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Rice quality classification system using convolutional neural network and an adaptive neuro-fuzzy inference system

IAES International Journal of Artificial Intelligence · Vol. 13 No. 4 (2024) · hlm. 4113–4120

DOI
10.11591/ijai.v13.i4.pp4113-4120
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

In the food sector, rice processing and classification are essential operations that help maintain strict quality and safety standards, satisfy various consumer preferences, and satisfy particular market demands. Artificial intelligence (AI) and machine learning techniques are used in automated systems to reliably and effectively classify rice quality. This research compares a rice quality classification system using a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS). Both methods are evaluated for their ability to classify rice based on quality, utilizing a dataset encompassing various physical characteristics. The comparative analysis results reveal the strengths and weaknesses of each approach in addressing this classification task. In this research, two classification systems for different varieties of rice-medium and premium—are compared. CNN and ANFIS are the techniques applied. The CNN accuracy on the rice picture is 62.5%. Thus, a contrast enhancement procedure was applied and had better accuracy at 75%. However, when contrasted with the classification made using the ANFIS approach, the ANFIS method continued to yield the best accuracy, 82.25%.

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24 penulis · 75 tautan kepenulisan bersama · 49 lintas organisasi

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  • Sunan Gunung Djati State Islamic University Bandung
  • Universitas Teknokrat Indonesia
  • Telkom University
  • Nusa Putra University
  • Serang Raya University
  • Kanazawa University
  • Politeknik Bina Trada Semarang
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Rekan penulisOrganisasiArtikel bersama
Muchtar Ali Setyo YudonoNusa Putra University1
Jumadi JumadiSunan Gunung Djati State Islamic University Bandung1
Abdul Haris KuspranotoPoliteknik Bina Trada Semarang1
Aryo De Wibowo Muhammad SidikNusa Putra University1
Rina MardiatiSunan Gunung Djati State Islamic University Bandung1
Nanang IsmailSunan Gunung Djati State Islamic University Bandung1
Adam FaroqiSunan Gunung Djati State Islamic University Bandung1
Liberty ChaidirSunan Gunung Djati State Islamic University Bandung1
Nana Rachmana SyambasBandung Institute of Technology1
Galih Nugraha NurkahfiNational Research and Innovation Agency1
Ratna MayasariAfiliasi tidak diketahui1
Syaiful AhdanUniversitas Teknokrat Indonesia1
Adi SuciptoUniversitas Teknokrat Indonesia1
Ridha Muldina NegaraTelkom University1
Ade NurhayatiTelkom University1
Jupriyadi JupriyadiUniversitas Teknokrat Indonesia1
Hasan Nur ArifinTelkom University1
Sumiati SumiatiSerang Raya University1
Agung TriayudiNasional University1
Wahyu Oktri WidyartoSerang Raya University1
Iksal IksalUniversitas Faletehan1

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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">Rice quality classification system using convolutional neural network and an adaptive neuro-fuzzy inference system</dc:title>
	<dc:creator>Kamelia, Lia</dc:creator>
	<dc:creator>Zaki Hamidi, Eki Ahmad</dc:creator>
	<dc:creator>Muhammad Fadilla, Reno</dc:creator>
	<dc:subject xml:lang="en-US">Neural Network; Fuzzy Logic</dc:subject>
	<dc:subject xml:lang="en-US">Adaptive neuro-fuzzy inference system; Classification; Convolutional neural network; Machine learning; Rice quality</dc:subject>
	<dc:description xml:lang="en-US">In the food sector, rice processing and classification are essential operations that help maintain strict quality and safety standards, satisfy various consumer preferences, and satisfy particular market demands. Artificial intelligence (AI) and machine learning techniques are used in automated systems to reliably and effectively classify rice quality. This research compares a rice quality classification system using a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS). Both methods are evaluated for their ability to classify rice based on quality, utilizing a dataset encompassing various physical characteristics. The comparative analysis results reveal the strengths and weaknesses of each approach in addressing this classification task. In this research, two classification systems for different varieties of rice-medium and premium—are compared. CNN and ANFIS are the techniques applied. The CNN accuracy on the rice picture is 62.5%. Thus, a contrast enhancement procedure was applied and had better accuracy at 75%. However, when contrasted with the classification made using the ANFIS approach, the ANFIS method continued to yield the best accuracy, 82.25%.</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>
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	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24904</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i4.pp4113-4120</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 4: December 2024; 4113-4120</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/24904/14220</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>
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