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All Journal REKA RACANA EMITTER International Journal of Engineering Technology Al-Wijdan : Journal of Islamic Education Studies Network Engineering Research Operation [NERO] Jurnal Pertanian Agros Sriwijaya Journal of Agribusiness and Biometrics in Agriculture Research (JSM) Jurnal Seni Musik
Aji Setiawan
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences Other

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Journal : EMITTER International Journal of Engineering Technology

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Optimization of Gray Level Co-occurrence Matrix (GLCM) Texture Feature Parameters in Determining Rice Seed Quality Aji Setiawan; Arif Budiman, Adam
EMITTER International Journal of Engineering Technology Vol 13 No 1 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i1.928

Abstract

Rice seed quality assessment is a critical measure in promoting agricultural productivity, as high-quality seeds directly influence crop yield and resilience. One of method for evaluating seed quality is texture analysis, which leverages the Gray Level Co-occurrence Matrix (GLCM) to extract meaningful features from seed images, providing insights into their condition and potential performance. This research aims to determine the optimal performance of GLCM parameters in identifying the texture characteristics of rice seed quality. The experiments were conducted using four angles (0°, 45°, 90°, and 135°) and three-pixel distances (1, 2, and 3), evaluating features such as homogeneity, contrast, dissimilarity, and energy. The results indicate that certain parameter configurations significantly affect the discriminative power of the extracted features, with the Support Vector Machine (SVM) classifier achieving the highest performance at a pixel distance of 1, with an accuracy of 0.73, precision of 0.79, recall of 0.73, and F1-score of 0.72. These findings demonstrate that optimizing GLCM parameter settings directly contributes to improved classification performance, highlighting the method's potential for enhancing rice seed quality assessment.
Co-Authors Arif Budiman, Adam Dindy Darmawati Putri Fachrul Madrapriya Irfanda Rizki Harmono Sejati Swandari, Tantri Timor Setiyaningsih Triwibowo Triwibowo Yati Muliati Yohana Theresia Maria Astuti Zulkifli, Lutfi