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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Ninda Silvia Tri Cahyani
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Penyakit Tanaman Kacang Tanah menggunakan Metode MKNN (Modified K-Nearest Neighbor) Ninda Silvia Tri Cahyani; Nurul Hidayat; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 3 (2023): Maret 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Peanuts (Arachis hypogaea) are a food crop consumed by the Indonesian people. The demand for peanuts has steadily increased to 900,000 tons, with an average annual production of 783,110 tons, or approximately 87.01%. In 2018, the value of Indonesian peanut production fell in comparison to previous years. According to the Central Statistics Agency (2018), Indonesia produced 512,198 tons of peanuts in 2018, a decrease from 638,896 tons the previous year. Several factors contribute to Indonesia's low peanut production. Pathogens such as fungi, bacteria, and nematodes attack one of them. The availability of software to assist farmers in diagnosing peanut plant diseases will be extremely beneficial in overcoming peanut crop failure caused by peanut plant disease attacks. The MKNN Modified K-Nearest Neighbor method was used in this classification of peanut plant diseases. The Modified K-Nearest Neighbor (MK-NN) method can be used to classify peanut plant diseases using two types of tests: testing the effect of the amount of training data on accuracy and testing the effect of the K value on accuracy, with 30 test data. This system is designed to assist farmers in determining the disease of peanut plants in peanut crop failures caused by attacks. System To classify with a 97% accuracy rate.
Co-Authors Edy Santoso Nurul Hidayat