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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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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.
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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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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