Publications

A Machine Learning Approach to Predicting On-Time Graduation in Indonesian Higher Education

Published in Elinvo (Electronics, Informatics, and Vocational Education), 2024

This study uses machine learning techniques to predict on-time graduation in Indonesian higher education. The research employed nine machine learning models, including Random Forest, Logistic Regression, Neural Networks, etc., to identify key predictors of on-time graduation. The result showed that Random Forest outperformed other models by achieving an accuracy of 85% and an AUC of 0.875.

Recommended citation: Pawitra, M. A. S., Hung, H. C., & Jati, H. (2024). A Machine Learning Approach to Predicting On-Time Graduation in Indonesian Higher Education. Elinvo (Electronics, Informatics, and Vocational Education), 9(2), 294-308. https://doi.org/10.21831/elinvo.v9i2.77052
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AGATOR (Automatic Garbage Collector) as Automatic Garbage Collector Robot Model

Published in International Journal of Future Computer and Communication, 2014

This research aims to design and make AGATOR (Automatic Garbage Collector), a rotor robot model as automatic garbage collector to counter accumulation of garbage in the river which has no flow effectively and efficiently.

Recommended citation: Nurlansa, O., Istiqomah, D. A., & Pawitra, M. A. S. (2014). AGATOR (automatic garbage collector) as automatic garbage collector robot model.International Journal of Future Computer and Communication, 3(5), 367-371. https://doi.org/10.7763/IJFCC.2014.V3.329
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Machine Learning Analysis on Students’ Demographic and Performance to Predict On-Time Graduation: A Case Study in Indonesia

Published in TWELF 2025, Tunghai University, Taichung, Taiwan, 2024

Utilizing a dataset from an Indonesian university,we employ supervised and unsupervised learning models. Our analysis assesses model performance, identifies significant predictors, and develops a dynamic warning system. Our analysis anticipates identifying demographic factors contributing to on-time graduation, predicting graduation likelihood using supervised learning, and developing a dynamic warning system for timely interventions.