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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab IJCOSIN : Indonesian Journal of Community Service and Innovation
Affan Hilmy Natsir
Telkom University
Computer Science & IT Education Engineering Other

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Pelatihan Pembelajaran Berbasis Pemanfaatan AI untuk Meningkatkan Produktivitas Guru di Era Digital Affan Hilmy Natsir; Dany Candra Febrianto
Indonesian Journal of Community Service and Innovation Vol. 6 No. 1 (2026): April 2026
Publisher : LPPM IT Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/ijcosin.v6i1.10495

Abstract

Perkembangan teknologi digital, khususnya Artificial Intelligence (AI), memberikan peluang strategis dalam meningkatkan produktivitas guru dan kualitas pembelajaran di era digital. Namun, pemanfaatan AI oleh guru masih terbatas akibat rendahnya literasi digital, kurangnya pemahaman etika penggunaan AI, serta minimnya pendampingan praktis. Artikel ini bertujuan untuk mendeskripsikan pelaksanaan dan dampak kegiatan Pengabdian kepada Masyarakat berupa pelatihan pengembangan pembelajaran berbasis pemanfaatan AI yang dilaksanakan oleh Himpunan Mahasiswa Teknik Informatika Telkom University Purwokerto di SDIT Top Kids. Metode kegiatan meliputi pemaparan materi, workshop praktik langsung, dan diskusi interaktif yang difokuskan pada penggunaan ChatGPT untuk penyusunan materi ajar, pembuatan soal, perencanaan pembelajaran, serta pemahaman etika dan risiko penggunaan AI dalam pendidikan. Evaluasi dilakukan melalui pre-test dan post-test serta observasi selama kegiatan. Hasil menunjukkan peningkatan rata-rata pemahaman peserta sebesar 27%, mencakup aspek konseptual, pedagogis, dan etika pemanfaatan AI. Temuan ini menunjukkan bahwa pelatihan berbasis praktik AI efektif dalam meningkatkan produktivitas dan kompetensi digital guru secara bertanggung jawab serta mendukung inovasi pembelajaran berkelanjutan.
HYBRID REFERENCE GENERATION WITH MMR AND TRANSFORMER MODELS FOR AUTOMATIC SHORT ANSWER SCORING Affan Hilmy Natsir; Clara Diva; Husni Mubarak
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7123

Abstract

The rapid transition to online learning during the COVID-19 pandemic has accelerated the demand for automated assessment systems capable of evaluating students’ understanding objectively, consistently, and efficiently. Automatic Short Answer Scoring (ASAS) has emerged as a central challenge within Natural Language Processing (NLP), particularly for assessing short or open-ended answers. This research proposes an ASAS framework that integrates Reference Answer Generation using Transformer-based models (BART and GPT-2) with the Maximum Marginal Relevance (MMR) method. While MMR has been widely used as an extractive technique to select diverse student answers as reference candidates, its performance remains limited by the availability and variability of student responses. To address this limitation, the proposed approach supplements MMR-generated references with paraphrased reference answers generated by fine-tuned Transformer models, thereby increasing lexical and syntactic diversity. Experimental evaluation was conducted on the Texas Short Answer Corpus, consisting of 2,442 student responses across 10 tasks. The results demonstrate that the proposed hybrid approach improves scoring performance, as indicated by an increase in Pearson correlation and a reduction in RMSE and MAE compared to the baseline MMR-based method.  
Co-Authors Clara Diva Dany Candra Febrianto Husni Mubarak