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All Journal Jurnal Informatika Upgris Syntax Literate: Jurnal Ilmiah Indonesia Musamus Journal Of Research Information and Communication Technology REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Intelligent Decision Support System (IDSS) Sustainable: Jurnal Kajian Mutu Pendidikan Nusantara Hasana Journal Jurnal Mandiri IT Brilliance: Research of Artificial Intelligence Jurnal Minfo Polgan (JMP) Journal Of World Science Batara Wisnu : Indonesian Journal of Community Services Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Informatika dan Teknologi Informasi JSITP Jurnal Indonesia Sosial Teknologi Jurnal Publikasi Teknik Informatika (JUPTI) Jurnal Ekonomi, Teknologi dan Bisnis Innovative: Journal Of Social Science Research Jurnal Sains dan Ilmu Terapan JURSIMA Jurnal Akademik Pengabdian Masyarakat Svarga Pena: Jurnal Pengabdian Kepada Masyarakat
Nugroho Adhi Santoso
STMIK YMI Tegal
Agriculture, Biological Sciences & Forestry Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Journal : Journal of Intelligent Decision Support System (IDSS)

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Application of K-NN algorithm using gray level co-occurrence matrix for mango fruit classification cased on leaf image Bangkit Indarmawan Nugroho; Taufiq Aziz; Nugroho Adhi Santoso; Gunawan Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.233

Abstract

Mango is a fruit crop favored by the community, especially the people of Probolinggo. The most widely planted types of mangoes in the Probolinggo area are Saruman is, golek, and manalagi mangoes because they taste good. This study uses mango leaves as a dataset of three types of mangoes: arumanis, golek, and manalagi. Various ways can be done to distinguish mango types, one of which is by looking at the shape and texture of the mango tree leaves. Suppose you look at the data in the field. In that case, the shape and texture of the leaves of Saruman, golek, and manalagi mangoes have many similarities, making it difficult to distinguish with the naked eye. This research aims to classify mango types based on leaf shape and texture using the K-Nearest Neighbor method. The shape feature extraction process uses compactness and circularity methods, while the texture feature extraction process uses energy and contrast from the co-occurrence matrix approach. The classification method used is K-Nearest Neighbor. The test results of shape feature extraction took 0.043 seconds and texture 0.053 seconds
Co-Authors Aang Alim Murtopo Abdul Rochim Ahmad Zulfikri Anik Mutaqin Anik Muttaqin Ardi Supratman Arif Rohman Hakim Bangkit Indarmawan Nugroho Bayu Aji Santoso Bryan Adam Hidayatullah Chalimatus Solikhah Dewi Srifani Fajrul Falah Fikri Zain Hidayatullah Gunawan Gunawan Gunawan Gunawan Gunawan Gunawan Intan Mayla Faiza Isni Azmi Khofifah Indah Hasanah Khofifah Indah Hasanah Kusworo Adi Maezun Nafis Maezun Nafis Marzuqi Marstyo Ardiansyah Moh. Jamaludin Muhammad Syifa Albana Nurul Fadilah Nurul Fadilah Oky Dwi Nurhayati Pingky Septiana Ananda Rifki Dwi Kurniawan Rifki Dwi Kurniawan Sarif Surorejo Slamet Kusworo Suryadi Budiutomo Syefudin Syefudin Syefudin Taufiq Aziz Windi Setiawati Wresti Andriani Zaenul Arif Zaenul Arif Zainul Arif Zakaria Zuldhan Ibnu Pangestu