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All Journal Journal of Information Technology and Computer Science Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Rian Nopiardi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Systematic Literature Review Analisis Perbandingan Implementasi Manajemen Risiko TI pada Sektor Perbankan Rian Nopiardi; Debora Marianthi; Ni Kadek Sri Manik; Assyifa Restu Maharani Putri; Andika Galuh; Reinhard Imanuel
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 6 No. 02 (2024): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v6i02.481

Abstract

This research is a Systematic Literature Review (SLR) research which aims to analyze the implementation of Information Technology (IT) Risk Management in the banking sector. Through the SLR process, researchers collected, evaluated, and synthesized evidence from 20 selected national journal articles obtained from Google Scholar using the Harzing's Publish or Perish application. The subjects of this research are various national journal articles which discuss IT Risk Management in the banking sector, while the objects are the implementation process, evaluation, strategy, analysis and influence of implementing IT Risk Management on financial performance and operational sustainability of banks. The research results provide important insights for professionals and stakeholders in strengthening the security and sustainability of bank operations in the ever-changing digital era. Recognizing the influence of external factors such as regulation and digital transformation, the research highlights the need for adaptation to the dynamics of the banking industry to improve IT Risk Management. The findings have the potential to contribute significantly to efforts to develop best practices in managing information technology risk in the banking sector.
Heart Disease Prediction System Employing Machine Learning Rian Nopiardi; Raka Dimas Saputra; Niken Fitria Apriani; Al Hafiz Akbar Maulana Siagian; Shidiq Al Hakim; Fatyanosa, Tirana Noor
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102926

Abstract

Heart disease places a significant strain on healthcare systems. Moreover, it kills millions of people each year, which is a leading cause of death worldwide. Hypertension, diabetes, unhealthy lifestyles, and genetic predispositions are all risk factors for heart disease. However, it is not easy to identify a heart disease. For this reason, helping in identifying the heart disease is important to prevent a death, e.g., caused by a heart attack. In this study, we aim to develop a heart disease prediction system. The system is developed according to the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework by employing machine learning algorithms. In this work, Logistic Regression (LR) and Random Forest (RF) are utilized as our machine learning algorithms for classifying heart disease using a heart disease dataset from Kaggle. Our results show LR has an AUC value of 0.921 and F1-Score 0.89 that outperforms RF with an AUC value of 0.920 and F1-Score 0.84 in this work. Then, we select LR to be applied to the developed heart disease prediction system.
Co-Authors Al Hafiz Akbar Maulana Siagian Andika Galuh Assyifa Restu Maharani Putri Debora Marianthi Ni Kadek Sri Manik Niken Fitria Apriani Raka Dimas Saputra Reinhard Imanuel Shidiq Al Hakim Tirana Noor Fatyanosa, Tirana Noor