<![CDATA[Pepper plants are agricultural commodities in need in everyday life today, because the plant has many uses. In addition, the chili plant is a plant that was a high economical value. Some of the problems in controlling diseases and pests, among others, are early symptoms that are not clearly visible so that farmers and communities find it difficult to detect the causes of damage that attacks the chilli plants, causing farmers to experience a decrease in crop production and even cause crop failure. The method used in solving this problem is the K-means Algorithm. This K-Means clustering algorithm is a method that attempts to classify existing data into several groups, where data in one group has the same characteristics with each other and has different characteristics from the data in other groups. Chilli data processed in this study was sourced from the working area of the Horticulture and Plantation Food Crops Office of West Sumatra Province with a total of 11 pieces of data. Furthermore, the data is processed using rapidminer studio 7.6 software. The results of the testing of this method there are three grouping causes of damage to plants, namely C0 for chili species with moderate damage, C1 for heavy damage and C2 for mild damage. Then the results of each cluster, namely C0 there is one type of pest, namely Fruit Fly, C1 there are 3 types of pests which consist of Yellow Virus, Anthracnose and Thrips, while C2 there are 7 types of pests consisting of aphids, mites, Fusarium wilt, Layu Bakeri, Curly Virus, Mati Pucuk, Puru fruit. This analysis is expected to make it easier for farmers to know the causes of damage to chili plants or related agencies can take action to anticipate the causes of damage to chili plants as quickly as]]>
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