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All Journal KOMPUTIKA - Jurnal Sistem Komputer
Bobi Kurniawan Soegoto
Universitas Komputer Indonesia
Computer Science & IT Control & Systems Engineering 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.
Co-Authors Adam Mukharil Bachtiar Andri Heryandi Diana Effendi Ednawati Rainarli Hani Irmayanti Maulana, Hanhan Sri Nurhayati Sufa Atin