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p-Index From 2021 - 2026
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All Journal IJECS: Indonesian Journal of Empowerment and Community Services Journal of Embedded Systems, Security and Intelligent Systems
Herdito Ibnu Dewangkoro
Department Of Informatics, Universitas Sebelas Maret, Indonesia
Religion Humanities Computer Science & IT Education Health Professions Public Health Social Sciences

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Leveraging Generative Artificial Intelligence to Enhance the Quality of Adaptive Learning at the Junior High School Level in Surakarta City Winarno; Heri Prasetyo; Wiranto; Sari Widya Sihwi; Herdito Ibnu Dewangkoro; Tarno; Anik Indriyani
IJECS: Indonesian Journal of Empowerment and Community Services Vol. 7 No. 1 (2026): IJECS: Indonesian Journal of Empowerment and Community Services
Publisher : Universitas Veteran Bangun Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/ijecs.v7i1.8005

Abstract

ABSTRACT The primary and secondary education curriculum in Indonesia changes almost every year. These changes lead to administrative adjustments in teaching and learning documents. Consequently, teachers tend to spend less time focusing on delivering instructional content and ensuring meaningful student learning, and instead devote substantial effort to reformatting lesson plans (RPP), student activity reports (LKPD), and other administrative documents. In many cases, these documents remain largely unchanged from year to year, with minimal innovation. To address this issue, a more efficient approach is required to enable teachers to innovate in designing instructional materials, lesson plans, student worksheets, and assessment instruments. The aim of this community engagement initiative is to enhance pedagogical and digital literacy in the use of generative AI to assist teachers in drafting learning objectives, structuring classroom activities, generating guiding questions, developing variations in instructional strategies, and producing effective teaching materials tailored to students’ needs. This activity was implemented using the ADDIE framework (Analysis, Design, Development, Implementation, and Evaluation). The results indicate that participants demonstrated a strong understanding of the material and an improvement in their cognitive knowledge. Specifically, 41.67% of participants reported that the material was easy to understand, while 58.33% stated that it was very easy to understand. Furthermore, 75% of participants indicated that the facilitator delivered the material very well, and 25% rated the delivery as good. A recommendation for future initiatives is the need for ongoing support through a post-training mentoring programme to ensure an understanding of the ethical use of generative AI. Keywords: generative AI, education, prompt, adaptif learning.
Benchmarking Indonesian Transformer Models and Explainable AI for Disaster-Related Sentiment Analysis Muhammad Saifuddin Eka Nugraha; Afrizal Doewes; Herdito Ibnu Dewangkoro
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.v7i3.13295

Abstract

Purpose – This study evaluates the comparative performance of Indonesian transformer models for disaster-related sentiment analysis and examines the faithfulness of explainable artificial intelligence methods applied to the best-performing model. Methods – YouTube comments related to the 2025 Sumatra flood were processed using a hybrid labeling approach combining automatic classification and expert annotation. After preprocessing and class balancing, IndoBERT, IndoBERTweet, and IndoRoBERTa were fine-tuned using Optuna-based hyperparameter optimization. Model performance was assessed using accuracy, precision, recall, and F1-score. Integrated Gradients (IG), Local Interpretable Model-Agnostic Explanations (LIME), and SHapley Additive exPlanations (SHAP) were subsequently evaluated using the Area Under the Threshold-Performance Curve (AUC-TP) to quantify explanation faithfulness. Findings – IndoRoBERTa achieved the strongest overall classification performance among the evaluated models. Faithfulness analysis showed that IG provided the strongest overall explanation performance and performed particularly well for negative and positive sentiment, whereas LIME showed better performance for neutral sentiment. SHAP produced comparatively weaker faithfulness under the applied evaluation protocol. Research Implications – The findings demonstrate the potential of Indonesian transformer models and quantitative XAI evaluation for analyzing disaster-related social media discourse. However, the results should be interpreted cautiously because automatic labeling, confidence-based undersampling, and the absence of inferential significance testing may affect generalizability. Originality – This study integrates comparative benchmarking of Indonesian transformer models with systematic deletion-based faithfulness evaluation of multiple XAI methods, extending explainable sentiment analysis beyond predominantly qualitative interpretation.
Co-Authors Afrizal Doewes Anik Indriyani Muhammad Saifuddin Eka Nugraha Prasetyo, Heri Sari Widya Sihwi Tarno Winarno Wiranto