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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Artificial Intelligence and Digital Business Journal of Multidisciplinary Sustainability Asean
Nandra, Nandra
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Digitalisasi Fungsi HRD melalui Sistem Informasi: Studi Efisiensi di Wahana Express Nandra, Nandra; M. Syahputra, M. Syahputra
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.1062

Abstract

Perkembangan teknologi digital mendorong setiap perusahaan untuk terus berinovasi, termasuk dalam pengelolaan sumber daya manusia. Suatu tahapan krusial yang bisa diterapkan ialah digitalisasi fungsi Human Resources Development (HRD) melalui penerapan sistem informasi. Studi ini bertujuan untuk memahami bagaimana penerapan sistem informasi HRD dapat meningkatkan efisiensi pengelolaan SDM di Wahana Express. Metode yang diterapkan ialah studi kasus kualitatif melalui teknik pengambilan data berformat wawancara, observasi, dan telaah dokumen. Temuan studi ini menjabarkan bahwa sistem informasi yang diterapkan mampu mempermudah proses kerja tim HRD, seperti administrasi karyawan, rekrutmen, pelatihan, hingga penilaian kinerja. Proses yang sebelumnya memakan waktu dan rentan kesalahan menjadi lebih cepat, akurat, dan terorganisir. Digitalisasi ini juga mendukung pengambilan keputusan yang lebih relevan sebab didasari oleh data yang real-time dan terintegrasi. Kesimpulannya, pemanfaatan sistem informasi dalam fungsi HRD berperan besar dalam menciptakan efisiensi sekaligus mendorong peran strategis HRD di era digital. Penelitian ini merekomendasikan pentingnya peningkatan kapasitas digital bagi staf serta pengembangan sistem yang berkelanjutan.
The Role of Gamification in Increasing Student Learning Motivation in Online Learning Syahputra, M.; Nandra, Nandra; Aminah, Aminah
Journal of Multidisciplinary Sustainability Asean Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijmsa.v1i6.1777

Abstract

Background. Online education has become an increasingly common learning method, but it often faces challenges in maintaining student motivation. Gamification, i.e. the application of game elements in non-gaming contexts, emerged as a potential strategy to increase engagement and motivation in online learning. Purpose. This study aims to explore the role of gamification in increasing students' learning motivation in online learning and to identify the most effective gamification elements in this context. Method. The method used is a quantitative approach with a survey design. Data was collected through a questionnaire filled out by 200 students from several high schools who participated in online learning with gamification elements. Statistical analysis was conducted to evaluate the relationship between gamification and learning motivation. Results. The results of the study showed that 70% of students had a high or very high level of motivation after applying gamification. Elements such as instant feedback obtained the highest scores in influencing motivation. Case studies show that 85% of students feel an increase in learning motivation in classrooms that implement gamification challenges. Conclusion. This study concludes that gamification can significantly increase students' learning motivation in online learning. The right implementation of gamification elements can create a more engaging and interactive learning experience, which is crucial in today's digital education.
Prediksi Dropout Mahasiswa: Early-Warning Berbasis Enrollment dengan Machine Learning Andika Putra, Febri; Mirajdandi, Syahisro; Nandra, Nandra; Okmarizal, Bisma; Mulyanda, Sandy
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i3.10714

Abstract

Dropout among university students remains a major challenge in higher education because it affects study continuity, institutional performance, and the efficiency of academic service planning. This study develops a machine learning–based Early Warning System (EWS) that leverages data available at enrollment and is updated after the first semester. Using the public dataset “Predict Students’ Dropout and Academic Success” (n = 4,424), the original three-class outcome (Dropout, Enrolled, Graduate) is simplified into a binary target, with dropout treated as the positive class. Two feature scenarios are evaluated: (1) enrollment-only for pre-entry screening and (2) enrollment plus first-semester indicators to update risk scores. Three models are compared: class-balanced Logistic Regression, class-balanced Random Forest, and Gradient Boosting. Model performance is assessed using accuracy, precision/recall/F1score for the dropout class, balanced accuracy, and ROC-AUC. Under the enrollment-only setting, Logistic Regression achieves the best early-warning performance (recall = 0.697; F1 score = 0.651). After incorporating first-semester features, performance improves (recall = 0.792; F1score = 0.779). Beyond model comparison, this study adds an operational perspective through confusion-matrix simulation and probability-threshold analysis to balance missed at-risk cases and follow-up workload.
Co-Authors Aminah Aminah Andika Putra, Febri M. Syahputra Mirajdandi, Syahisro Mulyanda, Sandy Okmarizal, Bisma Syahputra, M.