intuitif dan juga dokumentatif.

Dalam integrasi BI dan KM, terdapat pertanyaan lanjutan, yaitu; "apakah KM-BI, atau BI-KM?"

Kognitif dalam paradigma Internet of Things

Apa itu Kognitif (Cognitive)? Sebelum kita bergeser atau bergerak ke pemahaman kognitif, secara general kita perlu memahami pengertian Kognisi (Cognition) terlebih dahulu. Kognisi adalah sebuah pemahaman (secara mental) yang berkaitan dengan proses bagaimana seseorang memahami lingkungannya (dunia) dan bertindak serta melakukan respon terhadapnya. Dengan kata lain, kognisi adalah sebuah proses dan tindakan yang dilakukan oleh manusia dengan 'kesadaran' yang penuh. Berangkat dari pengertian kognisi di atas, maka dapat kita simpulkan bahwa kognitif adalah kemampuan otak dalam mengolah informasi, memecahkan masalah, melakukan observasi, menyusun dan merencanakan mekanisme dalam mempelajari dan menyimpulkan informasi yang didapat.

Kemampuan kognitif adalah keterampilan berbasis otak dan syaraf manusia dalam melaksanakan tugas dari yang paling sederhana sampai yang paling kompleks. Kemampuan kognitif memiliki keterkaitan dengan mekanisme bagaimana kita belajar, mengingat, memperhatikan, dan memecahkan masalah secara otomatis. Misalnya, untuk menjawab telepon, setiap otak manusia melibatkan persepsi (mendengar nada dering), pengambilan keputusan (menjawab atau tidak), keterampilan motorik (mengangkat dan menjawab), kemampuan bahasa (berbicara dan memahami bahasa), keterampilan sosial (menafsirkan nada suara dan berinteraksi dengan baik dengan manusia lain). Cukup familiar? Apakah kita pernah melakukan analisa tahap per-tahap untuk menjawab panggilan telepon? Atau kita melakukan otomasi dengan refleks mengangkat telepon yang sedang berdering, atau mengacuhkannya? Atau, mungkinkah otak kita memang melakukan analisa proses tanpa kita sadari, dan kemudian memberi perintah menjawab telepon atau tidak?

Pertanyaan selanjutnya adalah; adakah atau mampukah sebuah teknologi (sistem komputer) melakukan analisa dari setiap informasi dan data secara kognitif serta beraksi atau mengambil keputusan secara kognisi?

Jawabannya adalah BISA! 

Lalu, apa hubungannya dengan Iternet of Things (IoT)?

Sebelumnya kita kaji terlebih dahulu apa pengertian umum mengenai IoT. 

Margaret Rouse pernah menulis sebuah pengertian dan tulisan ringan di halaman techtarget mengenai IoT. Margaret Rouse mengatakan bahwa IoT merupakan sebuah sistem atau perangkat komputasi yang mampu membuat manusia, hewan, dan benda lain saling terhubung secara digital dan mampu saling bertukar data melalui jaringan (dengan sebuah alat pengenal/identifikasi sehingga semua dapat saling bertukar data tanpa bantuan manusia untuk meneruskan atau membantu proses pertukaran data tersebut). Kata jaringan di sini merupakan jaringan yang luas, yaitu Internet. 

Kita mampu berkomunikasi dan melihat secara langsung saudara kita yang ada di islandia melalui video call dari perangkat telepon genggam, tanpa perlu 'ribet' untuk memahami proses pertukaran data. Gadget kita sudah melakukan semuanya dengan satu syarat; terhubung ke Internet!

Google mampu mendeteksi tingkat kemacetan dengan cara memantau pergerakan semua perangkat dalam satu area dengan fasilitas map-nya. Tentu saja pertukaran data akan lancar, jika users (anda dan saya) mengizinkan google (melalui fasilitas map nya) untuk mengakses gps di perangkat kita. Plus, mengupload data ke mereka secara periodikal.

Dalam perspektif IoT, pentingnya analisa adalah karena perspektif akan apa yang hendak kita lakukan terhadap data dan informasi. Pentingnya analisis yang bersifat kognitif adalah, karena sistem harus dapat bekerja sesuai dengan tugasnya terhadap lingkungan sekitarnya.

Pernahkah anda merasakan lapar namun bingung harus melakukan apa terhadap makanan yang telah tersaji di hadapan anda? Jika pernah, maka otak yang anda miliki tidak mampu melakukan analisa dengan benar. 

Pernahkah anda 'diomeli' oleh orang tua anda ketika telepon berdering dan anda malah berdiri kebingungan? 

Atau, apa reaksi anda jika adik atau kakak anda buru-buru beranjak dari tempat duduk dan mengangkat telepon sambil berhalo-halo ria padahal telepon di rumah tidak berdering sama sekali?

Tiga kejadian seperti di atas adalah merupakan gambaran dari gagalnya sebuah sistem Analisa Kognitif. 

Jika anda merasa haus dan langsung mengambil air minum yang telah tersedia dan meminumnya, itu merupakan gambaran sederhana dari sebuah keberhasilan sistem Analisa Kognitif.

Adakah sebuah teknologi yang seperti itu?
Jawabannya adalah: "Ada"!

Mampukah sebuah sistem melakukan hal yang serupa?
Jawabannya adalah: "Mampu!"

Pertanyaan yang lain adalah: "Bagaimana dengan Big Data? Triliunan data yang tersebar di Internet?"

Well, saya sedang memikirkan kemampuan berfikir yang manusia miliki. Mampukah manusia menampung seribu pertanyaan dalam satu menit dan memilah mana pertanyaan yang berhubungan dengan 'apa yang harus dijawab' dan memutuskan untuk memberi jawaban yang tepat dalam tempo kurang dari dua detik?

Improving Employees Retention Rate Through Knowledge Management and Business Intelligence Components

Introduction

Knowledge Management (KM) is opted as strategic tool by executives to keep their team motivated. These teams consist of employees who have higher level of inspiration and competency. In modern era, worker turnover rate is crucial issue for businesses to attain competitive advantage. Employee’s retention can be measured by their level of motivation and task orientation in work environment. No organization can compromise for loss of skilled employee because it is much essential than any other source of development. The KM is platform that supports strategic business decisions with people, process, and technology aspects.

