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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Petir Progresif: Jurnal Ilmiah Komputer TIN: TERAPAN INFORMATIKA NUSANTARA Computer Science (CO-SCIENCE) Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Klik - Jurnal Ilmu Komputer Arcitech: Journal of Computer Science and Artificial Intelligence Prioritas : Jurnal Pengabdian Kepada Masyarakat MDP Student Conference Jurnal Sistem Informasi Komputer dan Teknologi Informasi Jurnal Teknik Indonesia (JU-TI) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Daniel Udjulawa
Universitas Multi Data Palembang
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Journal : Progresif: Jurnal Ilmiah Komputer

 1 
Klasifikasi Mutu Biji Kopi Menggunakan Metode CNN-SVM Berdasarkan Cacat Fisik dan Warna Michael Gunawan; Daniel Udjulawa
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3344

Abstract

The determination of coffee bean quality in Indonesia is generally still done manually based on physical defects and color, which is subjective and time-consuming. This study aims to develop a digital image-based green coffee bean quality classification model using the Convolutional Neural Network and Support Vector Machine (CNN-SVM) method. CNN is used as a feature extractor with a ResNet-50 architecture, while SVM functions as a classifier using a Radial Basis Function (RBF) kernel. The dataset consists of 10 classes of coffee bean defects and is divided into 80% training data and 20% test data. The test results show an accuracy value of 77.68%, precision of 80.04%, recall of 77.15%, and f1-score of 77.47%. This approach proves that the combination of CNN and SVM can improve the accuracy and stability of the model. This finding is a novelty in the development of an efficient and objective artificial intelligence-based automatic coffee quality sorting system.Keywords: Coffee Bean Classification; CNN-SVM; ResNet-50 AbstrakPenentuan mutu biji kopi di Indonesia umumnya masih dilakukan secara manual berdasarkan cacat fisik dan warna, yang bersifat subjektif dan memerlukan waktu lama. Penelitian ini bertujuan untuk mengembangkan model klasifikasi mutu biji kopi hijau berbasis citra digital menggunakan metode Convolutional Neural Network dan Support Vector Machine (CNN-SVM). CNN digunakan untuk ekstraksi fitur dengan arsitektur ResNet-50, sedangkan SVM berfungsi untuk klasifikasi menggunakan kernel Radial Basis Function (RBF). Dataset terdiri dari 10 kelas cacat biji kopi dan dibagi menjadi 80% data latih serta 20% data uji. Hasil pengujian menunjukkan nilai akurasi sebesar 77,68%, presisi 80,04%, recall 77,15%, dan f1-score 77,47%. Pendekatan ini membuktikan bahwa kombinasi CNN dan SVM mampu meningkatkan akurasi dan stabilitas model. Temuan ini menjadi kebaruan dalam pengembangan sistem sortasi mutu kopi otomatis yang efisien dan objektif berbasis kecerdasan buatan.Kata kunci: Klasifikasi Biji Kopi; CNN-SVM; ResNet-50
Co-Authors Abdul Rahman Andreyas, Andreyas Anggoro Aryo P. Dafid Dafid Dafid Dafid Dandy, Dandy Darmawan, Dean Famerdi, Farhan Agung Fatchurrachman, Ahmad Fawzan, Muhammad Febbiola Febbiola Febbiola, Febbiola Ja'far, Ja'far Kadafi, Aldo Kasanova, Sinyo Krisvyanti, Fanny Kurniawan, Marthin Hokita Lie, Jeason M Fajri Septian Dwi Cahyo Martin, Nicolas Marzali, Azizurrahman Michael Gunawan Muhammad Haviz Irfani Mulyati Mulyati Mulyati Mulyati Novan Wijaya Nyimas Artina Oktarina, Serly Pramudiya, Relin Ramadhan, Ahmad Wildan Rizky Regiza Giovanno Rivaldo, Mario Robert, Michael Roda Robert, Victoria Valensita Setiawan, Jerry Sewithto, Muhyi Haadi Shela, Shela Simanjuntak, Andreas Jeremy Obet Sutanto, Steven Yesua Timoteus Ivan Sariyo William William Wiwik Handayani Yoannita, Yoannita Yohannes, Yohannes Zulnardi, Andre