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All Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Afriadi, A
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Computer Science & IT Control & Systems Engineering

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Identifikasi Varietas Anggur Secara Otomatis Menggunakan Segmentasi Gambar Berbasis Warna dan Analisis Tekstur: Pendekatan K-Means Clustering Afriadi, A; Harnaranda, Jefri; Ramadhanu, Agung
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.518

Abstract

In this study, we propose an automated system for identifying grape varieties (red and green) using color-based image segmentation and texture analysis. The system employs K-Means Clustering for color segmentation in the Lab* color space, followed by Gray-Level Co-occurrence Matrix (GLCM) texture feature extraction to differentiate grape types. The experimental results show that the proposed method achieved an accuracy of over 90% in identifying grape varieties, demonstrating its potential for industrial applications in fruit processing. Our findings indicate that the system is robust under various lighting conditions and can significantly reduce human error in grape sorting processes. Automated Identification of Grape Varieties Using Color-Based Image Segmentation and Texture Analysis: A K-Means Clustering Approach
Co-Authors Harnaranda, Jefri Ramadhanu, Agung