Sistem Diagnosis Penyakit Tanaman Kentang Menggunakan Metode K-Nearest Neighbor - Syndu Pramanda Galuh Widestra; Nurul Hidayat; Ratih Kartika Dewi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Potato is one of the potential plant to be grown by people. The production and productivity of potato in indonesia decreases each year. Badan Pusat Statistik (BPS) takes a note of the potato production in Indonesia and it is getting reduction of 9.82% from 1.176.304 ton in 2009 to 1.060.805 ton in 2010. The potato productivity also decreases from 16.51 t/ha in 2009 to 15.95 t/ha in 2010. The problem which causes its decreasing productivity is pest attacks and disease, so it needs a system which can help to diagnose it since early time from the pest attacks and the diseases of potato. Method which can be applied to solve the problem in diagnosing the disease as well as doing the prediction is by using K-nearest neighbor (kNN). Based on the functional testing, the disease diagnosis system in potato plants works well according to the design requirements and successfully implemented in the form of a software. In this study, the number of K have little effect on the accuracy, because after being tested, it turns out that the more the k value does not guarantee its accuracy, and vice versa. The K-nearest neighbor method is good for the diagnosis of potato plants because it produces result an average accuracy of 91.785%.