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All Journal Journal of Computer Networks, Architecture and High Performance Computing Brilliance: Research of Artificial Intelligence
Manurung, Ericky Benna Perolihin
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Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics Other

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Effectiveness of Using ArchiCAD in Interactive 3D Visualization in Building Drawing Engineering Learning Media Syahputra, Dinur; Sandy, Cut Lika Mestika; Sukiman, T. Sukma Achriadi; Manurung, Ericky Benna Perolihin; Rizal, Reyhan Achmad
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6342

Abstract

This study aims to analyze the effectiveness of using ArchiCAD software as a tool for interactive 3D visualization in the context of learning media for building drawing engineering. Traditional methods of teaching technical drawing often rely on two-dimensional representations, which can limit students’ spatial understanding and comprehension of complex architectural forms. By integrating ArchiCAD, a Building Information Modeling (BIM)-based software, students are exposed to a more immersive and realistic learning experience, enabling them to visualize construction elements more clearly. The research employs a quasi-experimental method involving two groups: an experimental group using ArchiCAD-based interactive media and a control group using conventional methods. Data collection was conducted through pre-tests and post-tests, as well as student perception questionnaires. The results indicate a significant improvement in the learning outcomes of the experimental group, both in terms of cognitive understanding and design skills. Furthermore, student responses show a high level of satisfaction and engagement when using 3D interactive media. These findings suggest that ArchiCAD can be effectively implemented as a digital learning medium in vocational and technical education settings, especially in the field of architectural drawing. The study recommends broader integration of BIM-based tools to support competency-based learning and enhance the quality of engineering education.
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.
Co-Authors Ginting, Victor Saputra Hizria, Rahmatika Rizal, Reyhan Achmad Sandy, Cut Lika Mestika Sukiman, T. Sukma Achriadi Syahputra, Dinur