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All Journal Journal of Applied Engineering and Technological Science (JAETS) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan
Pangestu, Farhan Aryo
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Penerapan Teknologi IoT dan Energi Terbarukan untuk Meningkatkan Efisiensi Budidaya Ikan di Desa Kasegeran Aldo, Dasril; Ginting, Melinda Br; Tanjung, Nia Annisa Ferani; Yasin, Feri; Sulaeman, Gilang; Pangestu, Farhan Aryo
Jurnal Pengabdian Masyarakat: Pemberdayaan, Inovasi dan Perubahan Vol 4, No 5 (2024): JPM: Pemberdayaan, Inovasi dan Perubahan
Publisher : Penerbit Widina, Widina Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59818/jpm.v4i5.836

Abstract

This service activity aims to overcome the high energy costs due to the use of excavated wells by fish farmers in Kasegun Village. Solar panel technology and Internet of Things (IoT) systems are applied to reduce energy costs and monitor pond water quality in real-time. This program is carried out through a series of technical stages including situation analysis, Focus Group Discussion (FGD) to identify technology needs, installation of solar panels and IoT sensors, as well as training on the use and maintenance of equipment. The results of the application of technology show a reduction in energy costs by 80%, a reduction in energy waste of up to 85%, and an increase in operational efficiency from 50% to 90%. The fish mortality rate also dropped from 25% to 3.25%. The success of the program is supported by partners' active participation in FGDs and trainings, which increases their understanding and independence in utilizing technology. However, the sustainability of the program depends on ongoing technical assistance.ABSTRAKKegiatan pengabdian ini bertujuan untuk mengatasi tingginya biaya energi akibat penggunaan sumur galian oleh pembudidaya ikan di Desa Kasegeran. Teknologi panel surya dan sistem Internet of Things (IoT) diterapkan untuk mengurangi biaya energi serta memantau kualitas air tambak secara real-time. Program ini dilaksanakan melalui serangkaian tahapan teknis meliputi analisis situasi, Focus Group Discussion (FGD) untuk mengidentifikasi kebutuhan teknologi, pemasangan panel surya dan sensor IoT, serta pelatihan penggunaan dan perawatan alat. Hasil penerapan teknologi menunjukkan penurunan biaya energi sebesar 80%, pengurangan pemborosan energi hingga 85%, dan peningkatan efisiensi operasional dari 50% menjadi 90%. Tingkat kematian ikan juga turun dari 25% menjadi 3,25%. Keberhasilan program ini didukung oleh partisipasi aktif mitra dalam FGD dan pelatihan, yang meningkatkan pemahaman dan kemandirian mereka dalam memanfaatkan teknologi. Namun, keberlanjutan program bergantung pada pendampingan teknis yang berkelanjutan.
Hybrid Optimization Model for Integrated Image Data Extraction Expert System in Rice Plant Disease Classification Aldo, Dasril; Kurniawati, Ajeng Dyah; Prabowo, Dedy Agung; Fauzi, Ahmad; Saputra , Wahyu Andi; Sudianto, Sudianto; Yasin, Feri; Agustianto, Satya Helfi; Pangestu, Farhan Aryo; Sulaeman, Gilang
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.6633

Abstract

The purpose of this study is to increase the accuracy for rice plant disease classification by developing a hybrid optimization model using Convolutional Neural Network (CNN) in combination with Extreme Learning Machine (ELM), followed by Support Vector Machine. A key issue is to overcome with traditional expert systems that difficult, due the variation differences and complex among rice plant image data set. For feature extraction, plant images are passed through CNN and for classification ELM & SVM used. Experimental results show the best accuracy of 98.63% is attained using CNN+ELM model on images resized to 100x100 pixels and has precision, recall, F1-Score all at value=0.99 By comparison, the CNN+SVM model achieves an accuracy of 91.92% using that same image size. Top AbstractIntroductionMethodsResultsDiscussionConclusionReferencesOverall, the proposed CNN+ELM combination can classify rice plant diseases better than using only a conventional approach (CNN) through various results from devices with limited computing power. The study presents a novel plant disease detection system that can be utilized for the development of precise tools to help improve agricultural management practices.
Co-Authors Agustianto, Satya Helfi Ajeng Dyah Kurniawati Dasril Aldo Dedy Agung Prabowo Fauzi Ahmad Muda Melinda Br Ginting Nia Annisa Ferani Tanjung Saputra , Wahyu Andi Sudianto, Sudianto Sulaeman, Gilang Yasin, Feri