It is widely believed that job satisfaction is wholly dependent upon leadership integrity and justified processes of decisions within organization. Thus, intelligence has been a significant factor in managing human capital. It covers all aspects of customer, competitor, markets, technological and environmental intelligence. Business Intelligence (BI) is process that generates valuable information with DSS (Decision Support System), data mining and advanced analytics for corporate strategic decisions. It is constant approach for creating and enriching significant information in the managerial context. For knowledge based organizations BI is considered as backbone in organizational structure. It turns data into actionable intelligence for executives to make strategies for work environment stability. Business are keen interested to use latest technology for meeting external and internal competition. BI adopts an effective aid to intelligence practitioners for realizing complete picture of resources in form of humans.

Many past studies have verified that utilizing high association work practices “can boost firm competitiveness”. Competitive advantage on the basis of employees is the most focused strategic goal for firms. Executive do believe that it is not so easy to imitate human mind. Skills and abilities take time to reach to a stage where employee’s intellectual worth even crosses tangible assets.

Within firms, KM is the heart of progression planning. Businesses that properly manage the alteration of new employees by replacing old ones allow job and industrial information to be transferred through the organization to ensure that such particulars are not lost. Either employees leave organization voluntarily or involuntarily. Certain business concerns included complexity, references to the increasing pace of change, globalization, information flow, economy, networking and proactively. Massive development in the information technology and communications demand to adopt BI applications in order to deal with business mechanisms, staying at the marketplace, rivalry, customer control, and retention.

The question session covers all aspects of BI to empowering the KM significance for organizational competitiveness in market place. Therefore, the following key research questions are proposed:
  1. What is the relationship between business intelligence, knowledge management, and employee retention in organizational context?
  2. How business intelligence ensure to reduce employees turnover rate?
  3. How business intelligence empowering the knowledge?
  4. To what extent are BI and KM being used in influencing retention and increase competitiveness?
  5. How does Business intelligence control internal and external operations in competitive environment?
  6. How efficiently Knowledge management build relation between employee and workplace?
With superior tools of BI, now employees can also easily convert their business information via the systematic intelligence to solve many business issues with technological advancement. In the light of multiple views and arguments, the model is proposed for KM and BI integration. Open ended questions facilitated individuals to openly share their views.

Fig I: Building relationship between components of BI and KM to maximize Employee Retention

For this empirical research, qualitative research technique is practiced for collection of adequate facts and figures. It included secondary data of selfinterviews to executives and managers in corporate sector, evaluation of existing literature and comparison with previous, past researches, journal, and articles and proposed models of BI to empowering KM. Employee’s feedback is also considered by secondary data resources while analyzing corporate practical strategies. Past data is also preferred as proof to their performance management activities within organization as financial reports, market share and customer service data bases. All those data are collected trough secondary data for this paper purposes.
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Persepsi Resiko Dalam Hubungan Kausal Antara Kecederungan Penggunaan Internet Dengan Kesediaan Nasabah Untuk Menggunakan Fasilitas Internet Banking Dan Sistem Pembayaran Online

Penelitian ini dilakukan untuk mengetahui sejauh mana pengaruh dari persepsi resiko terhadap kesediaan nasabah untuk melakukan layanan internet banking dan pembelian online, serta untuk mengukur apakah pengalaman dan kecenderungan menggunakan internet memiliki pengaruh yang signifikan dalam meningkatkan kesediaan nasabah untuk menggunakan fasilitas online banking dan pembayaran online. Ada beberapa hal yang mempengaruhi kesediaan konsumen dalam menggunakan layanan internet banking antara lain adalah bahwa factor kecenderungan menggunakan internet dan faktor pengalaman menggunakan internet memiliki pengaruh yang positif terhadap kesediaan nasabah untuk menggunakan fasilitas internet banking. Persepsi resiko dalam menggunakan fasilitas layanan internet banking memiliki pengaruh yang negative terhadap kesediaan nasabah dalam menggunakan fasilitas internet banking, karena penggunaan media internet tentu saja memiliki beberapa macam bentuk resiko yang tentu saja dapat diterima konsumen. Faktor kecenderungan menggunakan internet dan faktor pengalaman menggunakan internet memiliki pengaruh yang negative terhadap persepsi resiko dalam kesediaan nasabah untuk menggunakan fasilitas layanan internet banking. Apakah persepsi resiko memiliki peran mediasi dalam hubungan antara kecenderungan menggunakan dan pengalaman menggunakan internet dengan kesediaan nasabah untuk menggunakan fasilitas internet banking merupakan hal yang menarik untuk diteliti. Penelitian ini menggunakan model dari penelitian sebelumnya yang dilakukan oleh Kuhlmeier dan Knight (2005). Kesediaan untuk menggunakan jasa layanan internet banking merupakan suatu bentuk peluang untuk perkembangan E-Commerce, khususnya di Yogyakarta, Indonesia.

Pendahuluan

Dalam persaingan bank yang sangat ketat, faktor keputusan nasabah menjadi perhatian yang serius. Masing-masing bank mempunyai pengungkapan yang beraneka ragam untuk memberikan segala sesuatu yang eperti apa yang diharapkan, seperti “Nasabah adalah raja”, “Keputusan konsumen adalah tujuan kami”, dan sebagainya. Inovasi pengembangan sistem layanan perbankan dewasa ini banyak dilakukan dengan memnggunakan bantuan teknologi informasi. Penggunaan teknologi informasi memberikan berbagai macam kemudahan, fasilitas, dan layanan bagi nasabah. Pihak perbankan harus segera dapat menyesuaikan diri dengan menerapkan sistem operasi perbankan dengan menggunakan bantuan teknologi informasi dalam memberi layanan bagi nasabahnya. Selain itu, layanan dengan menggunakan fasilitas teknologi informasi harus didukung oleh kemampuan pihak perusahaan untuk menciptakan suatu program yang mudah untuk digunakan nasabahnya (user frienly). Salah satu layana perbankan yang digunakan fasilitas teknologi informasi adalah online banking dalam melakukan kegiatan pembayaran secara online, atau internet banking. Internet banking merupakan probosan baru dalam dunia perbankan yang diberikan perusahaan jasa perbankan untuk melayani transaksi keuangan nasabah secara online, baik itu transaksi dengan menggunakan kartu kredit, kartu debit, dan transfer keuangan secara online melalui internet banking (Mäenpää, 2006:304).

