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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Buana Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesian Journal of Artificial Intelligence and Data Mining SENTIA 2016 Journal of Intelligent Decision Support System (IDSS) Idealis : Indonesia Journal Information System GUYUB: Journal of Community Engagement Journal of Electrical Engineering and Computer (JEECOM) Journal of Technology and Informatics (JoTI) Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Internet of Things and Artificial Intelligence Journal Jurnal Ilmiah Teknik Informatika dan Komunikasi COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Abdimas Terapan: Jurnal Pengabdian Kepada Masyarakat Terapan JOKI: Journal of Computing and Informatics
Ratri Enggar Pawening
Universitas Nurul Jadid
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Transportation Other

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Journal : Journal of Intelligent Decision Support System (IDSS)

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Classification of Corn Seed and Cob Quality Based on Texture and RGB Color Features Using Backpropagation Method Pawening, Ratri Enggar; Zaskiya, Karina Desy; Hasanah, Syarifatul
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 4 (2024): December: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i4.282

Abstract

The quality of agricultural products, such as corn, is greatly influenced by various factors, both environmental factors and agricultural engineering factors. This post-harvest quality affects product performance and is in line with consumer satisfaction, so it will greatly affect its selling price. Manual corn quality grouping requires a lot of time and effort. This study aims to develop a method for classifying corn kernel and cob quality based on digital image processing, using RGB color and texture features. The dataset consists of 150 corn kernel images divided into two quality categories, namely "good" and "bad". The research process involves preprocessing stages, color feature extraction using RGB color space, and texture features using the Gray Level Co-occurrence Matrix (GLCM) method. The classification model is built using the Backpropagation artificial neural network algorithm. The test results show that this method is able to achieve classification accuracy of up to 75%. The implementation of this method is expected to increase the efficiency of the corn quality selection process, reduce dependence on manual assessment, and provide significant benefits to the agricultural sector, especially farmers and the corn industry. These findings provide an important contribution to the development of digital-based post-harvest technology in Indonesia.
Co-Authors Abu Tholib Agmelita Dwi Jianika Agus Zainal Arifin Agus Zainal Arifin Anny Yuniarti Arum Puji Rahayu Ayu Basirotul Muzayyanah Darlis Herumurti Deddy Junaedi Fadillah Nurul Imami Faiqatul Hikmah Fajaratul Lailiah Fakhrina Kamila Fatimatus Zahro Fiqih Humaidah Fitriyah Hidayati Gufairoh, Siti Gufairoh Gulpi Qorik Oktagalu Pratamasunu Hasanah, Syarifatul Honainah Honainah Kusdatul Komariyah Kusuma, Selvia Ferdiana Meliana Eka Puteri Miftahul Hasanah Moh Furqan Mohammad Furqan Muthmainnah, Atik Muzayyanah, Ayu Basirotul Nadhiroh, Anis Yusrotun Nurul Kamilatul Mahmuda Nurul Lihayati Nurul Wasilatul Laili Oktagalu Pratamasunu, Gulpi Qorik Putri Mar’atu Sholehah Raudhah Lailatul Mufidah Rizqa Raaiqa Bintana Rofiatul Hasanah Rohman Dijaya Shudiq, Wali Ja'far Sofi Magfiroh Sya'roni, Wahab Tio Darmawan Ummi Kholifah wahyuni wahyuni Wulandari, Uky Novita Zainal Arifin Zaskiya, Karina Desy