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All Journal Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian Masyarakat Multi Disiplin Ilmu
Ginting, Victor Saputra
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Computer Science & IT Education Environmental Science

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Evaluation of Machine Learning Algorithms for an Early Warning System of Student Graduation in a Python Programming Course Hizria, Rahmatika; Manurung, Ericky Benna Perolihin; Ginting, Victor Saputra
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7718

Abstract

The high failure rate in Python programming courses has become a serious issue for educational institutions. This study aims to evaluate the performance of four machine learning algorithms as the basis of an Early Warning System for predicting student graduation, namely Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbors (KNN). The dataset consists of 3,000 records with 15 features, including demographic data, programming experience, and students’ learning activities. Performance evaluation was conducted using accuracy, precision, recall, F1-score, and ROC-AUC metrics after optimal hyperparameter tuning through GridSearchCV with 5-fold cross-validation. The evaluation results indicate that Random Forest achieved the best performance with an accuracy of 89.33%, precision of 87.50%, recall of 46.23%, F1-score of 60.49%, and ROC-AUC of 94.40%, outperforming SVM (accuracy 86.33%, F1-score 55.43%), Logistic Regression (accuracy 86.50%, F1-score 53.71%), and KNN (accuracy 84.83%, F1-score 44.17%). Feature importance analysis identified experience_encoded, hours_spent_learning_per_week, and projects_completed as the three strongest predictors of student graduation. These findings provide empirical evidence that Random Forest is the most effective algorithm for implementing an Early Warning System in Python programming courses, enabling instructors to identify at-risk students early and provide timely interventions to improve learning success rates.
Optimalisasi Media Promosi Sekolah Melalui Pelatihan Desain Grafis Dan Video Editing Di SMP Swasta Rusyda Medan Hizria, Rahmatika; Ichsan, Aulia; Manurung, Ericky Benna Perolihin; Takhir, Said Hambali; Ginting, Victor Saputra
Jurnal Pengabdian Masyarakat Disiplin Ilmu Vol. 4 No. 1 (2026): Jurnal Pengabdian Masyarakat Multi Disiplin Ilmu
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jpmasdi.v4i1.7926

Abstract

Latar belakang: Perkembangan teknologi digital telah mengubah cara sekolah dalam mempromosikan institusinya. Media promosi yang menarik dan profesional menjadi kebutuhan esensial untuk meningkatkan daya tarik sekolah di mata calon siswa dan orang tua. SMP Swasta RUSYDA Medan membutuhkan peningkatan kapasitas dalam mengoptimalkan media promosi sekolah melalui desain grafis dan video editing yang modern dan efektif. Metode pengabdian: Kegiatan pengabdian dilaksanakan melalui workshop intensif dengan pendekatan praktis menggunakan platform Canva untuk desain grafis dan CapCut untuk video editing. Metode pelatihan mencakup ceramah, demonstrasi langsung, praktik mandiri, dan pendampingan. Peserta terdiri dari 25 orang guru dan staf SMP Swasta RUSYDA Medan. Hasil pengabdian: Kegiatan ini berhasil meningkatkan rata-rata kompetensi peserta dari 8% menjadi 83%. Peserta berhasil memproduksi total 90 konten promosi, yang terdiri dari 60 konten desain grafis dan 30 konten video. Dampak dari kegiatan ini terlihat pada peningkatan engagement rate media sosial sekolah sebesar 45% dan kenaikan inquiry calon siswa baru sebesar 28% dalam satu bulan setelah pelatihan.
Co-Authors Hizria, Rahmatika Ichsan, Aulia Manurung, Ericky Benna Perolihin Takhir, Said Hambali