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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Tutut Herawan
University of Malaya
Computer Science & IT

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

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Enterprise Architecture Characteristics in Context Enterprise Governance Base On COBIT 5 Framework Heru Nugroho; Tutut Herawan
Indonesian Journal of Electrical Engineering and Computer Science Vol 3, No 1: July 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v3.i1.pp240-248

Abstract

The existence of the enterprise architecture is an attempt of managing and planning over the evolution of information systems in the sphere of an enterprise with model-based. In developing the enterprise architecture, there are several tools definition of components in the system. This tool is known as enterprises architecture (EA) framework. In this paper, we present a method to build a model of enterprise architecture in accordance with the needs of the Organization by Understanding the characteristics of the EA framework such as Zachman, TOGAF, and FEAF. They are selected as the EA framework will be used to determine the characteristics of an EA because the framework is most widely used in corporate or Government. In COBIT 5 framework, there is a process associated with enterprise architecture it is APO03 Manage Enterprise Architecture. At this stage of the research, we describe the link between the characteristics of the EA with one process in COBIT 5 framework. The results contribute to give a recommendation how to design EA for organization based on the characteristic of EA in Context Enterprise Governance using COBIT 5 Framework.
Expert System of Quail Disease Diagnosis using Forward Chaining Method B. Herawan Hayadi; Kasman Rukun; Rizky Ema Wulansari; Tutut Herawan; Dahliyusmanto Dahliyusmanto; David Setaiwan; Safril Safril
Indonesian Journal of Electrical Engineering and Computer Science Vol 5, No 1: January 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v5.i1.pp206-213

Abstract

Expert system applications were in great demand in various circles since 1950, with a coverage area that was large. Expert System on the organization was aimed at adding value, increasing productivity as well as the area of managerial can make decisions quickly and accurately. Neither with organizations that did business quail, which was very promising, but needed to be alert for the presence of disease in quail healthy, as in the case in birds quail were highly vulnerable to various kinds of diseases caused by viruses or bacteria. the benefits of the expert system that was able to diagnose quickly and accurately to the symptoms of the disease caused was expected to helped the farmers in of anticipation the many losses caused by disease. Required accuracy and the accuracy of the counting in diagnosing the symptoms of the disease in order to summarized the results by using forward chaining method.
Mining Association Rules: A Case Study on Benchmark Dense Data Mustafa Bin Man; Wan Aezwani Wan Abu Bakar; Zailani Abdullah; Masita@Masila Abd Jalil; Tutut Herawan
Indonesian Journal of Electrical Engineering and Computer Science Vol 3, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v3.i3.pp546-553

Abstract

Data mining is the process of discovering knowledge and previously unknown pattern from large amount of data. The association rule mining (ARM) has been in trend where a new pattern analysis can be discovered to project for an important prediction about any issues. Since the first introduction of frequent itemset mining, it has received a major attention among researchers and various efficient and sophisticated algorithms have been proposed to do frequent itemset mining. Among the best-known algorithms are Apriori and FP-Growth. In this paper, we explore these algorithms and comparing their results in generating association rules based on benchmark dense datasets. The datasets are taken from frequent itemset mining data repository. The two algorithms are implemented in Rapid Miner 5.3.007 and the performance results are shown as comparison. FP-Growth is found to be better algorithm when encountering the support-confidence framework.
Co-Authors B. Herawan Hayadi Dahliyusmanto, Dahliyusmanto David Setaiwan Heru Nugroho Iwan Tri Riyadi Yanto Kasman Rukun Masita@Masila Abd Jalil Mustafa Bin Man Rd. Rohmat Saedudin Rizky Ema Wulansari Safril Safril Sutoyo, Edi Wan Aezwani Wan Abu Bakar Zailani Abdullah