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All Journal e-Journal Pustaka Kesehatan ASAS : Jurnal Hukum Ekonomi Syariah Journal of Agromedicine and Medical Sciences Indonesian Journal on Software Engineering (IJSE) Jurnal Pilar Nusa Mandiri Publica: Jurnal Pemikiran Administrasi Negara Journal of Computer Networks, Architecture and High Performance Computing Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Salam (Islamic Economics Journal) Ilmu Komputer untuk Masyarakat Transformasi: Journal of Economics and Business Management Ta'allum: Jurnal Pendidikan Islam
Muhamad Hasan
Laboratorium Anatomi Fakultas Kedokteran Universitas Jember
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Classification of Banana Ripeness Based on Color and Texture Characteristics Ahmad Hafidzul Kahfi; Hasan, Muhamad; Riyan Latifahul Hasanah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i1.1985

Abstract

Banana is one of the most consumed fruits globally and is a rich source of vitamins, minerals and carbohydrates. With the many benefits that bananas have, many farmers cultivate this fruit. The problem that occurs when the harvest is produced on a large scale is the process of selecting bananas that are still unripe or ripe. Usually farmers carry out the selection process manually by visually identifying ripeness based on the color of the fruit skin. However, direct observation has several drawbacks such as subjectivity, takes a long time and is inaccurate. For this reason, we need a system that can help determine the maturity level of bananas automatically through a series of banana image processing processes. One way that can be used to determine the maturity level of bananas is by looking at the color and texture of the bananas. This study aims to classify the maturity level of bananas based on the color and texture characteristics of the banana image using the Gray Level Co-occurrence Matrix and K-Nearest Neighbor methods for the classification process. Based on the results of the research analysis that has been carried out, using the parameter k which has a value of 3 obtains very high accuracy.
Co-Authors Ahmad Hafidzul Kahfi Anna Mukhayaroh, Anna Bintang Balele Cholis Abrori, Cholis Cicih Komariah Darodjat, Darodjat Dedy Chandra Hariyono Dedy Chandra Hariyono, Dedy Chandra Desie Dwi Wisudanti Djaya Mario Eri Surya Alfonsus Dwita Aryadina Rachmawati Dwiza Riana Erfan Efendi Erina Pane Fahrurrozi4, Nabil Ali Fauzi, Muhammad Yasir Hasbi Maulana Arsyad Heni Noviarita Indah Amin Sugiharti Jajang Jaya Purnama Kahfi, Ahmad Hafidzul Mattarima Mattarima Mayangky, Nissa Almira Nita Merlina, Nita Panjaitan, Yogi Yosua Putra, Ananta Kusuma Riyan Latifahul Hasanah Riyan Latifahul Hasanah Rony Prasetyo Rony Prasetyo, Rony Sarah Kinan Andalusia Simanjuntak, Harapan Tua Ricky Fredy Siti Fauziah SRI RAHAYU Sriyanto Sriyanto Yani, Achmad Yedi Murdanu