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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sains dan Informatika IKRA-ITH Informatika : Jurnal Komputer dan Informatika ILKOM Jurnal Ilmiah Building of Informatics, Technology and Science Jurnal Mantik JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi
Chusna, Nuke L
Universitas Krisnadwipayana
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Journal : Building of Informatics, Technology and Science

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Classification of Fruits High in Vitamin C Using Self-Organizing Map and the K-Means Clustering Algorithm Nuke L Chusna; Nurhasan Nugroho; Umbar Riyanto; Ahmad Ari Aldino
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4104

Abstract

Vitamin C-rich fruits not only taste fresh and delicious but also have the potential to increase the body's resistance to various diseases and maintain a proper nutritional balance. Information about fruits high in vitamin C is very important in order to increase public knowledge about which fruits contain high levels of vitamin C. However, to classify fruits high in vitamin C based on their image, a model is needed that is able to analyze the characteristics present in the image of the fruit. The purpose of this study is to build a classification model for high-vitamin C fruits with a combination of the Self-Organizing Map (SOM) artificial neural network algorithm and K-Means Clustering. Prior to classification, an image segmentation process is carried out using the K-Means Clustering algorithm, which will separate the image into parts that have similar visual characteristics. After the segmented image, the features of the object are extracted based on shape and texture. After the features of the image have been obtained, proceed with classifying images using the SOM algorithm by mapping multidimensional data into a lower-dimensional spatial representation to obtain the appropriate group or class. The accuracy test results for the built model produce an accuracy value of 93.33% and are included in the good category
Co-Authors Adam Elvandi Yusup Adriyan Priyatma Ahmad Ari Aldino Ali Khumaidi Amat Damuri Andrian Muljadi Avip Kurniawan Avip Kurniawan Fachri, Moch Herry Wahyono Herry Wahyono Herwanto Herwanto Herwanto Herwanto M.Ageng Laksono Mega Wahyu Rhamadani Moch Fachri Muhammad Syamsul Arif Nur Hikmah Nur Hikmah Nurhasan Nugroho Risanto Darmawan Sampoerno, Ahmad RIzqi Uci Dwi Rahayu Umbar Riyanto Wiliani, Ninuk Yonas Prima Arga Rumbyarso