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Ratna Ika Putri
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jasens@isas.or.id
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Indonesian Society of Applied Science Jl. Raya ITS, Sukolilo, Surabaya, 60111
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INDONESIA
Journal of Applied Smart Electrical Network and System (JASENS)
Published by Indonesian Society of Applied Science
ISSN : -     EISSN : 27235467     DOI : https://doi.org/10.52158/jasens
Core Subject : Science, Engineering,
Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Journal of applied smart electrical network and system (JASENS) aims to provide a forum for academic researchers, industrial professionals, engineers, consultants, managers, educators and practitioners to contribute and disseminate innovative new work on electrical engineering related smart electrical network and system. Scope The topical issues considered by the journal covers, but not limited to, the following topics: Application of smart grid Energy management systems (with application to building and home automation) Power system Power electronics Control engineering Industrial automation The Internet of Thing for smart electrical network system Artificial intelligent for electrical system Intelligent monitoring and outage management Smart sensors and advanced metering infrastructure Embedded systems Micro-grids Digital Protection Relay Renewable energy Energy storage and distributed energy resources
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 72 Documents
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Model Deep Learning Hybrid CNN-AE untuk Klasifikasi Presisi Warna Buah Melon Oktarina, Yurni; Dewi, Tresna; Septiyani AR, Dini
Journal of Applied Smart Electrical Network and Systems Vol. 6 No. 2 (2025): Vol. 6 No. 02 (2025): Vol 06, No. 02 Desember 2025
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/d3hydf28

Abstract

Melon fruit color classification is a critical step in assessing fruit ripeness and quality. This study proposes a hybrid deep learning model that integrates Convolutional Neural Network (CNN) and Attention Enhancement (AE) for accurate classification of melon fruit color. The model leverages CNN’s strength in visual feature extraction while enhancing focus on crucial image regions through the attention mechanism. A diverse image dataset of melon fruits was collected under various lighting conditions and angles. Pre-processing steps, including data augmentation, normalization, and image scaling, were applied to improve model generalization. The CNN-Attention hybrid architecture incorporates an attention module into the CNN layers to emphasize significant features. Comparative experiments between the standard CNN and the hybrid model demonstrate that the latter achieves superior classification accuracy, with an average improvement of 5%. Moreover, the hybrid model exhibits better robustness against image noise and lighting variations. These results indicate that incorporating Attention Enhancement can yield a more adaptive and reliable model for melon fruit color classification. The proposed approach is expected to support the development of automated systems for fruit sorting in agriculture and distribution, enhancing speed, accuracy, and efficiency for farmers, traders, and consumers.
Koordinasi Rele Arus Lebih di Perusahaan Nikel Indonesia Menggunakan Grasshopper Optimization Algorithm Putra, Riko Satrya Fajar Jaelani; Aulia Rahma Annisa; Yudi Andika; Sholahuddin Muhammad Irsyad; Rahmat Basya Shahrys Tsany
Journal of Applied Smart Electrical Network and Systems Vol. 6 No. 2 (2025): Vol. 6 No. 02 (2025): Vol 06, No. 02 Desember 2025
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/kn1tk933

Abstract

Setiap sistem kelistrikan bergantung pada ketersediaan energi yang berkelanjutan untuk menjaga produktivitas industri tetap berjalan. Gangguan listrik akan merusak peralatan listrik. Sistem proteksi berfungsi untuk meminimalkan hingga menghilangkan gangguan secara cepat, selektif, dan terkoordinasi demi meminimalkan kerusakan sistem dan memastikan pasokan listrik yang tidak terputus. Khususnya untuk rele arus lebih, time dial setting (TDS) merupakan aspek penting yang harus dipertimbangkan perihal koordinasi proteksi. TDS mengatur waktu operasi rele untuk mengamankan sistem kelistrikan. Umumnya, perhitungan manual digunakan untuk menentukan nilai TDS. Metode trial and error sering digunakan untuk mengoordinasikan rele satu sama lain. Metode ini telah berkembang menjadi algoritma cerdas yang dapat menemukan solusi secara efektif dan cepat, salah satu algoritmanya adalah grasshopper optimization algorithm (GOA). Hasil dari simulasi pada penelitian ini didapatkan nilai koordinasi antara rele 4 bekerja pada 0,1 detik & rele 3 bekerja pada 0,3 detik saat timbul gangguan pada trafo 2, rele 3 bekerja pada 0,297 detik & rele 2 bekerja pada 0,496 detik saat timbul arus gangguan pada bus 4, rele 2 bekerja pada 0,492 detik & rele 1 bekerja pada 0,493 saat timbul arus gangguan pada bus 3 serta kinerja rele 1 yang hanya sebagai primer untuk mengamankan generator bekerja pada 0,354 detik saat timbul arus gangguan pada bus 2 telah menghasilkan nilai perhitungan TDS yang akurat serta masih mematuhi aturan pengaturan minimum nilai coordination time interval (CTI) antara rele primer dan rele backup yaitu 0,2 detik dalam satu rating tegangan dan mendekati 0 di rating tegangan yang berbeda.

Page 8 of 8 | Total Record : 72

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