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All Journal Journal of Energy, Mechanical, Material and Manufacturing Engineering Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Journal of System and Computer Engineering
A. Syahrinaldy syahruddin
Institut Teknologi Bacharuddin Jusuf Habibie
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Mechanical Engineering Other

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Implementasi Pembelajaran Berbasis Kecerdasan Buatan Di Upt Sd Negeri 16 Parepare Muhaimin Hading; Radhiansyah Radhiansyah; Nurul Chairunnisa Noor; A. Syahrinaldy syahruddin; A. Inayah Auliyah; Andi Nurfadillah Ali; Muhammad Ikhwan Burhan; Muhammad Irsan
Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Vol. 6 No. 2 (2025): Volume 6, No 2, Juni 2025
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/atjpm.v6i2.6311

Abstract

Pemanfaatan teknologi dalam pendidikan dasar menjadi semakin krusial di tengah perkembangan era digital dan Revolusi Industri 4.0. Kegiatan pengabdian ini dilatarbelakangi oleh pentingnya peningkatan literasi teknologi di lingkungan sekolah dasar, khususnya dalam pemanfaatan kecerdasan buatan (Artificial Intelligence/AI) untuk mendukung proses belajar mengajar yang lebih interaktif dan adaptif. Program ini dilaksanakan di UPT SD Negeri 16 Parepare dengan tujuan utama untuk mengimplementasikan pendekatan pembelajaran berbasis AI. Metode pelaksanaan meliputi pelatihan intensif kepada guru mengenai konsep dan praktik penggunaan AI, penerapan langsung AI dalam kegiatan pembelajaran di kelas, serta evaluasi untuk mengukur dampak kegiatan. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan guru dalam mengintegrasikan AI ke dalam pembelajaran, serta meningkatnya minat dan partisipasi aktif siswa selama proses belajar. Kegiatan ini memberikan kontribusi positif dalam memperkenalkan transformasi digital di lingkungan sekolah dasar, serta berpotensi menjadi model replikasi untuk sekolah lainnya.
Attention-Driven Contrastive Learning for the Identification of Rare Partial Discharge Signal in GIS Muhaimin Hading; Herviana Herviana; Muh. Ikhsan Amar; A. Syahrinaldy Syahruddin; Muhammad Irsan; Aulia Salsabila R.H
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2664

Abstract

Gas-insulated switchgear (GIS) is a critical component in high-voltage power transmission systems, where partial discharge (PD) activity can indicate early-stage insulation defects. However, phase-resolved partial discharge (PRPD)-based fault diagnosis remains challenging due to noisy signals, perturbed measurement conditions, and severe class imbalance, particularly for rare floating-electrode defects. This study proposes an attention-driven contrastive learning framework for rare PD signal identification in GIS. PRPD data are represented as two-dimensional density matrices derived from phase angle, discharge magnitude, and occurrence count. The proposed framework applies PRPD-specific data augmentation, followed by ResUNet-based denoising, CBAM-based feature refinement, and supervised contrastive learning to improve feature separability among PD classes. The framework was evaluated using a public 550 kV GIS PRPD dataset containing corona-type, surface-type, floating-electrode-type, and noise classes. The results show that augmentation substantially improved robustness. When trained with raw data, the proposed model achieved 91.90% accuracy and 53.81% F1-score under the original test scenario, but decreased to 48.76% accuracy and 46.26% F1-score under IEC-perturbed testing. After augmentation, the model achieved 98.24% accuracy and 96.58% F1-score under the original scenario, and maintained 97.44% accuracy and 97.76% F1-score under IEC perturbation. These findings indicate that the proposed framework supports robust PRPD representation learning for GIS PD diagnosis under perturbed and imbalanced conditions.
Photovoltaic performance analysis using Computational Fluid Dynamics (CFD) simulation methods A. Syahrinaldy Syahruddin; Radhiansyah; Marfiansyah Nasra Dwiprayuda
Journal of Energy, Mechanical, Material, and Manufacturing Engineering Vol. 10 No. 2 (2025)
Publisher : University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jemmme.v10i2.42915

Abstract

Photovoltaic technology is increasingly in demand by the wider community. Situated in the tropics, Indonesia benefits from abundant sunlight, positioning it favourably for the adoption of this technology. Studies on photovoltaics have been conducted in potential areas, including Parepare, South Sulawesi. As one of the South Sulawesi regions with strong agricultural and industrial potential, it is currently pursuing sustainable renewable energy development, encompassing photovoltaic technology. This study aims to examine the performance of solar photovoltaic systems through Computational Fluid Dynamics (CFD) simulations. The solar photovoltaic system is modelled and simulated using fluid-flow simulation software. ANSYS Fluent Student is applied with a constant solar radiation of 800 V/m2 for an hour. Experimental test data serve as a reference for simulation in this study. The results indicate that the maximum temperature ranged from 47.7 °C to 50 °C, represented by an orange-to-red colour gradient in the temperature contour, while the minimum temperature ranged from 26.9 °C to 31 °C, represented by a dark-to-light blue gradient. This suggests that the one-hour heating process under constant solar radiation, as simulated by this method, operates effectively and more accurately reflects real-world conditions.
Co-Authors A. Inayah Auliyah Andi Nurfadillah Ali Aulia Salsabila R.H Hading, Muhaimin Herviana Herviana Marfiansyah Nasra Dwiprayuda Muh. Ikhsan Amar Muhammad Ikhwan Burhan Muhammad Irsan Nurul Chairunnisa Noor Radhiansyah Radhiansyah Radhiansyah