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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Telematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Komputasi Jurnal Sisfokom (Sistem Informasi dan Komputer) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Mantik Jurnal Infortech Journal of Computer Science and Engineering (JCSE) Jurnal Teknologi Informatika dan Komputer Jurnal Sistem Informasi (JSI) Abditeknika - Jurnal Pengabdian Kepada Masyarakat Reputasi: Jurnal Rekayasa Perangkat Lunak IMTechno: Journal of Industrial Management and Technology Info Abdi Cendekia SATIN - Sains dan Teknologi Informasi Jurnal Komputer Antartika Informatics and Software Engineering (ISE)
Agus Junaidi
Universitas Bina Sarana Informatika
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : Jurnal Komputasi

 1 
Klasifikasi Penyakit Daun Padi menggunakan Random Forest dan Color Histogram Sarifah Agustiani; Yoseph Tajul Arifin; Agus Junaidi; Siti Khotimatul Wildah; Ali Mustopa
Jurnal Komputasi Vol 10, No 1 (2022)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v10i1.2961

Abstract

Indonesia is an agrarian country, which is a sector that plays an important role most of the Indonesian population makes agriculture the main focus, but the function of rice fields into housing or industry has resulted in a decrease in rice production, in addition to pests, diseases, unfavorable weather, Irrigation is not smooth resulting in less than the maximum yield. For this reason, it is necessary to have technology that can implement the process of detecting rice leaf disease in order to provide information to farmers about rice leaf damage. The most modern approach today can be done with machine learning or deep learning by using various algorithms to improve recognition and accuracy in the detection and diagnosis of plant diseases. Based on this, this study aims to propose a method of classifying rice leaf diseases in order to provide information to farmers about rice leaves which are expected to reduce the disease by detecting the disease early so as to increase rice production. In this study, the classification process is carried out using the augmented image, then the Color Histogram feature extraction method is applied, and the classification is carried out using the Random Forest algorithm. In addition, this study also conducted several comparisons, including feature extraction and yahoo to get the results, and the highest results reached 99.65% of the proposed method.
Co-Authors Agustiani, Sarifah Ahmad Yani Ahmad Yani Aji Miftahus Salim Ali Mustopa, Ali Alif Rahman Andi Saryoko Angga Eko Pratama Angga Riski Ramadan Anis Suryatri AS, Usman Asyiri, Syekh Mohammad Candra Sumirat Cucu Ika Agustyaningrum Devi Angelina Simaremare Dewi Kartika Donna Setiawati Fany Visella febryan_wiraputra febryan Firdaus Idam Fitrayuda Rivaldy Frisma Handayanna Frisma Handayanna Gustin Setyaningsih Hardiyan Haryani Indra Permana Putra intan Irwan Agus Sobari Jananto Watori Khairunnisa Zakaria Maruloh Mochammad Abdul Azis MOHAMAD SYAIFUL AMRI Popon Handayani Popon Handayani Pribadi, Denny Rachmat Hidayat Riska Aryanti Rizki Wahyudi Rudianto Rudianto S.M Santi Winarsih Samsiyatun Samsiyatun Samudi Samudi Sandra Jamu Kuryanti Siti Khoiriyah Siti Khotimatul Wildah Siti Marlina Siti Nur Khasanah Sopiyan Dalis Syekh Mohammad Asyiri Usman AS Wahyudin Wahyudin Yoseph Tajul Arifin Yunita Yunita Yunita Yunita Yunita Yunita Yunita Yunita Yunita