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All Journal Darma Diksani: Jurnal Pengabdian Ilmu Pendidikan, Sosial, dan Humaniora Jurnal Ilmiah Informatika dan Komputer
Ade Yusupa
Sam Ratulangi University
Computer Science & IT Languange, Linguistic, Communication & Media Social Sciences Other

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Sosialisasi dan Pelatihan Digitalisasi Presensi dan Kinerja Guru SMA Negeri 4 Manado dengan Aplikasi DIGURU: Socialization and Training on the Digitalization of Teacher Attendance and Performance at SMA Negeri 4 Manado Utilizing DIGURU Application Victor Tarigan; Kartyka Nababan; Ade Yusupa
DARMADIKSANI Vol 5 No 3 (2025): Edisi November (Special Edition)
Publisher : Jurusan Pendidikan Bahasa dan Seni, FKIP, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/darmadiksani.v5i3.8576

Abstract

Perkembangan teknologi informasi telah membawa perubahan signifikan dalam pengelolaan administrasi pendidikan, termasuk dalam proses presensi dan pelaporan kinerja guru. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk memperkenalkan dan meningkatkan kemampuan guru dalam memanfaatkan aplikasi DIGURU (Digitialisasi Presesnsi dan Kinerja Guru), yaitu aplikasi Android untuk Presensi guru berbasis geolocation dan swafoto (selfie), serta pelaporan aktivitas mengajar secara digital. Program ini dilaksanakan di SMA Negeri 4 Manado melalui tahapan sosialisasi, pelatihan, dan pendampingan intensif kepada para guru. Dalam kegiatan ini, guru dilatih untuk melakukan presensi dengan validasi lokasi dan foto sebagai bukti kehadiran, serta mengunggah laporan kegiatan mengajar lengkap dengan dokumentasi foto kelas secara real-time. Hasil pelaksanaan menunjukkan peningkatan pemahaman, antusiasme, dan keterampilan guru dalam penggunaan teknologi digital. Aplikasi ini juga membantu pihak sekolah dalam memperoleh data kehadiran dan pelaporan kinerja guru yang lebih cepat, akurat, dan transparan. Implementasi DIGURU diharapkan menjadi langkah awal menuju transformasi digital dalam manajemen sekolah, serta meningkatkan efektivitas, efisiensi, dan akuntabilitas administrasi pendidikan.
Comparative Analysis of Naive Bayes and Support Vector Machine for Sentiment Classification of Indonesian-Language Mobile Application Reviews on Google Play Store Norris Elden Salassa; Arpen Patanduk; Ade Yusupa; Yaulie Deo Y. Rindengan
Jurnal Ilmiah Informatika dan Komputer Vol. 3 No. 1 (2026): Juni 2026 (In Progress)
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/informatech.volume3number1.515

Abstract

This study conducted a comparative performance evaluation of Multinomial Naive Bayes and Support Vector Machine (SVM) with a linear kernel in classifying the sentiment of Indonesian-language mobile application reviews collected from the Google Play Store. A total of 2,847 reviews targeting the GoPay digital wallet application were gathered via web scraping using the google-play-scraper library. After preprocessing, including case folding, cleansing, tokenization, stopword removal, and stemming using the Sastrawi library, the final dataset comprised 2,634 usable reviews. Sentiment labeling was conducted automatically based on star ratings: ratings of 4 and 5 were assigned as positive (1,841 reviews, 69.9%), while ratings of 1 and 2 were assigned as negative (793 reviews, 30.1%). Feature extraction used TF-IDF with a vocabulary size of 8,432 unique terms. Model training used an 80:20 train-test split with stratified sampling. SVM parameters were set to kernel=linear and C=1.0; Naive Bayes used alpha=1.0 (Laplace smoothing). Experimental results show that SVM achieved an accuracy of 88.3%, precision of 0.89, recall of 0.88, and F1-score of 0.88, while Naive Bayes obtained an accuracy of 82.1%, precision of 0.84, recall of 0.82, and F1-score of 0.83. SVM demonstrated superior performance across all four evaluation metrics, with the largest gap observed in the F1-score for the negative class (SVM: 0.71 vs. Naive Bayes: 0.56). These findings confirm that SVM is more robust against class imbalance in informal Indonesian-language review data.
AI-Driven Learning Personalization in LMS Platforms:A Systematic Review Of Mechanisms, Effectiveness, And Computational Challenges Jacob Alfanicolls Rahayaan; Andre Immanuel Porayou; Arpen Patanduk; Ade Yusupa
Jurnal Ilmiah Informatika dan Komputer Vol. 3 No. 1 (2026): Juni 2026 (In Progress)
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/informatech.volume3number1.517

Abstract

Background: The widespread adoption of Learning Management System (LMS) platforms in higher education has yet to overcome the fundamental limitation of uniform content delivery, which fails to accommodate individual differences in prior knowledge, learning pace, and cognitive style. Artificial Intelligence (AI) offers a transformative pathway to address this gap through data-driven personalization. Objective: This Systematic Literature Review (SLR) synthesises empirical evidence on the computational mechanisms, implementations, effectiveness outcomes, and technical-ethical challenges of AI-driven learning personalization in LMS environments within higher education, with an explicit focus on informatics and computational perspectives. Method: Adhering to PRISMA 2020 guidelines, 38 articles were selected from 312 candidates retrieved from Google Scholar, ScienceDirect, IEEE Xplore, and DOAJ (2021–2026), following three-stage screening and quality appraisal using the Mixed Methods Appraisal Tool (MMAT; minimum score 3/5). Results: Five dominant computational mechanism clusters were identified: (1) behavioral log analytics using sequence mining, clustering, and NLP; (2) academic failure prediction with Random Forest and Gradient Boosting (AUC up to 0.91, accuracy 78–89%); (3) hybrid recommender systems combining collaborative filtering, content-based filtering, and Knowledge Graph-GNN approaches (Precision@K gains of 14.3%); (4) adaptive assessment via Bayesian Knowledge Tracing combined with Item Response Theory; and (5) emerging applications of Large Language Models, Retrieval-Augmented Generation (RAG), Federated Learning, and Explainable AI (XAI/SHAP). A meta-analytic synthesis across 47 experimental studies yields a pooled effect size of d = 0.52 (medium-to-large) on academic performance. Significant challenges persist in data privacy compliance (UU PDP No. 27/2022), algorithmic fairness for 3T-region students, instructor AI literacy, and infrastructure disparity. Novelty: This review introduces a computational taxonomy of AI mechanisms in LMS, differentiating it from prior SLRs that focus predominantly on pedagogical or descriptive dimensions. Six priority research gaps are identified, including XAI adoption, culturally-fair algorithm design, and federated architectures for decentralised Indonesian institutions.
Co-Authors Andre Immanuel Porayou Arpen Patanduk Jacob Alfanicolls Rahayaan Kartyka Nababan Norris Elden Salassa Victor Tarigan Yaulie Deo Y. Rindengan