{"id":107459,"date":"2026-02-21T05:27:04","date_gmt":"2026-02-20T22:27:04","guid":{"rendered":"https:\/\/www.its.ac.id\/news\/?p=107459"},"modified":"2026-02-26T06:17:46","modified_gmt":"2026-02-25T23:17:46","slug":"its-doctor-develops-early-warning-system-for-online-learning-risks","status":"publish","type":"post","link":"https:\/\/www.its.ac.id\/news\/en\/its-doctor-develops-early-warning-system-for-online-learning-risks\/","title":{"rendered":"ITS Doctor Develops Early Warning System for Online Learning Risks"},"content":{"rendered":"
\"\"
Dr Bruri Trya Sartana SKom MM MKom during the presentation of his dissertation in the Doctoral Promotion Open Session of the ITS Electrical Engineering Department at Tower 2 ITS<\/figcaption><\/figure>\n

ITS Campus, ITS News \u2014 <\/strong>Online learning trends that focus on increasing final grades often cause failure in student learning evaluations. Observing this issue, a doctoral program graduate from the Electrical Engineering Department of Institut Teknologi Sepuluh Nopember (ITS)<\/a>, Dr. Bruri Trya Sartana SKom MM MKom, developed an early warning system model that utilizes learning behavior data to identify academic risks in online learning.<\/p><\/blockquote>\n

Bruri revealed that online learning systems utilizing a Learning Management System<\/em> (LMS) have the potential to create patterns of learning failure. He used digital footprints, including material access activities, interactions, and assignment completion patterns, to observe the patterns that cause failure. \u201cFinal evaluations that only utilize final grades are less effective in assessing student learning success,\u201d he explained.<\/p>\n

Furthermore, the lecturer from the Faculty of Information Technology at Budi Luhur University added that academic risk is a multidimensional factor. These factors can be seen from student learning engagement patterns, assessment performance stability, and procrastination behavior in completing assignments. \u201cDemographic factors were also measured against the possibility of student learning failure risks,\u201d he added.<\/p>\n

In developing his research, the man from Jakarta used a non-linear machine learning<\/em> approach to model the relationship between learning behavior variables and academic risk. The main dataset used was the Open University Learning Analytics Dataset<\/em> (OULAD), which was then validated using institutional Moodle LMS data. \u201cThis aims to ensure relevance in implementation within the educational scope,\u201d he clarified.<\/p>\n

\"\"
Dr Bruri Trya Sartana SKom MM MKom (standing) during his dissertation presentation in a Closed Session at the ITS Electrical Engineering Department<\/figcaption><\/figure>\n

Additionally, Bruri used the Categorical Boosting<\/em> (CatBoost) research model with a hyperparameter optimization process through the Optuna framework<\/em>. To ensure model reliability, evaluations were conducted using the 5-fold stratified cross validation<\/em> method. \u201cThis approach allows for a balanced data distribution, resulting in more stable evaluation results with minimal bias,\u201d he elaborated.<\/p>\n

Through this combination of approaches, Bruri explained that the CatBoost model is capable of providing the most superior performance compared to other comparative models. This superiority is evident in the Area Under Curve<\/em> (AUC) value, accuracy level, and consistency of results at each testing stage. \u201cThis model is able to detect at-risk students before they enter the final evaluation stage,\u201d he said.<\/p>\n

With this combination, Bruri designed an early warning system based on specific timeframes. He divided the risk detection mechanism into three stages: short-term, medium-term, and long-term. \u201cThis system can detect near-term risks and generate projections of potential graduation delays,\u201d he explained.<\/p>\n

In the short-term stage, Bruri stated that the system will identify students who begin to show a decrease in learning engagement, such as being late in submitting assignments. The medium-term stage will monitor assessment performance stability and academic consistency patterns. \u201cThe long-term stage is designed to project the likelihood of students failing to reach their graduation target on time based on performance and learning behavior detected from the start,\u201d he revealed.<\/p>\n

These findings prove that the integration of LMS data with non-linear machine learning<\/em> approaches can serve as the foundation for an accurate and applicable early warning system. The man with glasses explained that this research supports the Sustainable Development Goals<\/em> (SDGs) commitment on point 4, which is Quality Education. With proper implementation, this research can assist educational institutions in sustainably improving the success of online learning. (ITS PUBLIC RELATIONS)<\/strong><\/p>\n

Reporter: Hani Aqilah Safitri<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

ITS Campus, ITS News \u2014 Online learning trends that focus on increasing final grades often cause failure in student learning evaluations. Observing this issue, a doctoral program graduate from the Electrical Engineering Department of Institut Teknologi Sepuluh Nopember (ITS), Dr. Bruri Trya Sartana SKom MM MKom, developed an early warning system model that utilizes learning […]<\/p>\n","protected":false},"author":774,"featured_media":107293,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[67,3671],"tags":[],"class_list":["post-107459","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-latest-en","category-quality-education-en"],"yoast_head":"\nITS Doctor Develops Early Warning System for Online Learning Risks - ITS News<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.its.ac.id\/news\/en\/its-doctor-develops-early-warning-system-for-online-learning-risks\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ITS Doctor Develops Early Warning System for Online Learning Risks - ITS News\" \/>\n<meta property=\"og:description\" content=\"ITS Campus, ITS News \u2014 Online learning trends that focus on increasing final grades often cause failure in student learning evaluations. 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