Sistem Pendukung Keputusan Diagnosis Penyakit Tanaman Kacang Tanah Berbasis K-Nearest Neighbors - Husain, T; Herlinda, Herlinda; Santi, S; Kasmawaru, Kasmawaru; Ahmad, Ahmad; Nurdiansah, Nurdiansah (Jurnal Teknologi Komputer dan Informasi, 2026)
This study aims to design and develop a web-based decision support system to detect diseases in peanut plants using the K-Nearest Neighbor method. The research was conducted in Kelurahan Tolo, Kelara District, Jeneponto Regency, an area with significant peanut farming activity. The system was developed to enable farmers to independently identify plant diseases based on observed symptoms without relying entirely on agricultural experts. The dataset used in this study consisted of 50 samples, including 40 training data and 10 testing data. The prediction process was carried out using the K-Nearest Neighbor algorithm with a parameter value of k = 3, as it provided the most suitable classification results. System performance was evaluated using a confusion matrix along with precision, recall, and F1-score calculations to measure the effectiveness of the proposed method. The results indicate that the system is capable of identifying peanut plant diseases with good accuracy. The implementation of this system can help farmers obtain disease information more quickly, reduce errors in pesticide application, and improve the effectiveness of plant treatment, thereby supporting agricultural productivity. Keywords: KNN; peanut disease; decision support system; prediction; web application