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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi International Journal of Engineering and Emerging Technology
Made Sudarma
Department of Electrical Engineering, Faculty of Engineering, Udayana University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Mechanical Engineering

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Audit IT Division In Maintenance Process Internal System PT JAMKRIDA BALI MANDARA With Framework Software Maintenance Maturity Model (SMMM) Made Dinda Pradnya Pramita; I Made Dhanan Pradipta; Made Sudarma
International Journal of Engineering and Emerging Technology Vol 2 No 2 (2017): July - December
Publisher : Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University

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Abstract

PT Jamkrida Bali Mandara is a credit guarantee institution owned by Bali Province which operates PT Jamkrida Bali Mandara operational company utilizing Management Information System and in developing and maintenace this system company is assisted by some IT staff and vendor. The process of maintenance of existing systems required an assessment of the performance of IT division. This assessment is done through audit method using Maintenance Maturity Model (SMmm) software framework. Based on the results of the audi obtained the highest level of capability is 3.7 in the domain domain maintenance training and the lowest level capability value is 2.03 in domain domain maintenance process / service definition and maintenace process performance Keywords: Software Maintenance Maturity Model, Maintenace System and Capability Level
Comparison of Gain Ratio and Chi-Square Feature Selection Methods in Improving SVM Performance on IDS Ricky Aurelius Nurtanto Diaz; I Ketut Gede Darma Putra; Made Sudarma; I Made Sukarsa; Naser Jawas
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 15 No 1 (2024): Vol. 15, No. 1 April 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i01.p06

Abstract

An intrusion detection system (IDS) is a security technology designed to identify and monitor suspicious activity in a computer network or system and detect potential attacks or security breaches. The importance of accuracy in IDS must be addressed, given that the response to any alert or activity generated by the system must be precise and measurable. However, achieving high accuracy in IDS requires a process that takes work. The complex network environment and the diversity of attacks led to significant challenges in developing IDS. The application of algorithms and optimization techniques needs to be considered to improve the accuracy of IDS. Support vector machine (SVM) is one data mining method with a high accuracy level in classifying network data packet patterns. A feature selection stage is needed for an optimal classification process, which can also be applied to SVM. Feature selection is an essential step in the data preprocessing phase; optimization of data input can improve the performance of the SVM algorithm, so this study compares the performance between feature selection algorithms, namely Information Gain Ratio and Chi-Square, and then classifies IDS data using the SVM algorithm. This outcome implies the importance of selecting the right features to develop an effective IDS.
Co-Authors I Ketut Gede Darma Putra I Made Dhanan Pradipta I Made Sukarsa Made Dinda Pradnya Pramita Naser Jawas Ricky Aurelius Nurtanto Diaz