Kemampuan suatu perusahaan jasa perbankan untuk memberikan layanan internet banking yang baik akan menurunkan resiko dan akan memberikan konstribusi positif dalam meningkatkan penggunaan jasa layana internet banking dalam setiap transaksi keuangan yang dilakukan oleh nasabah. Penggunaan jasa layanan internet banking diyakini akan memberikan lebih banyak manfaat dan kemudahan bagi nasabah maupun bagi perusahaan dibandingkan jika menggunakan sistem transaksi keuangan yang konvensional.

Information Technology

Teknologi informasi memiliki peranan penting dalam perekayasaan sebagai besar proses bisnis. Kecepatan, kemampan pemrosesan informasi, dan konektivitas komputer serta teknologi internet dapat meningkatkan efisinsi proses bisnis. Teknologi informasi tidak hanya terbatas pada teknologi komputer (seperangkat alat keras dan lunak) yang digunakan untuk memproses dan menyimpan informasi, melainkan yang mencakup teknologi informasi untuk mengirimlan informasi. Teknologi informasi secara de facto sudah menjadi landasan untuk melakukan bisnis.

Theory of Reason Action (TRA)

Theory of Reason Action dikembalikan oleh Fishbein and Ajzen (1975) yang membantu para peneliti untuk memahami dan memprediksi sikap dan perilaku individu (Davis at al., 1989:17). TRA telah berhasil memprediksi dan menjelaskan perilaku pada berbagai wilayah kajian. Teori tersebut paling sering digunakan sebagai model teoritis dalam sistem informasi. Kinerja seseorang mengenai perilaku tertentu ditentukan oleh tujuan untuk menjalankan prilaku, dan tuuan tersebut ditentukan oleh sikap dan norma subyektif (Davis et al., 1989:18). Beberapa faktor tertentu dalam menetapkan perilaku penerimaan teknologi, antara lain: behavioral intention untuk menetapkan perilaku, apabila behavioral intention digabungkan dengan sikap atau attitude dan kaidah norma atau subjective norm.

Theory of Planned Behavior (TPB)

Menurut Hermana (2005:22) TPB merupakan perluasan dari TRA dengan penambahan variabel perceived behavioral control-selain perilaku dan norma subyektif, untuk menerangkan situasi dimana individu tidak memiliki pengendalian terhadap perilaku yang diinginkan (Azen, 1991, seperti yang dikutip oleh Chau dan Hu (2001:53). Menurut King (2003:8) penelitian menurut adopsi teknologi yang sudah menggunakan TRA dan TPB sebagai model teoritisnya, tettapi TRA lebih umum digunakan. Chau dan HU (2001:53) menggabungkan TPB dengan TAM. Variabel pengengaliannya diukur dengan 3 indikator yaitu menggabungkan TPB dengan TAM. Variabel pengengaliannya diukur dengan 3 indikator yaitu kemampuan, pengetahuan, dan sumberdaya yang dimiliki.

Technology Accepance Model (TAM)

TAM yang diperkenalkan pertama kali oleh Fred D. Davis pada tahun 1986, adalah adaptasi dari TRA yang dibuat khusus untuk pemodelan penerimaan pengguna terhadap sistem informasi. Menurut Davis (1989:11), tujuan utama TAM adalah untuk memberikan dasar untuk penelusuran pengaruh faktor eksternal terhadap kepercayaan, sikap, dan tujuan pengguna. TAM menganggap bahwa 2 keyakinan, yaitu persepsi manfaat (perceived usefukness, disingkat PU) dan persepsi kemudahan penggunaan (perceived ease of use, disingkat PEOU), adalah pengaruh utama untuk perilaku penerimaan komputer. Pada umumnya penguna teknologi akan memiliki persepsi positif terhadap teknologi yang disediakan. Persepsi negatif akan muncul sebagai dampak dari penggunaan pernah mencoba teknologi tersebut. Artinya persepsi negatif berkembang setelah pengguna pernah mecoba teknologi tersebut atau pengguna berpengalaman buruk terhadap pengguna teknologi tersebut. Sehingga model TAM dapat digunakan sebagai dasar untuk menentukan upaya-upaya yang diperlukan untuk mendorong kemauan menggunakan teknologi.

Teknologi Analytics Dalam Validasi Keputusan

Setiap organisasi membutuhkan tempat data internal dalam memanfaatkan sumber-sumber dan aliran data baru. Perangkat pintar yang terhubung akan meminimalisir keterlibatan manusia dari lingkaran pengguna dalam beberapa kasus, sehingga perangkat akan membuat keputusan dan menyesuaikan diri sendiri, mengoreksi dan memperbaiki diri mereka sesuai dengan yang diperlukan. Istilah ini disebut sebagai Learning Machine. Learning machine menggunakan pendekatan secara analitik untuk memaknai data. Setiap data yang digunakan dalam keperluan teknik analitik pada machine learning adalah merupakan jendela menuju Internet of Things (IoT).

Kapabilitas Internet of Things (IoT) / Internet of Things Capabilities

Empat jenis dari IoT Capabilities:
  1. Monitoring - Sensor terhadap operasi penggunaan dan performa data.
  2. Control - Fungsi dari alat-alat yang dapat dipersonalisasi dan dikontrol.
  3. Optimization - Umpan balik secara terus menerus dari pemantauan dan kontrol untuk meningkatkan efisiensi, kinerja yang lebih baik, perawatan, diagnostik, dan perbaikan.
  4. Autonomy - Monitoring, kontrol, dan optimasi memberi peluang terhadap operasi independen dan berkomunikasi dengan sistem lainnya, melakukan interaksi dengan lingkungan, melakukan personalisasi, diagnosa terhadap diri sendiri, dan perbaikan.

Setiap kali perangkat cerdas terhubung ke internet maka akan semakin besar peluang untuk melakukan perubahan sesuai dengan yang dibutuhkan oleh user, mengingat banyaknya data yang dapat dikumpulkan dan luasnya batasan komunikasi yang dapat dimanfaatkan. Kita bahkan bisa memantau operasi peralatan sistem atau mesin dan kemudian memperluas batas-batas campur tangan manusia dengan mengontrol beberapa buah peralatan atau beberapa sistem. Hal ini kemudian akan memberi pertimbangan baru akan peran manusia dalam menjalankan sebuah sistem atau mesin, karena sebagian besar fungsi yang terdapat pada sistem atau mesin tersebut adalah otomatis. 

