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All Journal Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian Masyarakat Multi Disiplin Ilmu
Rahmatika Hizria
Universitas Deli Sumatera
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 Rahmatika Hizria; Ericky Benna Perolihin Manurung; Victor Saputra Ginting
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Articles Research 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.
Analysis Of The Determinants Of Financial Resilience Among Retiree Customers Using An Explainable Ai (Xai) Approach Victor Saputra Ginting; Rahmatika Hizria; Said Hambali Takhir
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.9459

Abstract

Financial resilience among high-risk retiree customer segments is a crucial issue in credit risk management, particularly because traditional scoring models are often black-box in nature and fail to provide transparent insight into the economic and behavioral factors that influence a borrower's repayment capacity. This research pursues a dual objective: first, to develop a high-performing predictive model for financial resilience classification using ensemble Machine Learning methods, and second, to apply SHAP-based Explainable AI (XAI) techniques to identify and quantify the key determinants of resilience in an accountable manner. The research methodology involved processing data from the Kaggle platform, including stratified sampling and SMOTE oversampling to address class imbalance, along with a performance comparison among Logistic Regression, Random Forest, and XGBoost. The results show that Logistic Regression proved to be the best-performing model on the held-out test set, achieving an AUC of 0.9479 and an F1-Score of 0.7313 for the resilient class. Furthermore, SHAP analysis revealed that the strongest determinants driving financial resilience were the number of prior delinquencies, loan purpose (particularly business-purpose loans), and a low debt-to-income ratio, with credit score also contributing. In practical terms, these findings provide a transparent and humane credit assessment framework, enabling financial institutions to formulate more inclusive yet prudent lending policies by prioritizing customers' actual repayment capacity over age alone.
Optimalisasi Media Promosi Sekolah Melalui Pelatihan Desain Grafis Dan Video Editing Di SMP Swasta Rusyda Medan Rahmatika Hizria; Aulia Ichsan; Ericky Benna Perolihin Manurung; Said Hambali Takhir; Victor Saputra Ginting
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.
Strategi Digital Branding melalui Pengoptimalan Media Sosial untuk Meningkatkan Citra Positif Sekolah Rahmatika Hizria; Aulia Ichsan; Said Hambali Takhir; Victor Saputra Ginting
Jurnal Pengabdian Masyarakat Disiplin Ilmu Vol. 4 No. 2 (2026): Jurnal Pengabdian Masyarakat Multi Disiplin Ilmu
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Citra positif sekolah menjadi salah satu faktor penting dalam menarik minat masyarakat, khususnya orang tua, untuk mempercayakan pendidikan anak pada suatu lembaga. Di era digital, media sosial berperan besar dalam membentuk persepsi publik, namun banyak sekolah, termasuk SD IT Amrullah Akbar, belum memanfaatkan media sosial secara optimal sebagai sarana digital branding. Guru dan tenaga kependidikan pada umumnya masih memiliki pemahaman terbatas mengenai strategi pengelolaan konten dan promosi digital yang efektif, sehingga potensi media sosial sebagai sarana membangun citra sekolah belum tergarap dengan baik. Kegiatan pengabdian dilaksanakan dalam bentuk workshop tatap muka dengan tahapan survei awal, penyusunan materi, pelaksanaan pelatihan, praktik langsung pembuatan konten, serta pendampingan dan evaluasi. Workshop diselenggarakan oleh tim dosen dan mahasiswa Program Studi Sistem Informasi Universitas Deli Sumatera bagi guru dan staf SD IT Amrullah Akbar, dengan metode ceramah, demonstrasi, dan praktik langsung pengelolaan akun media sosial sekolah Peserta menunjukkan peningkatan pemahaman mengenai konsep digital branding, teknik fotografi dan videografi sederhana untuk konten sekolah, penulisan caption yang menarik, serta strategi pengelolaan jadwal unggahan pada platform Instagram dan Facebook. Peserta juga mampu menyusun rencana konten (content plan) sederhana untuk mendukung citra positif sekolah secara berkelanjutan. Workshop digital branding melalui pengoptimalan media sosial terbukti efektif meningkatkan kompetensi guru dalam mengelola citra sekolah secara daring dan dapat dijadikan model kegiatan berkelanjutan bagi sekolah lain
Co-Authors Aulia Ichsan Ericky Benna Perolihin Manurung Said Hambali Takhir Victor Saputra Ginting Victor Saputra Ginting