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All Journal Jurnal Pendidikan IPA Veteran Conference SENATIK STT Adisutjipto Yogyakarta Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Zonasi: Jurnal Sistem Informasi Joined Journal (Journal of Informatics Education) Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Telecommunication, Electronics and Control Engineering (JTECE) Indonesian Journal of Informatics and Research Journal Of Human And Education (JAHE) Journal of Electronic and Electrical Power Application Jurnal Pengabdian Mitra Masyarakat (JPMM)
Rizki Noor Prasetyono
Universitas Peradaban
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Zonasi: Jurnal Sistem Informasi

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DETEKSI PENYAKIT BERCAK COKLAT, COKLAT SEMPIT DAN HAWAR MELALUI SPEKTRUM WARNA CITRA DIGITAL DAUN PADI MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK rito cipta; Khoerunisa; Nurul Mega Saraswati Saraswati; Rizki Noor Prasetyono; M. Zidan Alfariki
ZONAsi: Jurnal Sistem Informasi Vol. 5 No. 2 (2023): Publication Periodic ZONAsi: Jurnal Sistem Informasi.
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v5i2.13245

Abstract

Rice is a prominent food crop commodity and has high potential in the agricultural sector, where rice is a staple food source for Indonesian people. This rice plant certainly has several obstacles, one of which is the presence of rice plant disease attacks through rice leaf spot which can cause crop failure, causing farmers to experience many losses and resulting in poor crop quality, namely empty or empty rice. The long identification process and if the treatment for this disease is very slow will cause the cost of treatment to swell. The use of digital image processing technology in solving problems in this study is to identify rice diseases through digital images based on the morphology of rice leaf spots. One way is by image classification or object classification in the image. The method that can be used in classifying this image is the Convolutional Neural Network (CNN). The accuracy obtained from the Convolutional Neural Network method is based on the 2 types of architecture used, namely the Letnet-5 architecture produces an accuracy of 85% and the Custom architecture produces an accuracy of 90%.
Co-Authors Anggraeni Mashinta Sulistyani Anggy Eri Sodilesmana Arif Nursetyo Ary Syahriar Ayu Siska Tri Mayasari Dian Ayu Kusuma Intan Dwi Hesty Kristyaningrum Ira Palupi Inayah Ayuningtyas Isnu Gita Kumara Jati Rinakri Atmaja Khoerunisa M. Zidan Alfariki Metiya Fatikhatur Rizqiyah Mohamad Furqon Muhammad Zidan Alfarikhi Nasrulloh Nasrulloh Nur Cholis Nurul Mega Saraswati Nurul Mega Saraswati Saraswati Randi Adzin Murdiantoro Rito Cipta sigitta Hariyono Rizky Mubarok Sarah Dien Hawa Trisnawati, Eka winarto winarto Yogi Ade Putra Zidan Alfarikhi