Mesin atau sistem dapat menggunakan penilaian mereka sendiri untuk membuat perubahan, koreksi, atau melakukan penyesuaian. Manusia tidak perlu memantau secara real time (tergantung pada proses). Pemantauan yang diperlukan kemungkinan hanya dalam proses menentukan pengambilan data dan memprosesnya (sesuatu yang harus dilakukan dengan data di beberapa titik atau data khusus).

Otonomi yang seperti ini membutuhkan kecerdasan yang lebih besar. Kecerdasan tersebut sangat mungkin dilakukan pada algoritma operasi otonom yang membutuhkan menggabungkan pendekatan machine learning yang mampu beradaptasi untuk menghadapi situasi baru ke dalam algoritma inti yang digunakan untuk pemantauan, pengendalian, dan optimasi.

Analytics dan Machine Learning

Data dan fungsi dapat diakses dari lokasi manapun melalui jenis perangkat khusus menyediakan konteks di mana pengguna dapat mengakses data. Misalnya sebuah gelang kebugaran dapat mengakses data tentang kesehatan fisik pengguna melalui iPhone atau laptop dalam konteks latihan tertentu. Dalam hal ini, gelang kebugaran bertindak sebagai sensor IoT serta menyediakan sarana untuk mengakses dan mengkonsumsi data. Perangkat ini juga menggolongkan perangkat lain melalui fungsi perangkat lunak. Data yang disediakan oleh perangkat dapat menawarkan wawasan tambahan tentang penggunaan user/pengguna dan preferensi, yang dapat dimanfaatkan ketika memperbarui fungsi dan mengembangkan fitur baru. 

Jika agregasi data pada populasi pengguna dapat dikombinasikan dengan dataset lainnya, maka wawasan baru akan dapat menjelaskan data epidemiologi, aktivitas tingkat populasi, gaya hidup, dan data demografis. Informasi ini memiliki nilai yang sangat penting untuk pebisnis, penyedia layanan kesehatan, perusahaan asuransi, dan lembaga pemerintah. Algoritma machine learning dapat digunakan untuk membuat prediksi berdasarkan pola data ini. Sebagai contoh, dalam sebuah studi Mayo Clinic, data aktivitas berkorelasi dengan tingkat pemulihan untuk pasien dengan penyakit jantung.

Machine learning dan algoritma prediktif merupakan dasar untuk sejumlah perangkat cerdas yang terhubung dengan pengguna. Termostat Nest misalnya, adalah contoh dari perangkat yang memanfaatkan pola data dalam memprediksi suhu yang disukai/diinginkan oleh user/pengguna di ruang tertentu pada waktu tertentu dalam sehari. Yang menjadi tantangan paling menarik adalah: Algoritma harus mampu merasakan, menanggapi, dan beradaptasi. Misalnya, sebuah mobil yang bertanggung jawab lebih dari sopir, dalam hal mobil melakukan interaksi dengan lebih banyak sumber lingkungan data (sensor, lampu, mobil-mobil lain, dan sebagainya). 

Kelas aplikasi lain yang tak kalah pentingnya adalah dalam hal 'otomasi' industri, logistik dan transportasi, jaringan listrik dan energi sistem, manajemen lalu lintas, sistem keamanan, dan kemampuan komunikasi antar sistem akan memberi kemudahan pada mesin dalam berkomunikasi langsung dengan mesin lainnya. Selain itu, aplikasi sistem akan membantu mesin dalam menafsirkan dataflows berdasarkan algoritma yang dapat 'berevolusi' dan 'beradaptasi', sehingga mesin dapat mencapai titik akhir yang diinginkan sesuai dengan parameter operasional tertentu.

Machine Learning, Business Intelligence, dan Big Data Analytics

Begitu mendengar istilah data mining, secara naluriah kita tahu bahwa itu bukan hanya tentang mengumpulkan data, dan kita pasti sepaham bahwasanya seberapa besar dan banyaknya data yang kita peroleh tidak selalu akan membuat kita menjadi pintar dan cerdas. Melainkan hanya membuat kita menjadi salah satu dari pemulung atau pengumpul yang memiliki tumpukan barang di seluruh pelosok rumah atau gudang kita. Ya, hal tersebut juga berlaku bagi mesin dan sistem. Big data sangat mungkin untuk dibuat menjadi sesuatu yang terorganisir dengan baik dan bisa memiliki potensi penggunaan yang membuatnya lebih bernilai. Tapi akankah kita pernah mengambil itu dan menggunakannya? Apakah kita akan benar-benar mendapatkan keuntungan dari apa yang telah susah payah kita kumpulkan? Kemungkinan besar tidak. Namun tujuan utamanya adalah untuk mengubah data ke dalam tindakan yang dapat memberi kontribusi. Business Intelligence.

Big Data Analytics

Sebagian besar data mining dan teknik analisis statistik bergantung pada teknologi DBMS relasional, data warehouse, ETL, OLAP, dan BPM. Sejak akhir 1980-an, berbagai algoritma data mining telah dikembangkan oleh para peneliti dari komunitas intelligence, algoritma, dan basis data buatan. Beberapa diantaranya yang paling terkenal adalah: C4.5, k-means, SVM (Support Vector Machine), Apriori, EM (Expectation Maximization), PageRank, AdaBoost, kNN (k-Nearest Neighbors), Naïve Bayes, dan CART. Algoritma tersebut mencakup klasifikasi, clustering, regresi, analisis asosiasi, dan analisis jaringan. Sebagian besar algoritma data mining yang populer ini telah dimasukkan dalam sistem data mining komersial dan open source. Kemajuan lain seperti jaringan saraf tiruan untuk klasifikasi/prediksi dan clustering dan algoritma genetika untuk optimasi dan pembelajaran mesin memiliki kontribusi untuk keberhasilan data mining dalam aplikasi yang berbeda.

Machine learning masih memiliki potensi untuk menjadi teknologi yang mainstream di perusahaan-perusahaan industri perangkat lunak, dalam penggabungan dengan teknologi lain yang berkaitan dengan kecerdasan buatan dan komputasi kognitif, dalam bisnis intelijen dan analisis industri. Potensi machine learning dalam menangani data dalam jumlah besar dan kemampuannya dalam ekstraksi pengetahuan dari big data tersebut menjadikan machine learing semakin popular dalam penggunaannya ke arah analisis prediktif dan data mining. Yang membuat machine learning selalu menjadi bahan yang menarik untuk dikaji adalah kaitannya dengan masalah yang kompleks mengenai algoritma yang digunakan harus beradaptasi dengan perubahan kondisi yang berlangsung secara terus menerus. Hal ini kemudian menjadi sebuah aplikasi yang sukses dari teknik learning machine misalnya dalam implementasi pendeteksi spam. Peran data analytics berubah menjadi pendamping dalam sistem pendukung keputusan secara otomatis, atau otomatisasi keputusan.


