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This Author published in this journals
All Journal Jurnal Minfo Polgan (JMP) Journal of Technology and Computer (JOTECHCOM)
Syafitri Ramadhani
Universitas Battuta
Computer Science & IT Library & Information Science Mathematics Social Sciences

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Decision Tree-Based Student Graduation Prediction System at the Faculty of Technology, University of Battuta Syafitri Ramadhani; Fahmi Ruziq; M. Rhifky Wayahdi
Journal of Technology and Computer Vol. 3 No. 2 (2026): May 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

On-time graduation is an important indicator of academic quality in higher education. However, the Faculty of Technology at Battuta University still faces the challenge of low on-time graduation rates among students. This study designed a graduation prediction system based on the C4.5 Decision Tree Algorithm by utilizing the academic data of students from the 2022–2024 batch, including GPA, IPS, failed courses, academic leave, status, and attendance. The method used is a quantitative approach with data mining classification techniques, and the system is implemented web-based using PHP and MySQL. The results show that the C4.5 algorithm is capable of accurately predicting student graduation potential and producing classification rules that are easy to understand. This system can help academic advisors and study programs detect students at risk of graduating late at an early stage so that appropriate follow-up actions can be taken.
K-Nearest Neighbor and Random Forest Algorithms in Loan Approval Prediction Syafitri Ramadhani; M. Rhifky Wayahdi
Jurnal Minfo Polgan Vol. 13 No. 1 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i1.14345

Abstract

Loan approval prediction is an important task in the financial sector, which helps banking institutions and lenders make informed decisions regarding loan applications. This research compares the performance of two machine learning algorithms, namely K-Nearest Neighbor (KNN) and Random Forest (RF), in the context of loan approval prediction. The research methodology includes data collection, pre-processing, modeling, and evaluation. The analysis results showed that the Random Forest model performed better overall than KNN, with more true positives and true negatives, and fewer false positives and false negatives. In addition, Random Forest recorded higher accuracy, precision, recall, and F1-score values. These findings provide valuable insights for financial institutions in improving credit risk management strategies and decision-making regarding loan applications.
Co-Authors M. Rhifky Wayahdi ruziq, fahmi