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Entin Monika
Universitas Muhammadiyah Bengkulu, Bengkulu
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Perbandingan Algoritma K-Means dan Fuzzy C-Means pada Segmentasi Citra Biji Jengkol Deteksi Kematangan Entin Monika; Harry Witriyono
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10389

Abstract

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.
Co-Authors Harry Witriyono