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All Journal Journal of Applied Engineering and Technological Science (JAETS) Warta LPM
Agustianto, Satya Helfi
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Implementasi Platform Si Cantik Bangsa di Kabupaten Kebumen dalam Pembangunan Desa Bahtiar, Arief Rais; Ramadhani, Rima Dias; Hikmaturokhman, Alfin; Nugraha, Novanda Alim Setya; Raharja, Pradana Ananda; Anto, Novri; Muna, Bunga Laelatul; Setiawan, Rehan Nur; Fadilah, Moh Lutfi; Agustianto, Satya Helfi; Mahargyani, Yasinta Swasti; Purwono, Adhi
Warta LPM WARTA LPM, Vol. 27, No. 2, Juli 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/warta.v27i2.4815

Abstract

Si Cantik Bangsa is a platform that aims to record participation and equality in village development. Through this community service, the focus is emphasized on understanding the problems of women's empowerment in development in three villages, namely Logede Village, Ampelsari Village, and Kaliputih Village in Kebumen Regency. In this context, the main concern is overcoming the significant decline in the Gender Empowerment Index which is a serious challenge faced by the Community and Village Empowerment Service (PMD). In an effort to overcome this problem, the PMD Department introduced an innovative program known as Planning Literacy Women (WANI LEmPER) which is in line with the Sustainable Development goals (SDGs) related to gender equality and women's empowerment. However, even though the WANI LEmPER program has been implemented, there are still obstacles that arise due to cultural resistance and rejection from various parties. Therefore, this service offers a solution through the implementation of Si Cantik Bangsa, an information technology-based platform that aims to document the activities of LEmPER WANI cadres and monitor women's participation. The service results show that indicators of success have been achieved, where 65% of respondents understand the benefits and features of Si Cantik Bangsa, while 25% still do not understand them. Thus, the use of this platform is considered a strategic step in increasing women's empowerment in village development planning to achieve SDGs and Gender Empowerment goals.
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 Ajeng Dyah Kurniawati Alfin Hikmaturokhman Anto, Novri Arief Rais Bahtiar Dasril Aldo Dedy Agung Prabowo Fadilah, Moh Lutfi Fauzi Ahmad Muda Mahargyani, Yasinta Swasti Muna, Bunga Laelatul Novanda Alim Setya Nugraha Pangestu, Farhan Aryo Pradana Ananda Raharja Purwono, Adhi Ramadhani, Rima Dias Saputra , Wahyu Andi Setiawan, Rehan Nur Sudianto, Sudianto Sulaeman, Gilang Yasin, Feri