Penelitian ini bertujuan untuk membandingkan kinerja metode K-Nearest Neighbor (KNN) dan Artificial Neural Network (ANN) dalam mengklasifikasikan tingkat kematangan cabai menggunakan citra digital berbasis segmentasi warna HSV. Permasalahan utama adalah proses penentuan kematangan cabai yang masih dilakukan secara manual dan bersifat subjektif. Metode penelitian meliputi pengolahan citra, segmentasi HSV, ekstraksi fitur warna, serta klasifikasi menggunakan KNN dan ANN dengan bantuan MATLAB. Hasil penelitian menunjukkan bahwa kedua metode mampu melakukan klasifikasi dengan baik, dimana metode K-Nearest Neighbor (KNN) memiliki tingkat akurasi yang lebih tinggi dibandingkan Artificial Neural Network (ANN) pada dataset yang digunakan.
Kata Kunci: Kematangan Cabai, HSV, K-Nearest Neighbor, Artificial Neural Network, Pengolahan Citra Digital.

This study aims to compare the performance of the K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) methods in classifying the level of ripeness of chili using digital images based on HSV color segmentation. The main problem is the process of determining chili ripeness which is still done manually and is subjective. The research methods include image processing, HSV segmentation, color feature extraction, and classification using KNN and ANN with the help of MATLAB. The results show that both methods are able to classify well, where the K-Nearest Neighbor (KNN) method has a higher level of accuracy than the Artificial Neural Network (ANN) on the dataset used. Keywords: Chili Maturity, HSV, K-Nearest Neighbor, Artificial Neural Network, Digital Image Processing.
Keywords: Chili Ripeness, HSV, K-Nearest Neighbor, Artificial Neural Network, Digital Image Processing.