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Image analysis for classifying coffee bean quality using a multi-feature and machine learning approach

IAES International Journal of Artificial Intelligence · Vol. 13 No. 4 (2024) · hlm. 4241–4248

DOI
10.11591/ijai.v13.i4.pp4241-4248
ISSN
2252-8938, 2089-4872
Terbit
2024-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Price and customer satisfaction depend on coffee bean quality. The coffee industry must analyze coffee bean quality. Global demand for robusta coffee is high. Coffee industry professionals mostly understand coffee bean quality. Thus, an image analysis using a computer vision-based approach for classifying robusta coffee bean quality is required. Image acquisition, region of interest (ROI) detection, pre-processing, segmentation, feature extraction, feature selection, and classification are covered in this study. A multi-feature derived based on color, shape, and texture features was employed in feature extraction, followed by feature selection using principal component analysis (PCA). Several machine-learning methods classified the coffee beans. The method performance was assessed using precision, recall, and accuracy. The selected features using the backpropagation neural network (BPNN) classifier outperformed others with 98.54% accuracy.

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  • Mulawarman University
  • Universitas Teknologi Yogyakarta
  • Universitas Negeri Padang
  • STMIK Banjarbaru
  • Sekolah Tinggi Ilmu Kesehatan Harapan Ibu Jambi
  • Universitas Jambi
  • Politeknik Negeri Samarinda
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Rekan penulisOrganisasiArtikel bersama
Didit SuprihantoMulawarman University6
Budi RahmaniSTMIK Banjarbaru5
Heru IsmantoMusamus Merauke University5
Novianti PuspitasariMulawarman University5
Dyna Marisa KhairinaMulawarman University4
Agus Qomaruddin MunirUniversitas Respati Yogyakarta4
Andri SyafriantoSTMIK El Rahma Yogyakarta4
Ummul HairahMulawarman University4
Andi TejawatiMulawarman University3
Hamdani HamdaniMulawarman University2
Masna WatiMulawarman University2
Nur Maya SalsabilaMulawarman University2
Dita NurmadewiBakrie University2
Viny Christanti MawardiTarumanagara University2
Heliza Rahmania HattaMulawarman University2
Henderi HenderiUniversitas Raharja2
Enny Itje SelaUniversitas Teknologi Yogyakarta1
Saucha DiwandariUniversitas Teknologi Yogyakarta1
Briyan Efflin SyahputraUniversitas Teknologi Yogyakarta1
Nathaniela Aptanta ParamaUniversitas Teknologi Yogyakarta1
Hugo ApriliantoSTMIK Banjarbaru1
Silvia MeilinaSekolah Tinggi Ilmu Kesehatan Harapan Ibu Jambi1
Melda YenniSekolah Tinggi Ilmu Kesehatan Harapan Ibu Jambi1
Khairinal KhairinalUniversitas Jambi1
Emosda EmosdaUniversitas Jambi1
Vina Zahrotun KamilaMulawarman University1
Novianti PuspitasariMulawarman University1
Amin Padmo Azam MasaMulawarman University1
Theresia Amelia PawitraMulawarman University1
Farida Djumiati SitaniaMulawarman University1
Hastuti HastutiUniversitas Halu Oleo1
Muhammad AnwarUniversitas Negeri Padang1
Ali Basrah PulunganUniversitas Negeri Padang1
Zhafranul NafisUniversitas Negeri Padang1
Dwiprima Elvanny MyoriUniversitas Negeri Padang1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Image analysis for classifying coffee bean quality using a multi-feature and machine learning approach</dc:title>
	<dc:creator>Septiarini, Anindita</dc:creator>
	<dc:creator>Hamdani, Hamdani</dc:creator>
	<dc:creator>Ery Burhandeny, Aji</dc:creator>
	<dc:creator>Nurcahyono, Damar</dc:creator>
	<dc:creator>Eka Priyatna, Surya</dc:creator>
	<dc:subject xml:lang="en-US">Computer vision, Pattern recognition</dc:subject>
	<dc:subject xml:lang="en-US">Coffee beans; Features selection; K-means; Machine learning; Principal component analysis</dc:subject>
	<dc:description xml:lang="en-US">Price and customer satisfaction depend on coffee bean quality. The coffee industry must analyze coffee bean quality. Global demand for robusta coffee is high. Coffee industry professionals mostly understand coffee bean quality. Thus, an image analysis using a computer vision-based approach for classifying robusta coffee bean quality is required. Image acquisition, region of interest (ROI) detection, pre-processing, segmentation, feature extraction, feature selection, and classification are covered in this study. A multi-feature derived based on color, shape, and texture features was employed in feature extraction, followed by feature selection using principal component analysis (PCA). Several machine-learning methods classified the coffee beans. The method performance was assessed using precision, recall, and accuracy. The selected features using the backpropagation neural network (BPNN) classifier outperformed others with 98.54% accuracy.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24697</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i4.pp4241-4248</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 4: December 2024; 4241-4248</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/24697/14234</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>