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All Journal Journal of Information Systems and Informatics Jurnal Sistem Informasi (JASISFO)
Khansa, Ainna
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Computer Science & IT

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PERBANDINGAN FRAMEWORK COBIT 5 DENGAN STANDAR AUDIT SISTEM INFORMASI LAINNYA Khansa, Ainna; Rahman, M Arief
Jurnal Sistem Informasi (JASISFO) Vol. 4 No. 2 (2023): September 2023
Publisher : Politeknik Negeri Sriwijaya

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Abstract

Perkembangan teknologi informasi mengubah bisnis dan organisasi secara signifikan. Sistem informasi yang handal kini menjadi kunci keberhasilan dan kelangsungan operasional di era digital. Oleh karena itu, pengawasan dan manajemen yang efektif semakin krusial. Salah satu standar terkenal dalam hal ini adalah COBIT 5 (Control Objectives for Information and Related Technology) yang dikembangkan oleh Information Systems Audit and Control Association (ISACA). COBIT 5 memberikan panduan komprehensif mengenai pengelolaan dan audit sistem informasi, dengan fokus pada kontrol, manajemen risiko, dan kepatuhan. Standar audit sistem informasi lainnya yang populer meliputi ISO/IEC 27001 (Information Security Management System), NIST SP 800-53 (Security and Privacy Controls for Federal Information Systems and Organizations), dan ITIL (Information Technology Infrastructure Library). Penelitian bertujuan membandingkan COBIT 5 dengan standar-standar ini. Hasilnya akan memberikan wawasan berharga bagi auditor, praktisi sistem informasi, dan manajer dalam memilih dan menerapkan kerangka kerja audit yang sesuai dengan kebutuhan organisasi. Metodologi penelitian melibatkan studi literatur, analisis perbandingan, dan penilaian terhadap penggunaan praktis dan efektivitas masing-masing kerangka kerja dalam konteks organisasi. Penelitian ini bertujuan mengidentifikasi kelebihan dan kekurangan masing-masing kerangka kerja untuk membantu organisasi mengelola dan mengawasi sistem informasi secara optimal.
Predicting Accounts Receivable of the Social Security Administration for Employment Using LSTM Algorithm Khansa, Ainna; Ependi, Usman
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1274

Abstract

This study explores the use of Long Short-Term Memory (LSTM) networks for predicting outstanding contributions from employers to the BPJS Ketenagakerjaan, Indonesia’s social security agency. The research aims to address the challenges BPJS faces due to delayed or unpaid contributions, which impact the institution's operational stability and financial health. The LSTM model, a deep learning technique well-suited for time-series prediction, was applied to historical data from BPJS Ketenagakerjaan to predict overdue contributions across three different training-validation splits: 70:30, 80:20, and 90:10. The results demonstrate that the 80:20 split achieved the highest validation accuracy of 84.71%, offering the optimal balance between training data and model generalization. The model's ability to predict overdue contributions with high accuracy could significantly improve BPJS's receivables management, allowing for more proactive financial planning and risk mitigation. The study also highlights the integration of an attention mechanism within the LSTM model, enhancing its predictive capabilities by focusing on the most relevant historical data. This research contributes to the field of predictive analytics in public sector financial management, showcasing the potential of machine learning in enhancing the efficiency and effectiveness of social security programs.
Co-Authors Rahman, M Arief Usman Ependi