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Journal : Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control

Risk Management using COBIT 5 for Risk : A Case Study on Local Government in Indonesia Prasetyo, Beny; Toha, Lailatul Qomariah; Yulia Retnani, Windi Eka
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 1, February 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i1.1585

Abstract

BP4D (Regional Development Planning, Research and Development Agency) Bondowoso utilizes information technology to support its duties and functions, one of which is SIPD (Sistem Informasi Pemerintah Daerah). SIPD provides many benefits and conveniences such as improving the quality of public services, transparency, improving bureaucratic accountability, but in its implementation SIPD can also pose dangerous risks both from processes involving the system and the system itself. These risks can disrupt BP4D Bondowoso's business processes and cause various losses. To protect BP4D Bondowoso from losses caused by risk, risk management is carried out using the relevant framework, namely COBIT 5 Enabling Process and COBIT 5 for Risk with the APO12 risk management process. Data were collected by interview and distributing questionnaires. Fifty-one risks were identified in the implementation of SIPD at BP4D Bondowoso consisting of 48 negative risks and 3 positive risks. The risks found dominate the type of IT Benefit / Value Enablement and the category of regulatory compliance. Identified 3 very high risks in the category of regulatory compliance and software. Overall risk dominates the medium rating, which is 28 risks and the high risk consists of 20 risks. The negative risk response is dominated by mitigate, which is 33 risks.
Improving Software Defect Prediction Using a Combination of Ant Colony Optimization-based Feature Selection and Ensemble Technique Retnani, Windi Eka Yulia; Furqon, Muhammad 'Ariful; Setiawan, Juni
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 4, November 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i4.2038

Abstract

Software defect prediction plays a vital role in enhancing software quality and minimizing maintenance costs. This study aims to improve software defect prediction by employing a combination of Ant Colony Optimization (ACO) for feature selection and ensemble techniques, particularly Gradient Boosting. This research utilized three NASA MDP datasets: MC1, KC1, and PC2, to evaluate the performance of four machine learning algorithms: Random Forest, Support Vector Machine (SVM), Decision Tree, and Naïve Bayes. The data preprocessing comprised handling class imbalance using SMOTE and converting categorical data into numerical representations. The results indicate that the integration of ACO and Gradient Boosting significantly enhances the accuracy of all four algorithms. Notably, the Random Forest algorithm achieved the highest accuracy of 99% on the MC1 dataset. The findings suggest that combining ACO-based feature selection with ensemble techniques can effectively boost the performance of software defect prediction models, offering a robust approach for early detection of potential software defects and contributing to improved software reliability and efficiency.