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ROSIDA VIVIN NAHARI
Universitas Trunojoyo
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Social Sciences Transportation Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Ekstrasi Fitur Daun Tembakau Berbasis Discrete Cosine Transform (DCT) Rosida Vivin Nahari; Arda Surya Editya; Riza Alfita
Journal of Applied Informatics and Computing Vol 4 No 1 (2020): Juli 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (831.819 KB) | DOI: 10.30871/jaic.v4i1.1756

Abstract

The success of the tobacco leaf classification process is very dependent on the extraction of tobacco leaf features. Several stages of digital image processing can improve the ability to identify the best quality tobacco automatically through extracting leaf texture features. This study aims to apply the leaf texture feature extraction system using the Discrete Cosine Transform method. Classification results measure the accuracy of the success of the system in extracting the best texture features. The classification of tobacco leaves requires extensive knowledge and complex terminology, even professional graders require significant time in this field for mastery of the subject. This is because tobacco leaves are usually considered to have characteristics that are useful for identification of tobacco quality where the extraction of appropriate features through leaf images can be considered a research problem that plays an important role for classification. The proposed research aims to find a suitable extraction model for obtaining color features through YCbCr color space conversion and tobacco leaf texture obtained from the transformation of the Discrete Cosine Transform frequency space. On classification stage in this research uses the maximum likelihood method. The trial results show an accuracy of success in the classification of tobacco leaves by 90% through the extraction of 12 features.
Co-Authors Achmad Fiqhi Ibadillah Achmad Jauhari, Achmad Achmad Ubaidillah Adi Kurniawan Saputro Aery Rachmad, Aery Arda Surya Editya Aris Jujur Prasetyo Choirudin, Muhamat Clarisna Evita Diana Rahmawati Diana Rahmawati Domas Wisnu Giri, Joseph Robert Hafid Wihangga Haryanto Haryanto Haryanto Haryanto Haryanto Hendra Wahyu Aprilyanto Heri setiawan Heru Cahyono Kartika Kartika Kartika Koko Joni Kunto Aji Wibisono KURIAWAN, ADI KUSUMA, M.KURNIAWAN HADI M. Dikhyak Falakhul Akmal Miftachul Ulum Mirza Pramudia Nur Rohman, Mohammad Izhandi Ifan Rachmat Setiawibawa Riza Alfita Riza Alfita Riza Alfita Riza Alfita Saffak, Mohammad Siti A’isya Sri Herawati Ulum, Miftachul Yundari, Yundari