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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 Bhakti: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Agunawan, Agunawan
Institut Teknologi Dan Bisnis Nobel Indonesia
Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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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.
Pengaruh Kompensasi Dan Motivasi Terhadap Kinerja Pegawai Laode Amijaya Kamaluddin; Agunawan Agunawan; Mappangara Sanusi
Economics and Digital Business Review Vol. 5 No. 1 (2024)
Publisher : STIE Amkop Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/ecotal.v5i1.1094

Abstract

Penelitian ini bertujuan untuk mengevaluasi Dampak Kompensasi dan Motivasi pada Kinerja Karyawan di PT. Boska Transportama.. Metode penelitian yang digunakan adalah kuantitatif deskriptif dengan mengumpulkan data primer melalui kuesioner. Responden penelitian terdiri dari 50 karyawan PT. Boska Transportama di Makassar. Uji hipotesis dilakukan dengan menggunakan analisis regresi linear berganda menggunakan perangkat lunak SPSS versi 25. Hasil penelitian menunjukkan bahwa kompensasi memiliki pengaruh positif dan signifikan pada kinerja karyawan di PT. Boska Transportama di Makassar. Dengan memberikan kompensasi yang sesuai dan menarik, perusahaan dapat meningkatkan motivasi dan kinerja karyawan. Tunjangan dan insentif yang tepat akan mendorong karyawan untuk bekerja lebih efektif dan produktif. Selain itu, penelitian ini juga menemukan bahwa motivasi memiliki pengaruh positif dan signifikan pada kinerja karyawan di PT. Boska Transportama. Ketika karyawan merasa termotivasi dan bersemangat dalam bekerja, mereka cenderung mencapai hasil kerja yang lebih baik dan memberikan kontribusi positif bagi perusahaan
PENERAPAN AI SEBAGAI ALAT BANTU PROSES PEMBELAJARAN DI TINGKAT PENDIDIKAN SEKOLAH DASAR Nurkhalik Wahdanial Asbara; Agunawan Agunawan; Fitriani Latief; Nurani Nurani; Auliyah Zakilah Ifani; Selvia Deviv; Dara Ayu Nianty; Yusri Mahendra; Tenri Wulandari
JMM (Jurnal Masyarakat Mandiri) Vol 8, No 1 (2024): Februari
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v8i1.20083

Abstract

Abstrak: Tujuan dari kegiatan pengabdian ini adalah memberikan pelatihan, mendampingi guru tentang perkembangan Artificial Intelligence (AI) dalam dunia pendidikan. Dengan adanya kegiatan ini, dapat meningkatkan softskill dalam proses belajar mengajar, mulai dari bagaimana guru membuat materi pembelajaran berbasis media, video pembelajaran yang menarik dengan menggunakan AI, sehingga para siswa mudah memahami apa yang disampaikan oleh gurunya dan mengintegrasikan AI dalam proses pembelajaran sehari-hari. Diharapkan setelah kegiatan ini akan membantu menciptakan lingkungan pembelajaran yang lebih efektif, efisien, dan responsif terhadap kebutuhan siswa dan guru. Dengan pendekatan yang teliti dan bijaksana, mulai dari pemaparan teori dan praktek langsung, pengembangan Artificial Intelligence (AI) dalam pendidikan dapat membawa manfaat positif dan berkelanjutan bagi semua pihak yang terlibat dalam proses pendidikan. Mitra pada kegiatan ini adalah guru SD Inpres Unggulan BTN Pemda Makassar sebanyak 18 orang. Hasil dari kegiatan ini adalah tingkat kepahaman peserta sebanyak 54%, kurang paham 29 %, dan tidak paham 17%. Hasil dari kegiatan pengabdian ini adalah mitra dapat memanfaatkan AI dalam mendesain materi pembelajaran, mengelola dan mengevaluasi proses pembelajaran.Abstract: The purpose of this service activity is to provide training, accompany teachers about the development of Artificial Intelligence (AI) in the world of education. With this activity, it can improve soft skills in the teaching and learning process, starting from how teachers create media-based learning materials, interesting learning videos using AI, so that students can easily understand what is conveyed by their teachers and integrate AI in the daily learning process. It is hoped that following these activities will help create a learning environment that is more effective, efficient, and responsive to the needs of students and teachers. With a rigorous and thoughtful approach, starting from the presentation of theory and direct practice, the development of Artificial Intelligence (AI) in education can bring positive and sustainable benefits to all parties involved in the educational process. Partners in this activity are 18 teachers of SD Inpres Unggul BTN Pemda Makassar. The result of this service activity is that partners can utilize AI in designing learning materials, managing and evaluating the learning process.
Weighted Similarity and Robustness Evaluation in a Case-Based Reasoning Expert System for Diagnosing Koi Fish Diseases Agunawan; Aulyah Zakilah Ifani; Muhammad Fadhlullah
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.13665

Abstract

Purpose – Early diagnosis of koi fish diseases remains constrained by limited expert availability and the dominance of static rule-based expert systems that are less adaptive to new cases. This study aims to develop a practitioner-informed Case-Based Reasoning (CBR) screening prototype for six koi fish disease categories using positive-evidence weighted symptom similarity. Design/methods/approach – This research used a Research and Development design involving knowledge acquisition from two koi cultivator practitioner-experts, representation of 15 clinical symptoms, and the Retrieve-Reuse-Revise-Retain cycle. The web-based system was implemented using CodeIgniter 4, PHP 8.0, and MySQL 8.0. Similarity used positive-evidence weighted Jaccard with an insufficient-evidence gate (Σsi < 2). Evaluation comprised a preliminary usability test with 15 respondents and robustness testing on 500 synthetic base profiles transformed under six disturbance scenarios. Findings – Under KB-v2 with positive-evidence similarity, the illustrative case ranked Cloudy Eye at 69.6% while Fin/Tail Rot scored 0.0% (no shared-absence inflation). On the pooled legacy set, overall accuracy was 65.0% (97.3% among scored cases), weighted F1-score was 0.76, 33.2% of cases returned insufficient evidence, and 5.4% of scored cases were ambiguous. The usability test yielded 57.7% Good, 37.7% Fair, and 4.4% Poor item responses. Research implications/limitations – The evaluation used synthetic data generated from the same practitioner knowledge matrix and has not been validated by aquatic veterinarians or real clinical field cases. Originality/value – The study contributes positive-evidence weighted similarity with an insufficient-evidence gate, a governed retain pathway for expert-confirmed cases, and robustness testing with decision-safety metrics under incomplete and noisy inputs.
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 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 Ruslan Ruslan Ruslan Ruslan Selvia Deviv Tenri Wulandari Wanda Cahyani Yamin, Muh. Arman Yusri Mahendra