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All Journal REKA RACANA EMITTER International Journal of Engineering Technology JIEET (Journal of Information Engineering and Educational Technology) Network Engineering Research Operation [NERO] Jurnal Pertanian Agros Juminten : Jurnal Manajemen Industri dan Teknologi Sriwijaya Journal of Agribusiness and Biometrics in Agriculture Research Jurnal Ilmiah Ekonomi dan Manajemen (JSM) Jurnal Seni Musik Journal of Law and Security Studies
Aji Setiawan
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Agriculture, Biological Sciences & Forestry Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology 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.
Implementation Fuzzy C-Means on Decision Support System BPNT (Bantuan Pangan Non-Tunai) Ministry of Social Affairs Indonesia Aji Setiawan; Akbar, Jordan Nur
EMITTER International Journal of Engineering Technology Vol 7 No 2 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v7i2.444

Abstract

Decision Support System can be an alternative solution to determine the candidate's decision. Bantuan Pangan Non-Tunai (BPNT) are selected based on criteria determined by the Ministry of Social Affairs of the Republic of Indonesia. BPNT recipients are conducted by the government to help someone who is less able to meet their daily needs. The occurrence of errors in determining the eligibility of prospective beneficiaries is a major problem, based on these problems there needs to be an information system that can provide a valid BPNT recommendation and one of which uses a grouping method with the Fuzzy C-Means (FCM) algorithm. System development using the waterfall method. The results of system implementation and testing showed that 90% of the system was following what was expected according to the results of the test with the system being built.
Co-Authors Adam Arif Budiman Akbar, Jordan Nur Alvin Adam Anton Suryadarma Dindy Darmawati Putri Fachrul Madrapriya Farida Pulansari Hazbie Alfarhizi Syahwadana Irfanda Rizki Harmono Sejati Muhammad Athallah Rafi Prans Sintia Diadani S Ritonga Ari Putri Sumiati Swandari, Tantri Timor Setiyaningsih Triwibowo Triwibowo Yati Muliati Yohana Theresia Maria Astuti Zulkifli, Lutfi