Otomatisasi Keputusan

Tahap keputusan bisa terjadi dalam dua cara: 
  1. Sebagai keputusan yang didukung dan dibuat oleh pengguna.
  2. Memungkinkan sistem untuk mendelegasikan kemampuan dalam membuat keputusan untuk dirinya sendiri.
Pada poin kedua maksudnya adalah mengotomatisasi proses pengambilan keputusan berdasarkan analisis sebelumnya dan membiarkan sistem untuk belajar, menyesuaikan, dan memutuskan dengan mendelegasikan keputusan untuk sistem, untuk proses memperluas jangkauan analisis untuk analisis prediksi, pesan peringatan dini, dan bahkan penemuan data. Ada sejumlah kasus di mana machine learning digunakan untuk meningkatkan kemampuan organisasi dalam memenuhi kebutuhan analisis, terutama untuk analisis yang diterapkan untuk platform Big Data. Beberapa contoh dari yang diterapkan untuk analisis Big Data yang lebih yang berorientasi bisnis adalah seperti facebook, yang mengatur apa yang akan ditampilkan di timeline user, mulai dari feeds sampai dengan iklan (sponsor) yang relevan dengan user tersebut. Tentu saja, keputusan dalam menampilkan feed dan iklan dilakukan oleh algoritma, bukan oleh manusia.

Paradigma Machine Learning dalam Bisnis dan Sistem Kognitif

Machine learning, bersama dengan banyak disiplin ilmu lain dalam bidang AI (Artificial Intelligence) atau kecerdasan buatan dan sistem kognitif dewasa ini semakin populer. Bahkan memberi dampak yang signifikan dalam waktu yang tidak begitu jauh pada industri perangkat lunak. Machine learning tercakup dalam beberapa konsep dasar disiplin ilmu yang memiliki potensi besar dalam mengubah sistem Business Intelligence (BI) atau bisnis intelijen dalam skala analitik.

Apakah Machine Learning itu?

Dalam istilah yang sederhana, machine learning adalah cabang dari disiplin ilmu yang lebih besar, yaitu Artificial Intelligence (AI) atau Kecerdasan Buatan yang melibatkan desain dan konstruksi aplikasi komputer atau sistem yang mampu belajar dan melakukan pengolahan sendiri berdasarkan masukan data. Disiplin ilmu dari machine learning juga menggabungkan disiplin ilmu analisis data seperti analisis prediktif dan data mining, termasuk data mining untuk pengenalan pola. Salah satu aplikasi yang lebih penting dari machine learning adalah untuk 'mengotomatisasi' proses akuisisi basis pengetahuan yang digunakan oleh apa yang kita sebut dengan sistem pakar, sistem yang bertujuan untuk meniru proses pengambilan keputusan keahlian manusia di lapangan.

Machine Learning Yang Membangun Bisnis.

Keberhasilan penerapan machine learning dalam disiplin ilmu tertentu seperti pengenalan suara, visi komputer, bio-pengawasan, dan pengendalian robot memberi pengaruh terhadap minat dan adopsi teknologi bahasa mesin yang cukup signifikan, khususnya selama dua dekade terakhir. Pendekatan utama pada bidang teknologi ini termasuk dalam menggunakan jaringan saraf tiruan, sistem pembelajaran berbasis kasus, algoritma genetika, dan pembelajaran analitik (analytical learning). Hal yang paling menarik adalah bagaimana keseluruhan teknologi dalam sebuah aplikasi dan algoritma sains tersebut kemudian menjadi sebuah aplikasi komersial dan bisnis.

Machine Learning untuk Bisnis yang Realistis.

DSS (Decision Support System) atau Sistem Pengambilan Keputusan (SPK) berbasis pengetahuan dapat memberi saran atau merekomendasikan sebuah tindakan untuk manajer. DSS merupakan sebuah sistem komputer dengan keahlian pemecahan masalah khusus, yang mengadopsi atau meniru keahlian manusia. 'Keahlian' terdiri dari pengetahuan tentang domain tertentu, dan pemahaman tentang masalah dalam domain tersebut untuk memecahkan masalah. Dalam hal ini ada unsur yang jelas untuk meningkatkan adopsi teknologi dan metodologi, seperti kolaborasi machine learning dengan manajemen secara intensif dan meningkatkan data non-tradisional (relasional). Kebutuhan untuk sistem dalam memecahkan kompleksitas bertepatan dengan fenomena seperti Big Data dan analisis canggih dalam bisnis yang memberikan ruang alami untuk pintu masuk terhadap learning machine dalam membantu mengatasi beberapa set 'big data' dalam kompleksitas pengetahuan untuk analisis data dan pengambilan keputusan.

Pada akhirnya, penggunaan Machine Learning sebagai bagian dari Big Data dan Advanced Analytics memiliki peran tersendiri dalam pembentukan daerah baru yang disebut Sistem Kognitif atau Cognitive System.

Cognitive Computing dan Machine Learning

Teknik Komputasi Kognitif (Cognitive Computing) merupakan sebuah model inovasi komputasi secara analitik dalam pemrosesan bahasa alami atau natural language dan pembelajaran mesin (machine learning). Inovasi yang dihasilkan dalam teknik ini adalah sebuah simulasi proses berfikir manusia secara komputasi. Komputasi Kognitif menciptakan sebuah Sistem IT yang secara otomatis mampu menyelesaikan masalah tanpa bantuan manusia. Sistem ini secara komputer sains disebut juga sebagai sistem yang mampu belajar sendiri dan menyelesaikan masalah sendiri dengan algoritma tertentu, atau disebut juga dengan 'self-learning'.

Sekilas mengenai Cognitive Computing

Cognitive Computing atau Komputasi Kognitif

Cognitive Computing atau komputasi kognitif. Apakah itu? Komputasi kognitif secara general dapat dikatakan sebagai sebuah simulasi dari proses pemikiran manusia ke dalam bentuk dan model terkomputerisasi atau terkomputasi. Komputasi kognitif melibatkan sistem belajar (self-learning) mandiri dalam sebuah sistem yang menggunakan data mining, pengenalan pola dan pengolahan bahasa pemrograman untuk meniru cara kerja otak manusia dalam sebuah sistem. Tujuan dari komputasi kognitif adalah untuk menciptakan sistem IT yang secara otomatis mampu memecahkan masalah tanpa memerlukan bantuan manusia.

