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All Journal TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat Micronic: Journal of Multidisciplinary Electrical and Electronics Engineering
Andi Nur Faisal
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Optimalisasi Deteksi Kerusakan Elektrikal Panel Surya dengan Transfer Learning dan Augmentasi Terkontrol berbasis YOLOv8 Andi Nur Faisal; Nuran, Andi Shridivia
Micronic: Journal of Multidisciplinary Electrical and Electronics Engineering Volume 3, Issue 1, Juni 2025
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/912s5p11

Abstract

Electrical fault detection in solar panels is a critical challenge in maintaining the efficiency of large-scale photovoltaic energy systems. This research develops a deep learning-based automated classification model by leveraging the YOLOv8-CLS architecture, refined through transfer learning and systematically applied data augmentation. The dataset consists of two panel condition classes, clean and electrical-damage, which were preprocessed through image size normalization, tensor transformation, and augmentation using RandAugment and random erasing. The model was trained for 15 epochs with fine-tuning applied to the head, while the backbone retained pretrained weights. Performance evaluation showed that the model achieved a Top-1 Accuracy of 98.21%, with precision for the electrical-damage class reaching 100%, recall at 94.12%, and an F₁-score of 0.9697. Furthermore, an average inference time of 18.82 milliseconds per image demonstrates high computational efficiency for real-time deployment. These findings indicate that the integration of the YOLOv8 architecture with transfer learning and controlled augmentation is effective for detecting electrical faults in solar panels and is suitable for implementation in automated monitoring systems based on edge or cloud computing.
Pelatihan Integrasi Perplexity Untuk Penelusuran Literatur Dan Penulisan Karya Ilmiah Di SMAN 5 Makassar Andi Shridivia Nuran; Aulia Rahmah; Hamidah Hamris; Azizah Fauziah Misbahuddin; Andi Nur Faisal
TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat Vol. 5 No. 3 (2025): TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat (September)
Publisher : Fakultas Teknik, Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/tekiba.v5i3.5343

Abstract

The rapid development of information and communication technology has created challenges for students in accessing credible scientific references and writing structured academic papers. At SMAN 5 Makassar, many students, especially those in the Kelompok Ilmiah Remaja (KIR), reported difficulties in conducting literature searches, evaluating reliable sources, and managing scientific references effectively. To address this issue, a community service program was conducted through training on the integration of Perplexity AI as a tool for literature search and scientific writing. The training was held on May 5, 2025, involving 30 students and supervising teachers. The implementation methods included interactive lectures, demonstrations, guided practice, group discussions, and reflection sessions. Evaluation was carried out through direct observation and performance assessments during the practice sessions. The results showed a significant improvement in students’ digital literacy and research skills. Before the training, less than 20% of participants were able to independently search and manage scientific references; after the program, more than 80% successfully utilized Perplexity AI to find academic references, structure scientific papers, and apply academic ethics, including plagiarism avoidance. Positive feedback was also received from teachers, and a sustainable mentoring group was formed to maintain the program’s impact. This initiative demonstrates that integrating AI-based tools can effectively enhance students’ research competence and foster a more adaptive digital learning ecosystem in schools
Co-Authors Andi Shridivia Nuran Aulia Rahmah Azizah Fauziah Misbahuddin Hamidah Hamris Nuran, Andi Shridivia