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All Journal CommIT (Communication & Information Technology) Indonesian Journal of Electrical Engineering and Computer Science
Djoni Haryadi Setiabudi
Petra Christian University
Computer Science & IT

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Effect of Students’ Activities on Academic Performance Using Clustering Evolution Analysis Djoni Haryadi Setiabudi; Michael Santoso
CommIT (Communication and Information Technology) Journal Vol. 17 No. 2 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i2.9053

Abstract

Educational data mining is a technique to evaluate educational process of university students, especially in their early stages. Most preliminary studies focus on observing courses undertaken by students from one semester to the next to predict their success rate. However, besides studying, many students are also involved in non-academic activities, which tends to affect their grades. Therefore, the research aims to determine the effect of student activities on grades while taking into account their academic activities. The method used for clustering is K-Means. Data are collected by observing students’ activity patterns in lectures. The research is conducted in two study programs at Petra Christian University: Business Management and Architecture. The results show that the K-Means method gives good results. The clusters formed from the data show non-homogenous groups and produce insights from several groups. The results show a tendency for students’ performance to increase along with the number of activities and points earned. Most students have increased activities during busy times in the third, fourth, fifth, and sixth semesters. The peak is between the fifth and sixth semesters. Then, it starts to decrease in the seventh and eighth semesters. Therefore, students’ activities in the Business Management study program affect performance significantly. Meanwhile, in the Architecture study program, it has an insignificant effect on performance.
Evaluation of hybrid parallelism for scalable training of DenseNet-121 in diabetic retinopathy classification Indar Sugiarto; Djoni Haryadi Setiabudi; Darrell Cornelius Rivaldo; Taweesak Kijkanjanarat; Resmana Lim
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp662-671

Abstract

Training large and complex deep learning models is often constrained by GPU memory limitations and prolonged training times. While several parallelism strategies have been proposed, this study specifically evaluates hybrid parallelism—a combination of data parallelism and pipeline parallelism—to address both challenges simultaneously. Using a case study on diabetic retinopathy (DR) classification with the DenseNet-121 architecture, we analyze the trade-off between computational efficiency and memory scalability. Results show that although hybrid parallelism does not yet provide speedup compared to a single-GPU setup—due to communication overhead and pipeline fragmentation—it enables training of large models that exceed the memory capacity of a single GPU. The trained model achieved a validation accuracy of 0.737, a quadratic weighted kappa (QWK) of 0.861, and a weighted F1-score of 0.749. In contrast, pure data parallelism showed a potential speedup of up to 1.9× in scenarios where the model still fits within a single GPU. These findings highlight the critical role of hybrid parallelism in overcoming the memory wall in large-scale model training, though optimization to reduce overhead remains a key challenge.
Co-Authors Darrell Cornelius Rivaldo Indar Sugiarto Michael Santoso Resmana Lim Taweesak Kijkanjanarat