Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
First Blog Post
Published:
Hello, friends! đź‘‹
portfolio
Portfolio item number 1
Published:
Short description of portfolio item number 1
Portfolio item number 2
Published:
Short description of portfolio item number 2
publications
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.
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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talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.