Integrating Knowledge Management and Business Intelligence Processes for Empowering Government Business Organizations

Emergence of information technologies has transformed the way business marketing is done and how business enterprises are managing the resources and information. Trend of globalization has induced the fierce competitiveness among business enterprises within domestic and international markets. The major quest for the technologies is not limited to strategic value of an organization but also empower the organization work context by utilizing its resources. Knowledge management has emerged as the latest techno-management trend for improving the work process and creating value for business organization operations. Knowledge management offers various techno-managerial implications to business organization for strategic development. However, there are scarce evidences on business intelligence, strategic management decision support related to business organization adopting these offerings. Major objective of Business Intelligence is to extract the information and find the hidden knowledge from all sources of data. Business Intelligence offers to make decision for enhancement of any organizations goal.  The broad overview of research articulates an understanding of government based organizations about the adoption of Knowledge management based Business Intelligence solutions and its challenges. Data mining is playing a key role in Knowledge Management based systems for business organizations and its implication lies in the implementation of data mining algorithm for exploring the huge amount of data, which determines the pure knowledge.
Majority of the government organizational data remains in either unstructured form such as raw form of data (i.e. internal or external document) or with its employees in the form of experience. Knowledge management process deals with extraction of both tacit and explicit knowledge of organization for improving the performance of organization. However Business Intelligence (BI) on the other hand gained its importance with constant enhancement in technologies and tools for extracting the hidden knowledge and patterns. Hence it can be argued that both Business Intelligence and Knowledge Management are complimentary to each other for extracting and managing the knowledge. Thus it’s very imperative for government organizations to have an integration of both Knowledge Management (KM) and Business Intelligence (BI) processes for enhancing the performance of the organization with respect to make organization decision for competitive environment and utilizing the organizational tacit knowledge.
The paper focuses on how BI and KM integration affect the government business organization while discussing its implementation challenges. The paper tries to analyze the correlation between Knowledge Management and Business Intelligence and exploring a road map for data mining based framework for Knowledge Management focusing government based organizations. Current situation of knowledge management strategic decision making and role of knowledge must need to be addressed before proposing any framework for government organization. Paper provides a detailed extensive literature review which aims to describe the basics of Knowledge Management based systems and integrating Business Intelligence with Knowledge Management. Study will draw a distinction between individual and organizational knowledge as well as whether knowledge is playing a key role in strategic development or not?

INTRODUCTION

In the era of knowledge and technical innovation, it has been widely accepted that intangible assets of any business organization will be key to its success. Knowledge is supposed to be most important asset of any business organization, which has the largest influence on competitiveness, strategic development, and growth. Every organization has individual and organizational knowledge either in the form of raw data or information. Raw data or information retains within organization in the form of implicit knowledge and with limited resources. These information or raw data needs to be processed to acquire knowledge through the use of knowledge management & data mining approach.  Further knowledge can be made accessible to all through knowledge management process. Several environmental factors around which each business operates are: globalization, fierce competition, changes in organization structure, growth of information technology, and advent of knowledge management process. Thus, emergence of Knowledge Management discipline has changed the direction of business strategic planning. In the context of business organization, knowledge management is used to acquire the knowledge and experiences it for strategic development. Reuse of preciously acquired knowledge can be beneficial for preventing past failure and used as a guideline for fixing recurrent issues. It has been claimed that in business enterprise the knowledge not only embedded to document and repositories but also with enterprise routine, process, and practices.
Thus, knowledge is recognizing itself as one of the most important assets of any organization. Knowledge is acquired through the processing of available data of organization using data mining approaches. Data mining is a tool for processing the data to find out the relationship within the data that can be beneficial for the user. Data mining has the potential to use is as a powerful tool for the business intelligence but yet not fully recognized.  With the proliferation of the new technologies data mining has experienced an exponential growth and became an integral part of Knowledge Management system. Data mining algorithms are applied to explore the underlying data of business organization and after processing it determines the effectiveness of knowledge.  
The major focus of this paper is role of Knowledge Management and Business Intelligence Processes for government based organization. Government based organizations means functional government agencies, various departments who perform public services. The paper aims to find how government organization managers adopt both KM and BI processes in public sector. Study aims to find out the interrelationship between Knowledge Management and Business Intelligence, and utilize it for strategic development and decision making. 
In government based organization, there are extensive amount of data which is used within organization for business policy management, organization decision making, and growth & development of organization. Since environment changes in any organization drastically, thus any change in the data also reflects the change in the system. The change can be related to various categories such as:
  1. Change in the quantity of data, it means with the growth in any organization amount of data will be increased substantially.
  2. With the increased amount of data, the correlation between data also changes it means the relationship between application system also changed.
Therefore these organization need to understand the data, process and mine the data to acquire knowledge from the large amount of data and extract intact and practical knowledge from random, vague, incomplete, and huge amount of data. This extracted knowledge can be utilized for decision making business intelligence. Primarily in the Knowledge management process, knowledge discovery process needs to apply data mining algorithms. Varieties of algorithms are available in data mining such as genetic algorithm, decision making, neural network, and fuzzy logic. 

This paper is based on government based organizations, in which Knowledge Management process needs to implement for strategic development and decision making, and organizational development for the social and economical growth of the organization as well as improve its competitiveness in the era of globalization. Research aims to monitor, explore the evolution of business intelligence and Knowledge management implementation as a means to improve the work practice of business organization. The fundamental purpose of the paper is to discuss the need of integrating KM and BI for exploiting structured & unstructured raw data, implicit information of the organization and its challenges. This will helpful for creating an integrated knowledge based decision support system framework for government based organization which integrates both Business Intelligence and Knowledge management.

