KLASIFIKASI TINGKAT KEMATANGAN BUAH NAGA KRISTAL BERDASARKAN WARNA KULIT MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS - Erick Hernando; Ade Chandra Saputra; Jadiaman Parhusip (AnoaTIK: Jurnal Teknologi Informasi dan Komputer, 2024)
This research aims to develop an effective method for determining the maturity level of dragon fruit in the harvestable, ripe, raw classes automatically by utilizing the K-Nearest Neighbors (K-NN) algorithm through the Knowledge Discovery in Databases (KDD) process. The KDD process, which involves a series of steps starting from data selection, data preprocessing, data transformation, to applying algorithms to produce useful knowledge, is used in this research to process and analyze dragon fruit image data. In this research the classification is processed through the KDD stages, including a preprocessing process to clean and prepare the data, the use of Min-Max Normalization to standardize the data so that all features are on the same scale, very important for the performance of the K-NN model, transformation to extract class data, and application of the K-NN algorithm for fruit maturity classification. The selection of the K-NN algorithm in the KDD stage is based on its simplicity and ability to classify data with a high level of accuracy. The research results show that the KDD method applied with the K-NN algorithm is able to classify the ripeness of dragon fruit with the best accuracy obtained at a value of K = 3 with an accuracy percentage of 91% without requiring physical cutting of the fruit. Thus, this research not only contributes to the field of precision agriculture but also shows how the KDD method can be applied effectively to solve real problems in the field.