Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Vol. 6, No. 1, June 2020, pp. 1~10 ISSN: 2338-3070, DOI: 10.26555/jiteki.v16i1.16630 1 Journal homepage : http://journal.uad.ac.id/index.php/JITEKI Email : jiteki@ee.uad.ac.id Implementation of Gray Level Co-occurrence Matrix on the Leaves of Rice Crops Lilis Indrayani 1 , Raden Wirawan 2 1 STMIK Kreatindo Manokwari, Jl. Kali Bambu Reremi Puncak, Manokwari, Papua Barat, 98314, Indonesia 2 STMIK Bina Adinata, Jl. Serikaya No.08, Bulukumba, Sulawesi Selatan, 92513, Indonesia ARTICLE INFO ABSTRACT Article history’s: Received 08 Mei 2020, Revised 17 June 2020, Accepted 25 July 2020. Rice is one of the cultivation plants that are very important for human survival, the success of rice harvesting affects the level of farmers' income. But farmers often suffer losses, as a result of illness in rice. Rice plants infected with the disease will show symptoms in the form of patches that have certain patterns and colors on some parts of the body of rice such as stems, leaves, and roots. Disease symptoms that arise on the leaves are most easily identified because the leaves have a wider cross-section than other body parts of rice. Therefore, in this study leaf is used as an initial step parameter for disease detection in rice. This research aims to identify diseases that exist in rice plants using the method of Gray Level Co-occurrence Matrix (GLCM). The GLCM method is a feature extraction method. The disease detection process on the leaves of the rice plants is done by retrieving the original image for the initial step, then the original image is segmented before converting to greyscale imagery. Once it is done extraction features by using GLCM features namely Entropy, Eccentricity, Contrast, Energy, Correlation, Homogeneity. The results showed 90% accuracy results using GLCM extraction. The introduction of the resulting disease can help to recognize the type of rice leaves infected with the disease. Keywords: GLCM, Gray Level, Co-occurrence Matrix, Leaves, Rice Crops. This work is licensed under a Creative Commons Attribution-Share Alike 4.0 Corresponding Author: Raden Wirawan, STMIK Bina Adinata, Jl. Serikaya No.08, Bulukumba, Sulawesi Selatan, 92513, Indonesia Email: liliraden12790@gmail.com 1. INTRODUCTION For farmers with rice, planting is their livelihood, Rice harvest success affects the level of farmers' income because most farmers rely on their life from the rice harvest. Paddy has a scientific name of Oryza sativa and belongs to the tribe of paddy-paddy or Poaceae. Paddy produces rice which is the staple food of most of our nation, so rice is one of the fields of agriculture that affects daily life. However, farmers often suffer losses [1][2], but one factor is the disease of rice. Although farmers have already gained some training and knowledge of how to care for and know the disease of paddy plants but mistakes remain occasionally occur in determining the disease [3][4]. This error occurs when human abilities are limited in knowing the disease visually [5]. Also, the characteristics of paddy disease are almost identical between one disease and the other. Referring to previous research by Dadi Rosadi and Asril Hamid under the title of research on rice crop diagnosis system using Forward Chaining method, which was designed using the Borland Delphi 7 programming language [6] And Sri Wulandari et al with the title of research system for diagnosis of pests and diseases of rice crops with Bayes method [7]. One factor of declining rice production is the disease that affects rice crops, especially in the leaves. Types of diseases in the leaves of rice crops are Blast, leaf blight, Tungro, Leaf burned [5]. The symptoms of diseases that arise in rice leaves are most easily identifiable because rice leaves have a wider cross-section than other parts of the rice body So that discoloration and spot-shape can be visible [4]. Therefore, the rice leaves can be used as the first step of disease detection in rice. With the problem, the researcher made a system for the introduction of rice crop disease using leaf parameters. For this study applied the GLCM (Gray-Level Co-occurrence Matrix) method. GLCM is used for the extraction of rice leaf characteristics by using Matlab. This research develops the image implementation using the GLCM method with feature extraction of 6 features, contrast, eccentricity, energy, homogeneity, entropy, and correlation with angles of 0 °, 45 °, 90 °, 135 ° and detect four types of rice disease are Blast, Leaf Blight, Burning Leaves, and Tungro.