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All Journal Jurnal Teknik Jurnal Inspiration Jurnal Manajemen STIE Muhammadiyah Palopo JMM (Jurnal Masyarakat Mandiri) JURNAL INSTEK (Informatika Sains dan Teknologi) YUME : Journal of Management Jurnal Dedikasi Pendidikan Economics and Digital Business Review Community Empowerment Nobel Management Review TRANSFORMASI : JURNAL PENGABDIAN PADA MASYARAKAT Jurnal Pustaka Mitra : Pusat Akses Kajian Mengabdi Terhadap Masyarakat Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems Journal of Security, Computer, Information, Embedded, Network and Intelligence System Nobel Community Services Journal International Journal of Education, Vocational and Social Science International Journal of Economics, Business and Innovation Research Jurnal Bisnis Digital dan Enterpreneur (BISENTER) Bhakti: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Agunawan, Agunawan
Institut Teknologi Dan Bisnis Nobel Indonesia
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Journal : journal of embedded systems security and intelligent systems

 1 
Implementation Of User Experience Questionnaire (UEQ) To Evaluate User Experience Of Edlink Application Eka Wijaya Paula; Aulyah Zakilah Ifani; Agunawan
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 3 (2024): November 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i3.3444

Abstract

The Nobel Institute of Technology and Business Indonesia uses EdLink in learning management, but the system has not been validated through user experience, so it is not known to what extent student satisfaction is in using it. This study was made to test student satisfaction in using the information system. Through the User Experience Quissionare (UEQ) method to 100 students as respondents. The UEQ assessment goes through 6 aspects, including efficiency, attractiveness, accuracy, clarity, stimulation, and novelty. The results obtained from the aspect of the accident received the lowest score among other aspects. While the highest aspect is the efficiency aspect.
Simulation-Based Hybrid LSTM–XGBoost Framework for Early Prediction of Simulated Learning-Loss Risk Using LMS Log Features Agunawan; Ruslan; Dandi Darmadi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13173

Abstract

Purpose – Learning loss remains a concern in Indonesian higher education after the pandemic, while LMS-based early warning systems remain limited for timely intervention. This study designs and evaluates a hybrid LSTM–XGBoost framework for early prediction of simulated LMS-based learning-loss risk, prioritizing architectural innovation over metric superiority. Methods – Using 6,440 synthetic LMS records structured from publicly documented Sevima EdLink log fields, early risk was predicted from weeks 1–4 features and labeled at week 6 with behavioral proxy rules. An 80:20 group-aware train–test split by student identifier used leakage-safe, training-only scaling and one-hot encoding. Models were compared as an untuned architectural benchmark with matched-feature ablations, approximate confidence intervals, calibration/threshold analysis, and feature importance inspection. Findings – The temporal-only LSTM model produced the strongest overall predictive performance, while the LSTM with static-feature fusion showed comparable results. The hybrid LSTM–XGBoost decision stage remained competitive but did not outperform the matched LSTM configurations or demonstrate a clear advantage over simpler baseline models. Ablation analysis further showed that neither static-feature integration nor the use of XGBoost as the final decision engine provided a meaningful performance improvement. Changes in engagement, feedback activity, interaction patterns, and early time-on-task emerged as the most influential simulated indicators of learning-loss risk. Research Implications – The hybrid architecture offers a replicable blueprint for LMS early-warning pipelines that separate temporal extraction (LSTM) from risk classification (XGBoost). Institutional use requires real LMS validation, recall optimization, and ethical compliance. Originality – This simulation-based late-fusion LSTM–XGBoost blueprint separates the prediction window (weeks 1–4) from the outcome window (week 6) and evaluates architectural contribution.
Co-Authors Abd Jamal, Abd Abd. Rahman Ahmad Firman Alwi, Nur Ainun Andi Amal Hayat Makmur Andi Ircham Hidayat Andi Widiawati Auliah, Muh. Rifky Auliyah Zakilah Ifani Aulyah Zakilah Ifani Bahrul Ulum Ilham, Bahrul Ulum Cahyani, Wanda Dandi Darmadi Dara Ayu Nianty Darius, Andi Devina Yaritsha Dirwan Dirwan E. Yunita Ferlita, Andi Iza Fika Kusuma Nur Fitriany Fitriany, Fitriany Fitryani Fitryani Fyan Wijaya Komputra Ahmadi Gafur, Gafur Harry Phinwa Sentosa Hasanah Nur Ifani, Aulya Zakila Irfan Marhaban Bin Fatahillah Ita Fitriati Jama, Ilham Muhadir Juharman, Muh. Kamaluddin, La Ode Amijaya Latief, Fitriani Mahendra, Yusri Mappangara Sanusi Mashur Razak Masita Masyita Risna Muhammad Asrauf Mustamin Muhammad Fadhlullah Muhammad Rijal Muhammad Yahya Muhammad Yassir Munajat Munajat, Munajat Muthi Syahidah Arifuddin Nurani Nurani Nurani Nurkhalik Wahdanial Asbara Nurul Istiqamah Nurul Istiqamah Paula Eka Wijaya Raden Wirawan Robin Arnan Ruslan Ruslan Ruslan Ruslan Selvia Deviv Tenri Wulandari Wanda Cahyani Yamin, Muh. Arman Yusri Mahendra