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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika INTEGER: Journal of Information Technology Jurnal Varian Jurnal Aplikasi Dan Inovasi Ipteks SOLIDITAS Jurnal Mnemonic Journal of Electrical Engineering and Computer (JEECOM) Journal of Applied Smart Electrical Network and System (JASENS) JASTEN (Jurnal Aplikasi Sains Teknologi Nasional Magnetika: Jurnal Mahasiswa Teknik Elektro
Irmalia Suryani Faradisa
Teknik Elektro, Institut Teknologi Nasional Malang
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Physics Social Sciences Other

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Classification of Betel Leaf Diseases Based on Convolutional Neural Network to Increase Production Herbal Spice Materials Tri Wahyuningrum, Dr. Rima; Hamed Ayani, Irham; Bauravindah, Achmad; Siradjuddin, Indah Agustien; Faradisa, Irmalia Suryani
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 1 (2025): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i1.4653

Abstract

Traditional medicine is the practice of utilizing medicinal plants to treat various illnesses, passed down from generation to generation. In Indonesia, there are various traditional medicines, one of which is using green betel leaves. One part of the green betel plant that is commonly attacked by pests is the leaf. The Convolutional Neural Network (CNN) method is a very common method used for image classification because this method produces the highest accuracy in classification and pattern recognition. This research uses data totaling 4000 images which are divided into four classes: healthy green betel leaves, anthracnose green betel leaves, bacterial spot betel leaves, and healthy red betel leaves. Detecting the disease type facilitates farmers in acknowledging the necessary measures required to provide treatment. Therefore, this study utilizes the benefits of the CNN approach, specifically its capability to conduct precise object detection and classification in image data, to minimize the widespread of disease. The CNN architectures implemented are DenseNet201, EfficientNetB3V2, InceptionResNetV2, MobileNetV2 and XceptionResnet50V2. Based on our research, the InceptionResNetV2 model achieved the highest performance with an accuracy of 86.0%, loss of 0.3880, and ROC of 98.0%. In the other hand, the MobileNetV2 and EfficientNetV2B3 models suffered from overfitting and underfitting and the models failed to classify betel leaf diseases.
Desain PLTS Off-Grid Dengan Teknologi Penyimpanan Energi Pumped Storage Di Mahakam Ulu Muhamad Cahyo Samudro; Widodo Pudji Muljanto; Faradisa, Irmalia Suryani; Sotyohadi
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/sfhg7b24

Abstract

This research presents the design of an off-grid Solar Power Plant (PLTS) integrated with pumped storage energy technology in Mahakam Ulu Regency, East Kalimantan, as a sustainable energy solution for remote regions. The main objective of this study is to develop a reliable, efficient, and environmentally friendly power generation system by utilizing local renewable energy potential. The research methodology includes identifying 24-hour load demand, analyzing PLTS capacity based on solar radiation potential, calculating hydrological parameters such as water head and flow rate, and determining the capacity and dimensions of key components, including turbines, penstock pipes, pumps, inverters, and storage reservoirs. The technical data show a reservoir volume of 566,784 m³, a head height of 100 meters, a penstock diameter of 1.9 meters, a PLTS capacity of 10.3 MWp using 13,734 solar panels, and a 15 MW inverter. The analysis results indicate that the designed system can maintain continuous electricity supply during nighttime or low-solar conditions through stored potential energy utilization. This configuration significantly reduces dependency on diesel generators, decreases carbon emissions, and supports the national clean energy transition agenda. The off-grid PLTS design integrated with pumped storage technology is proven to be a feasible and sustainable approach that can serve as a reference for developing similar renewable energy systems in other isolated 3T regions with comparable geographical and natural characteristics.
Co-Authors 1Mohamad Cholil Achmad Sahrul Afandi Agung Darmawan Ahmad Dony Mutiara Bahtiar Alfarid Hendro Yuwono Aryuanto Soetedjo Basuki Widodo Bauravindah, Achmad Bima Romadhon Parada Dian Palevi Budi Fathony DAMARDHI, SAHI RAFAEL Dyah Ayu Girindraswari Eko Yohanes Setyawan F. Yudi Limpraptono Febriana Santi W Febriana Santi Wahyuni Hamed Ayani, Irham I Komang Somawirata kukuh prayoga pangestu prastiyawan M. Ibrahim Ashari M. Imron Khoirus Saleh Mohammad Ibrahim Ashari Muhamad Cahyo Samudro Muljanto, Widodo Pudji Nicola Natha Teopilus Ginting Parama Diptya Widayaka Pradana, Yuditya Adi Pranata, Sandio Dwi Radimas Putra M. D. L. Radimas Putra Muhammad Davi Labib Rizqi Cahyo Muflih Putra - Roichan Nafis Sandy Dwi Kurniawan Setiawan, Katon Bagus Siradjuddin, Indah Agustien Sirojul Hadi Sotyohadi Tri Wahyuningrum, Dr. Rima Widodo Pudji Muljanto Yogi Nugraha Aditama Yoppy Aldya