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All Journal Journal Serambi Engineering (JSE) Jurnal Bakti Dirgantara
Tri Mulya Dharma
Universitas Jabal Ghafur
Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Other

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Peningkatan Literasi AI dan Pemanfaatan Prompting Artificial Intelligence Untuk Keterampilan dan Kreativitas Siswa SMK Negeri 3 Sigli Tri Mulya Dharma
Jurnal Bakti Dirgantara Vol. 3 No. 2 (2026): Jurnal Bakti Dirgantara (In-Press)
Publisher : Universitas Dirgantara Marsekal Suryadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35968/d1336h80

Abstract

The rapid advancement of Artificial Intelligence (AI) requires students to possess digital literacy. However, initial identification of 10 11th-grade DKV students at SMK Negeri 3 Sigli revealed prompting-writing limitations and a lack of structured AI training due to manual design methods. This Community Service (PKM) program, conducted on July 13, 2026, aimed to enhance AI literacy and prompting techniques for product content creation. The learning by doing method combined theoretical lectures with practical mentoring using Gemini AI. Progress was evaluated via Quizizz pre-tests and post-tests, measuring AI literacy concepts and technical prompting formulation. Quantitative results showed students average AI literacy understanding increased from 30% to 80%, while prompting skills rose from 10% to 65%. This training successfully equipped students with creative, safe, and responsible AI prompting capabilities.
Learning Rate Tuning of Transfer Learning Models for Fresh and Spoiled Beef Image Classification Tri Mulya Dharma; Mukhsin Nuzula; Aviv Fitria Yulia; Dwi Feriyanto; Ningsiah
Jurnal Serambi Engineering Vol. 11 No. 3 (2026): Juli 2026
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

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Abstract

Beef freshness assessment is a critical aspect of the food industry, where conventional methods relying on visual inspection and olfaction are subjective and prone to human error. This study aims to develop an automated beef freshness classification system utilizing deep learning to enhance the accuracy and objectivity of quality assessments. A total of 2.800 beef images were sourced from an open research data repository, divided equally into fresh and spoiled classes. Three Convolutional Neural Networks (CNN) architectures with transfer learning VGG16, ResNet50-V2, and Inception-V3 were evaluated. The models were systematically tested using learning rate tuning at 0.01, 0.001, and 0.0001 to optimize training convergence. Evaluation results showed that VGG16 outperformed other models in classifying beef freshness. VGG16 achieved a peak testing accuracy of 99.46% at a learning rate of 0.001. The main contribution of this study is the systematic evaluation of transfer learning architectures to establish an optimal baseline for beef quality assessment. By deploying the best performing model into a web based application, this approach offers a practical, objective, and accessible alternative to conventional manual inspection, enabling rapid early detection of beef freshness to improve food safety.
Co-Authors Aviv Fitria Yulia Dwi Feriyanto Ningsiah Nuzula, Mukhsin