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All Journal KOMPUTIKA - Jurnal Sistem Komputer Jurnal Sisfotek Global
Bobi Kurniawan Soegoto
Universitas Komputer Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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Hyperparameter Optimization of Random Forest for Multiclass Classification of Student Academic Performance Using Multidimensional Factors Sri Nurhayati; Diana Effendi; Bobi Kurniawan Soegoto; Adam Mukharil Bachtiar; Hanhan Maulana; Ednawati Rainarli
Komputika : Jurnal Sistem Komputer Vol. 15 No. 1 (2026): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v15i1.18885

Abstract

Classification for academic performances among students in a multi-class scenario is a challenging task due to its dependencies on multiple factors and characteristics, particularly in the medium academic performance category. This scenario makes it a problem for some models with their conventional settings in terms of their ability to optimally distinguish categories of academic performances while being used in classification tasks, thus leading to the need for optimization techniques in enhancing their performances. This research paper will design an optimization strategy for improving the performances of the Random Forest algorithm in a multi-class academic performance classification among students. This will help in enhancing decision-making systems in education. The research method used is a machine learning approach with a Random Forest algorithm optimized through hyperparameter tuning using RandomizedSearchCV. This study utilizes secondary student data obtained from the Kaggle public repository, consisting of 6,607 data points with 20 determining factors covering academic, behavioral, social, environmental, and health aspects. The results showed that Random Forest hyperparameter optimization was able to improve model performance from a baseline accuracy of 79.56% to 81.08% on the validation data, and achieved an accuracy of 81.69% on the test data. In addition, there was an improvement in performance in the Medium category classification, as indicated by an increase in the F1-score value from 0.69 to 0.72. Therefore, the optimization of Random Forest proved to be good in enhancing the performance and stability of multiclass classification of student academic performance.
Smart Notification System with the Integration of Robotic Process Automation and Reinforcement Learning Andri Heryandi; Sufa Atin; Hani Irmayanti; Adam Mukharil Bachtiar; Hanhan Maulana; Bobi Kurniawan Soegoto; Ednawati Rainarli
Komputika : Jurnal Sistem Komputer Vol. 15 No. 1 (2026): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v15i1.18951

Abstract

This study proposes the development of an intelligent academic notification system by integrating Robotic Process Automation (RPA) and Reinforcement Learning (RL) to improve the effectiveness of delivering information to students and parents. RPA is utilized to automate the process of sending notifications across various channels, such as email and WhatsApp, ensuring fast, consistent, and hands-free message distribution. RL is implemented to determine the optimal communication channel based on delivery history, message status (sent, failed, read), and the cost associated with each channel. Each student is represented as a state, while the selection of a communication channel becomes an action evaluated using Q-learning. The system learns from recipient behavior and updates the Q-table to enhance the accuracy of channel selection for future notifications. Additionally, the system applies an automatic escalation mechanism to parents as the deadline approaches. The result of this research is a smart notification system that can be implemented within academic information systems to enhance operational efficiency and student engagement.
Probity Audit of Procurement Information System: A Case Study of SIMPRODAS Implementation in Bandung Regency Dodi Syaripudin; Lusianto Lusianto; Rizki Adha; Sri Supatmi; Bobi Kurniawan Soegoto; Zainal Arifin Hasibuan
JURNAL SISFOTEK GLOBAL Vol 16, No 2 (2026): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v16i2.16252

Abstract

Probity audit is an independent assessment activity aimed at ensuring integrity, correctness, and honesty in the public sector procurement process of goods and services. This study analyzes the application of probity audit in the procurement information system (SIMPRODAS – Probity Oversight Information System BEDAS) in Bandung Regency as a form of digital transformation in enhancing accountability and transparency of regional financial management. Using a qualitative research approach through a case study method, this study involves the collection of primary data through in-depth interviews with 15 informants from the Inspectorate, financial management units, and regional work units, as well as secondary data from policy documents and audit reports. The results indicate that the implementation of SIMPRODAS has improved real-time monitoring in the procurement process, but still faces challenges related to human resource competencies, cross-organizational system integration, and clarity of audit normalization guidelines. The study recommends capacity building for audit human resources, refinement of information-technology-based probity audit technical guidelines, and strengthening coordination between the Government Internal Supervisory Apparatus (APIP) and management. These findings provide practical contributions for local governments in optimizing probity audits through the utilization of modern information systems.
Co-Authors Adam Mukharil Bachtiar Andri Heryandi Diana Effendi Dodi Syaripudin Ednawati Rainarli Hani Irmayanti Lusianto Lusianto Maulana, Hanhan Rizki Adha Sri Nurhayati Sri Supatmi Sufa Atin Zainal Arifin Hasibuan