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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Justek : Jurnal Sains Dan Teknologi Jurnal Saintek Lahan Kering Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi Progresif: Jurnal Ilmiah Komputer Zonasi: Jurnal Sistem Informasi JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) BERNAS: Jurnal Pengabdian Kepada Masyarakat Bakti Cendana: Jurnal Pengabdian Masyarakat Aiti: Jurnal Teknologi Informasi Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Generic Jurnal Restikom : Riset Teknik Informatika dan Komputer Journal Social Science And Technology For Community Service Journal Of Information And Technology Unimor (JITU) Jurnal Sains Teknologi dan Sistem Informasi JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) Adopsi Teknologi dan Sistem Informasi SisInfo : Jurnal Sistem Informasi dan Informatika Majalah Ilmiah METHODA Prosiding Seminar Riset Mahasiswa Journal of Informatics and Computing
Tey Seran, Krisantus Jumarto
Program Studi Teknologi Informasi, Universitas Timor
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Journal : Aiti: Jurnal Teknologi Informasi

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Identifikasi penyakit pada foliage tanaman cendana menggunakan algoritma ID3 berdasarkan fitur GLCM dan Color Moment Seran, Krisantus Jumarto Tey; Baso, Budiman
AITI Vol 22 No 1 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i1.73-83

Abstract

The main cause of the decreased economic value of sandalwood trees is disease. There are several ways to detect (identify) diseases in sandalwood trees, one of which is through the leaves. One way to identify the disease is by observing the color and shape of the leaves affected by the disease using image processing or computer vision. In this study, the GLCM feature is combined with the Color Moment feature to analyze the disease of the Sandalwood leaves, especially the mean value of each RGB in the Sandalwood leaf image. The ID3 algorithm is chosen as a learning method to analyze texture and color extraction data to determine the type of disease. The data testing results showed an accuracy level of 92.31% when the GLCM feature is combined with the Color Moment feature. It indicates that the combination of these features provides good results in detecting and classifying the type of disease in Sandalwood leaves.
Co-Authors Abi, Maria Oktavian Achmad, Fariz Amsikan, Dionisius K. Anastasia Kadek Dety Lestari Bana, Yoseph Arianto Banusu, Lusitania Baso, Budiman Blegur, Willem Amu Chrisinta, Debora Da Conceicao, Fedelia Da Dionisius K. Amsikan Fallo, Kristoforus Fetronela Rambu Bobu Fried Markus Allung Blegur Gelu, Leonard Peter Isto, Kristoforus Boik Tani'i Knaofmone, Maria Letitia Kristoforus Fallo Lake, Delviana Mammi Sarlota Bana Margaretha Baria Maria Grestiana Sani Maria Ilona Silab Marjeni Liana Nino Martins, Alfiana Fontes Matute, Alejandro Jr. V. Mau, Lidia Marselina Mirja Hoar Moensaku, Emanuel Nababan, Darsono Nahak, Agustinus Yasvin Nahak, Maria Selviyanti H Nahak, Roswita R.K. Natalia Karmila Ketmoen nggadas, carles Nurliah Apryanti Oktavianus Klau Lekik Patricia Gertrudis Manek Risald Sae, Ina Rai Salu, Yoana Fransiska De Cantal Sesfao, Yusmiati Farida severinus ponis Spanyoli Muriano Fuel Stefanus Lau Manek Taitoh, Rosalinda Hoar Ullu, Hevi Herlina Usnaat, Sonya Oktavia Victoria Nova Naiheli Willem Amu Blegur Willem Amu Blegur Yasinta O.L Rema Yoseph Pius Kurniawan Kelen Yulsiani Sanam