LITERATURE REVIEW

Most researchers and practitioners agreed on the practical implication of knowledge as one of the important assets of any organization. Knowledge Management and Business Intelligence are the two major areas of researchers concern. Knowledge management is a tool for empowering the knowledge within the organization, and useful for decision making. However, Business Intelligence has affected the business world the most for transforming the raw data into knowledge. This can be used for prediction analysis. A dearth research has been performed to explore Knowledge Management, Business Intelligence and its applicability within various application domains.
Authors have analyzed that Business intelligence is the broad categorization of applications of processing large amount of data for any organization to make prediction analysis. Operations such as OLAP (online analytical processing), data warehousing, data reporting, and business rule modeling are used by Business Intelligence. However, Knowledge Management is the process of knowledge acquiring and creation, knowledge sharing and dissemination and knowledge application. Authors have suggested that both Business Intelligence and Knowledge management are influenced by environment of the organization. The success ration of Knowledge Management is directly proportional to employee attitude. Thus, there is a need of common platform for the organization where both employer and employee can share the knowledge.
Author has proposed a scheme for transforming Knowledge Management into Business Intelligence. Author has also briefed certain parameters for implementing them to organization for a common workflow. However, the new or new solution cannot be added directly for the adoption purpose. Tacit knowledge plays a vital role in all the phases of any newly innovative process and implementation of tacit Knowledge Management and can be helpful for handling new problems.
Author has proposed a memory model for linking individual knowledge to knowledge managements. However, the practical implication of this model is very weak. Outcome of knowledge management process over business intelligence and organizational performance with the help of influential variable. Therefore, it can be concluded that any knowledge management based system is a handy tool for achieving completive advantages. Some of the attempts have been done by the authors to integrate knowledge management for real time Business Intelligence and its benefits. Focused on tacit knowledge and explained it as a vital component for organization. However, management of tacit knowledge is a challenging task. Thus, there is the need of a common framework where tacit knowledge can be categorized into various degrees.
Both knowledge management and business intelligences are different from each other in terms of common foundation. Thus the interrelation between knowledge management and business intelligence needs to be explored. Simply an insight can be concluded that business intelligence is used for transforming data to knowledge, whereas Knowledge Management can be used as a tool for knowledge acquisition, knowledge sharing and to create new knowledge.
The advantages and disadvantages of Knowledge Management, Business Intelligence and further proposed KMBI framework for the integration of Knowledge Management and Business Intelligence. This framework consist of three different layers namely data, presentation, and function integration.
With reference to various contexts, articles, paper reviewed, and application of Knowledge management it is analyzed that data mining is widely used toll for Knowledge Management and Business Intelligence both. Since both Knowledge Management and Business Intelligence are correlated and can be integrated for the better performance of an organization. Both are complimentary of each other, thus both can result in more effectiveness for government based business organization.

MANAGING THE KNOWLEDGE

Knowledge is defined as the mix frame of facts, expectation, skills, and combination of relevant information collected through experience, study, and reasoning, for enhancing the ability of decision making and evaluating the right context. However data, information and knowledge are the key terms which are the set member of knowledge management and may used interchangeably. Several arguments are made by the researchers about these terms, and defined as:
Data can refer to unprocessed, unstructured collections of random facts; Information refers to structured and processed data having some sense to the user, whereas Knowledge refers to the most refined and highly useful data for decision making and problem solving.
Various researchers have proposed several classification methods for classifying the knowledge. The classification of knowledge is helpful to the organizations for processing and managing their various available knowledge resources. Most widely accepted classification of knowledge is: Explicit and Tacit knowledge.
Explicit knowledge contains the knowledge, which has been already processed in the form of visual, text, diagrams, tables, manuals, and specific documents. Acquisition of explicit knowledge is easy, since it is in the form of table, manuals, and document; so as easy to manage too. In case of government based business organization explicit knowledge may contain such as business specification, product specification, contracts, and customer data.
However, tacit knowledge refers to most valuable knowledge as it is in the form of experience, skills, and communications. It remains as understanding of people and expressed in the form of language. Tacit knowledge is very beneficial to find best solutions and managing the organization on the basis of previously known knowledge. The only issue with tacit knowledge is, it cannot be articulated as it remains in the form of experience and skills. Since, tacit knowledge is personal, as it is retained in mind in the form of experience, skill and perceptions, hence very tough to manage, share and articulate it. In case of government based organization tacit knowledge may include work such as process, project dealing, problem solving, and expert opinions.
Some authors have proposed that some part of the tacit knowledge can be acquired and converted into explicit knowledge. Several authors have proposed an hierarchy to have an understanding of data, information and knowledge types as shown in figure 1.

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Web Technology Knowledge Management and its Privacy and Security Challenge

Web 2.0 covers a whole range of technology that can enable social networking and other activities across the developed nations and enable effective communication among the user group. The Web 2.0 is adopted by organizations in developed countries to enable IT functions, managing public relationship and human resource to enable interpersonal communication. The future framework around semantic technology and applying effective SOA will define the secure direction of social networking enabled through Web 3.0. The cloud  computing evolution will help improve the knowledge management and able to handle big data for these social media platforms. The proposed Web framework will enable multiple content sources, integrate various applications, combine social networking content under highly secure environment and will turn Web 2.0 into a participatory web and making sharing of information and data secure under open source environment. It can provide a complete virtual architecture and will create a global delivery platform for the developed countries. The future framework around semantic technology and applying effective SOA will define the secure direction of social networking enabled through Web 3.0. The cloud computing evolution will help improve the knowledge management and able to handle big data for these social media platforms.

INTRODUCTION

The evolution of web-technology has emerged the wide spread usage of Web 2.0 systems across organizations in developed countries. The Web 2.0 has provided exceptional set of transformational benefit and helped rise of social networking platform that lead to challenges with privacy and security.

The report primary objective is to propose future model to include that how web 2.0 can be made more secure for social media platforms and what role researchers can play during the evolution. The future roadmap to be around growth of semantic technology and cloud computing evolution to create a path of next level web development for developed countries.

The research proposal is intend to cover roadmap of research and development around comparing research gap of web technology in social media and also evaluating the future perspective of the service around web 2.0 and 3.0. The gaps will be identified from cross-domain area and also to assemble research facts to seek evolution of web 2.0 and 3.0 and different approach and view adopted by the researchers.

Web 2.0 Adoption

The developed countries swiftly adopted Web 2.0 since 2009 and that enabled productivity for them. The web 2.0 acted like a platform that enabled organizations to add new dimensions to manage their business through online sources.  The recent development of social media platforms like Facebook, Twitter, and Myspace has added dimensions to way users interact.


