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Ida Bagus Kresna Sudiatmika
Primakara University
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Law, Crime, Criminology & Criminal Justice Social Sciences

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Implementation Of AI Technology As An Innovative Learning Medium At SMK Dwijendra Denpasar: Training On Prompting And The Use Of AI-Based Media For Grade XI And XII Students Ida Bagus Kresna Sudiatmika; Made Adi Paramartha Putra
Jurnal Pengabdian Mandiri Vol. 3 No. 1 (2026): Juni
Publisher : Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70963/mandiri.v3i1.1620

Abstract

The ability to utilize artificial intelligence (AI) technology is a crucial competency that young people must possess in the digital era. This community service activity aims to implement prompting training and the use of AI-based media for grade XI and XII students of SMK Dwijendra Denpasar from three departments: Computer and Network Engineering (TKJ), Accounting, and Hospitality. The implementation methods include interactive workshops, hands-on practice, and problem-based projects. A total of 87 students from the three departments actively participated in this program. The results show an increase in prompting ability by 93.7% and AI-based media usage ability by 130.0% after the training. Students also demonstrated significant improvement in confidence in using AI technology for learning and work purposes. This program proves that structured and contextual AI training is effective in improving the digital skills of SMK students across department.
Citation-Enhanced Retrieval-Augmented Generation For Automated Scientific Literature Review: A Novel Multi-Factor Ranking Approach Ida Bagus Kresna Sudiatmika; Made Adi Paramartha Putra
Jurnal Komputer Indonesia Vol. 5 No. 1 (2026): Maret
Publisher : Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jki.v5i1.1618

Abstract

Scientific literature review is a fundamental process in academic research that requires significant time and effort. This study proposes a novel framework that combines Retrieval-Augmented Generation (RAG) with Citation-Enhanced mechanisms and a Multi-Factor Ranking algorithm to automate the scientific literature review process intelligently and accurately. The proposed approach integrates three main components: (1) semantic-based document retrieval module using dense vector embeddings, (2) a citation augmentation system that analyzes citation networks between scientific papers, and (3) a multi-factor ranking algorithm that considers semantic relevance, citation impact, publication recency, and author authority. Experiments were conducted on the S2ORC (Semantic Scholar Open Research Corpus) dataset containing over 200,000 scientific papers across various domains. Evaluation using ROUGE-L, BLEU-4, BERTScore, and Citation F1 metrics demonstrates that the proposed approach yields significant improvements over conventional RAG methods. The proposed system achieves a ROUGE-L score of 0.612 and BERTScore of 0.847, improving by 8.3% and 6.1% respectively compared to standard RAG baseline. The results demonstrate that integrating citation information in the retrieval and text generation process substantially enhances the quality, accuracy, and completeness of automatically generated literature reviews.
Co-Authors Made Adi Paramartha Putra