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All Journal Jurnal Ilmu Komputer JAIS (Journal of Applied Intelligent System) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Sistem Informasi dan Aplikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMPUTIKA - Jurnal Sistem Komputer Progresif: Jurnal Ilmiah Komputer Jurnal Manajemen Informatika dan Sistem Informasi Budimas : Jurnal Pengabdian Masyarakat Abdimasku : Jurnal Pengabdian Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika: Jurnal Pengembangan IT Science Technology and Management Journal (STMJ) Inovasi Sosial: Jurnal Pengabdian Masyarakat
Saraswati, Galuh Wilujeng
Universitas Dian Nuswantoro
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : JAIS (Journal of Applied Intelligent System)

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Classification of Arabica Coffee Green Beans Using Digital Image Processing Using the K-Nearest Neighbor Method Nurun Najmi Amanina; Galuh Wilujeng Saraswati
Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i2.6449

Abstract

Arabica coffee is the largest commodity produced by farmers in Pagergunung Village, Bulu District, Temanggung Regency. Coffee production in recent years has increased rapidly by 80% with the increasing lifestyle of the Indonesian people marked by the number of coffee shop buildings in various regions, and of course the demand for Arabica coffee has also increased, therefore it must improve the quality or quality of the coffee itself. However, in determining and classifying the quality of coffee beans, errors often occur due to the lack of understanding of the farmers in processing coffee. Based on this, the purpose of this research is to classify using the K- Nearest Neighbor method and feature extraction using the average value of Red-Green-Blue (RGB) color in determining the quality and quality of coffee beans according to grade so that they can get a high selling price. In this study using as many as 150 training image data and 150 testing image data, the results of this classification accuracy are 80% using k=1.
Co-Authors Achmad Naila Muna Ramadhani Adhitya Nugraha Aditya Wahyu Ramadhan Adji, Dian Restu Ahmad Zainul Fanani Ajib Susanto Akbar Dwi Syahputra Angga Apriano Hermawan Azhara Devi Sandi Azzahra, Tarissa Aura Bagas Aditya Mahendra Danny Oka Ratmana Danny Oka Ratmana Dianna Yanuaresta Dwi Puji Prabowo, Dwi Puji Erba Lutfina Etika Kartikadarma Fafaza, Safira Alya Fakhrurrozi Fakhrurrozi Febrianti, Ervina Febrianto, Nanang Filmada Ocky Saputra Filmada Ocky Saputra Guruh Fajar Shidik Gustina Alfa Trisnapradika Handoyo, Dhiky Resandi Wur Heru Agus Santoso Lakui Johary Lutfina, Erba Malik Aziz Ali Mardiantara, Naya Alifiah az Azar Putri Megantara, Rama Aria Meilani Dwi Permatasari Miranti Alysha Zulia Larasati Muhamad Ni'am Syukri Roni Asmi Muhammad Syaifur Rohman Muhammad Syaifur Rohman Muljono, - Mulyanto, Edy Nur Inayati Nurun Najmi Amanina Pergiwati, Dewi Prashanti, Eva Pulung Nurtantio Andono Rahmat Trinanda Pramudya Amar Rama Tri Agung Ramadhan, Aditya Wahyu Ramadhani, Irfan Wahyu Ratmana, Danny Oka Renjiro Azhar Pramono Ricardus Anggi Pramunendar Rino Agung Rizky Syah Gumelar Rohman, Muhammad Syaifur Rohman, Muhammad Syaifur Saputra, Filmada Ocky Sri Winarsih, Nurul Anisa Wildan Mahmud Winarsih, Nurul Anisa Sri Yustiqomah, Evita Citra