Privacy and Security Challenges

The major privacy and security challenges posed by use of Web 2.0 technology. The specific security challenges to Web 2.0 were primary due to challenges with adoption and integration. The Web 2.0 was able to mitigate few of the given threats due to adopting of following characteristics:
  1. Moving away from traditional web filtering.
  2. Using new protocol like AJAX, SAML and XML for detecting problems.
  3. Using rich internet application and RSS feed.
  4. Higher bandwidth to avoid any such threats.
  5. More user generated content.

Definition of Web 2.0 and Social Media


  1. The Web 2.0 was defined and its relevance from social media perspective was defined by the researcher while its various technologies were also covered as part of research. The adoption of Web 2.0 by the organizations was primary done so as to manage its various functions.
  2. The Web 2.0 has been defined from global perspective and its acceptance level in various developed countries. The social media sites like Facebook, LinkedIn, Ning are used or personalized learnings and its features to enable learning across countries.
  3. The Web 2.0 has incorporated various new means of enabling communication like tagging, mashups, Wikis and all enabled through social media sites. The Web 2.0 has now evolved as read-write web and enabled range of activity including communication, data sharing, video sharing, live chats and enabling collective intelligence.
  4. Web 2.0 has enabled virtual communities through social media platforms. These virtual communities now act as target customer for various e-commerce business sites.
  5. The survey conducted by Prospero Technologies in 2007 reveal that more than sixty percent respondents feel that Web 2.0 has enabled social media performance and further created a market place for developed countries to target e-customers.

Scope Covered From Study Stand Point

  1. There were identified gaps in the scope of the research while the analysis was purely based on survey done by Gatner and primarily evolved only around adoption of social media network and benefits to different type of network.
  2. The scope and study of Web 2.0 well evaluated through using various literature and other research work done by scholars. The analysis was done around various social media and other platforms to enable user connectivity and collaboration.
  3. The Web 2.0 evolution has also been significant due to technology advancement of devices like smart phones, iPads, iPhone, PDAs and android enables mobile phone devices to use internet while on the go and ease of use for various users.
  4. The Web 2.0 has evolved more towards social interact than just providing information technology. The social networking sites are based on HTML web programing language to enable global connectivity.
  5. There are three important dimensions of Web 2.0 and all directly or indirectly are related with social media platforms to enable conversation and participation through various applications and enabled technologies.

Privacy and Security Issues With Mitigations


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RISK PERCEPTION IN THE CORRELATION BETWEEN THE TENDENCY OF USING INTERNET AND CUSTOMERS’ WILLINGNESS TO USE ONLINE PAYMENT SYSTEM

This study aims to determine the extent of the influence of risk perception on the willingness of customers to use online payment system and online purchases, as well as to measure whether the experience and the tendency to use the internet have a significant effect in increasing the willingness of customers to use the online banking facilities and online payments.
There are several things that affect the willingness of customers to use the online payment system services; those are, the tendency factor of using the internet and experience factor of using the internet. Those factors have a positive influence on the willingness of customers to use online payment system facility.
Perception of risk in using the online payment system service facilities has a negative influence on the willingness of customers to use online payment system facility because the use of the internet has several types of risks that should be acceptable to customers.
The tendencies to use the internet and the internet experience factors have a negative influence on the perception of risk in the willingness of customers to use online payment system facilities. Does the perception of risk have a mediation role in the relationship between the tendency to use and experience to use the internet to customers’ willingness to use online payment system? This is a kind of interesting question to study.
The willingness of using online payment system service is a big opportunity for the E-Business development, especially in Yogyakarta, Indonesia.

INTRODUCTION

In a very tight competition among banks, customer decision factors become a serious concern. Each bank has various disclosure diverse to give everything as expected, like "A customer is a boss", "Customer decision is our goal", and so on. Developmental in banking service system today is mostly done with information technology as the main device. The use of information technologies provide a wide range of convenience, facilities, and services for customers. The banks should be able to adapt quickly to implement the system of banking operations with the help of information technology in providing services for their customers. In addition, the service by using information technology must be supported by the ability of the company to create programs or tools that are easy to use by clients (user friendly).
One of the banking services using information technology facilities is the online banking in conducting online payments, or online payment system. Online payment system is a new breakthrough in the world of banking company provided banking services to serve customers' financial transactions online, either a transaction using credit cards, debit cards, and online money transfer through online payment system.
The ability of banking services in providing a good online payment system service will reduce the risk and provide a positive contribution to improve the use of online payment system in every financial transaction made by their customers. The use of online payment system services is believed to provide more benefits and convenience for the customer and for the company the use of a conventional financial transaction system.

Problems

  1. Does the trend of using the internet and the experience of using the Internet significantly affect the willingness of customers to use online payment system?
  2. Does the tendency of using the internet and the experience of using the Internet become a significant influence on the perception of the risk of the use of online payment system facility?
  3. Is there a relationship between the tendency to use the Internet and the experience of using the internet with the willingness of customers to use online payment system?
  4. Does the risk perception of the online payment system services have a significant effect on the customers' willingness to use online payment system?

Limitations

Limitations of the problems in this study are:
  1. The respondents in this research are the bank customers who use the services of Online payment system service in Yogyakarta, Indonesia.
  2. The variables used in this study consist of: The tendency to use the internet, The experience of using the internet, Perception of risk, and Willingness to use the online payment system facility.

Research Objectives

The purposes of this study are:
  1. To determine the influence of the tendency of using the internet and the experience of using the internet to the willingness of customers to use online payment system. 
  2. To determine the effect of the risks perception in using online payment system to the customers' willingness to use online payment system. 
  3. To determine the mediating effect of perceived risk of using online payment system in the correlation between the tendency to use the internet with the customers' willingness to use online payment system.

LITERATURE REVIEW

Information Technology

Information technology has an important role in most of the business process reengineering. Speed, information processing capabilities, and connectivity of computer and internet technology can improve and increase the business process efficiently. Information technology is not limited to computer technology (hardware and software set of tools) that is used to process and store information, but that includes information technology to transmit information.

Theory of Reason Action (TRA)

Theory of Reason Action was introduced by Fishbein and Ajzen (1975), which helps researchers to understand and predict the attitudes and behavior of individuals. TRA has successfully predicted and explained the behavior of different areas of study. The TRA theory is most often used as a theoretical model of the information system. Individual's performance of a particular behavior is determined by the purpose for running behaviors, and the destination is determined by attitude and subjective norm. Several specific factors in determining the behavior of technology acceptance are: behavioral intention to define the behavior, if behavioral intention combined with the attitude and subjective norm.