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All Journal Jurnal Produksi Tanaman Info-Teknik Ilmu Budaya: Jurnal Bahasa, Sastra, Seni, dan Budaya Syntax Literate: Jurnal Ilmiah Indonesia International Conferences SDGs 2030 Challenges and Solutions Tibuana : Journal of Applied Industrial Engineering JURNAL PENDIDIKAN TAMBUSAI Jurnal Arsitektur dan Perencanaan (JUARA) International Journal Of Science, Technology & Management (IJSTM) Journal of Entrepreneurship & Business Jurnal Genre (Bahasa, Sastra, dan Pembelajarannya) Indonesian Journal of Multidisciplinary Science Jurnal Tarbiyah dan Ilmu Keguruan Borneo Adjektiva: Educational Languages and Literature Studies Jurnal Pengabdian Nasional (JPN) Indonesia
Arifin, Syaiful
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Application of K-Means Clustering for Detection Downy Mildew at Madura Corn Plant Using Digital Image Processing Rosyadi NR, Imron; Prasetyowati, Erwin; Said, Badar; Arifin, Syaiful; Ridoni, Mohammad Syafiir
Tibuana Vol 6 No 2 (2023): Tibuana
Publisher : UNIPA PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/tibuana.6.2.7845.147-152

Abstract

The development and cultivation of corn is necessary in line with the increasing consumption of food ingredients and industrial needs, especially food products made from corn. In the development of maize in Indonesia, the main obstacle is the disturbance of Plant Pest Organisms (OPT), especially diseases, one of which is downy mildew. This disease can be identified by a change in color, so we need a way to find out the difference between the color of healthy leaves and the color of leaves that have changed due to downy mildew. One solution that can be used is image processing. Therefore the aim of this study was to detect downy mildew based on leaf color in corn plants based on digital image processing, to produce precise and objective results. The algorithm used is the K-Means Clustering algorithm. This study uses 50 images of training data and 25 images of test data. Based on the simulation of downy mildew disease identification using K-Means Clustering it achieves an accuracy rate of 85%.  
Co-Authors Adeline, Sheyla Alfian Rokhmansyah Ananda, Anggi Risma Ayu Anwar, Rusli Asnan Hefni Azainil Azainil Bibit Suhatmady Dahri Dahlan, Dahri Damanhuri Daryono Daryono, Daryono Daud, Wennita Delsiana, Meilani Endang Dwi Sulistyowati Erwin Prasetyowati Fatmasari Endayani Fauzan Bintang Andaswara, Muhammad Fitriani, Noor Fitriansyah, Fitriansyah Hartinah, Siti Helmi, Yusfik Hestin Mulyandari, Hestin Juliansyah Juliansyah LA MUDI, LA Laksono, Rudy Dwi Lita Soetopo Lumban, Yunita M. Rusydi Ahmad, M. Rusydi Marwah Ulwatunnisa Meinar Puspasari Muhammad Anwar Mulyati Mulyati Mursalim Mursalim Nanis Hairunisya Nina Queena Hadi Putri Nisyah, Khairun Purnama, Dina Putra, Andika Dwi Putri, Afra Apriliyanti Kusuma Putri, Selvia Dwi Erwanda Resfilianda, Klarazetz Revina Ridoni, Mohammad Syafiir Rindaningtyas, Yulita Dwi Rosyadi NR, Imron Royani, Kiki Indah Rusmini Rusmini Said, Badar Salami, Ananda Anugerah Budi sanusi, Anwar Seliana, Seliana Setyawati, Meita Sihwahjoeni, Sihwahjoeni Siti Nur Hidayah, Siti Nur Susilo Susilo Syamsul Rijal Tri Indrahastuti Ulwatunnisa, Marwah Wahyono, Rudy Widyatmike Gede Mulawarman Wilianti, Regina Yusak Hudiyono, Yusak Zainal Abidin