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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Hadi Dwi Abdullah Hamid
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Diagnosis Penyakit Tanaman Cabai Menggunakan Metode Modified K-Nearest Neighbor (MKNN) (Studi Kasus: BPTP Karang Ploso Malang) Hadi Dwi Abdullah Hamid; Nurul Hidayat; Ratih Kartika Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Red chili is one of the most important vegetables in Indonesia, whether it is as a commodity that is consumed domestically and as an export commodity. As vegetables, beside red chili has a high nutritional value, it also has a high economic value. However, the productivity of national red chilli is still very low at 7.34 tons / ha, whereas the actual yield can potentially reach 12 tons / ha. In a planting period, chili can be harvested several times. If the season and the treatment is very good, chili can be harvested 15-17 times but generally, it can be harvested only 10-12 times. The low productivity of chili can be caused by a variety of factors, including poor quality of chili seeds, decreasing soil fertility, bad implementation of cultivation techniques, plant pest and disease problems. In order to handle this, technology is needed by applying one of the classification methods, namely Modified K-Nearest Neighbor (MKNN). Modified K-Nearest Neighbor (MKNN) is the development of the KNN method that has been designed to overcome the weaknesses of the distance between data and weight in the KNN. The method analysis is based on 18 symptoms of the disease with the process of calculating euclidean distance, calculating the validity and calculation of wighted voting which result in the determination of the classification class based on the specified K value. The test results showed that when using the value K = 5 produces an accuracy of 94%, then K = 8 produces an accuracy of 92%, K = 11 produces an accuracy of 88% and testing K = 14 produces an accuracy of 88%. Based on the results obtained, the Modified K-Nearest Neighbor (MKNN) method showed good accuracy for classifying chili disease.
Co-Authors Nurul Hidayat Ratih Kartika Dewi