Perbandingan Algoritma K-Means dan Fuzzy C-Means pada Segmentasi Citra Biji Jengkol Deteksi Kematangan - Entin Monika; Harry Witriyono (TIN: Terapan Informatika Nusantara, 2026)
This study aims to compare the performance of K-Means and Fuzzy C-Means (FCM) algorithms in image segmentation of jengkol seeds (Archidendron pauciflorum) for automatic ripeness detection. The dataset comprises 300 images categorized into three ripeness classes: ripe (100 images), half-ripe (100 images), and unripe (100 images). Images were acquired using a 12 MP smartphone camera at a standardized resolution of 640×480 pixels under controlled lighting at a distance of 20 cm from the object. The research pipeline includes image preprocessing (RGB-to-HSV and LAB/CIELAB color space conversion, median filter noise reduction, and contrast enhancement), K-Means and FCM segmentation, color and texture feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and performance evaluation based on accuracy, Peak Signal-to-Noise Ratio (PSNR), and computational time. Results indicate that FCM achieves 90–93% accuracy and 30–32 dB PSNR, outperforming K-Means (85–88% accuracy, 27–29 dB PSNR). Nevertheless, K-Means excels in computational efficiency (0.45 s vs. 1.20 s). FCM is recommended for high-accuracy applications, whereas K-Means is preferred when computational efficiency is prioritized.