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Real-time object detection to classify export quality of mangosteen using variants of you only look once version 8

IAES International Journal of Artificial Intelligence · Vol. 15 No. 1 (2026) · hlm. 116–128

DOI
10.11591/ijai.v15.i1.pp116-128
ISSN
2252-8938, 2089-4872
Terbit
2026-02-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Mangosteen is one of the leading export commodities from Indonesia. Despite its great economic potential, only about 25% of Indonesian mangosteens meet export standards, mainly due to visual defects such as yellow sap and spots on the skin of the fruit. The process of sorting export worthy mangosteens has been done manually, which tends to be time consuming and inconsistent. Therefore, this study aims to utilize artificial intelligence technology in building a real-time image recognition model to improve the efficiency and accuracy of the export-quality mangosteen sorting process. This study uses you only look once version 8 (YOLOv8) as an image recognition model with YOLOv8 variants, including nano, small, medium, large, and extra large variants. The results of the study using 4,014 primary and 255 secondary data of mangosteen, the highest performance is reached by YOLOv8 medium 82% of accuracy, 0.856 of mean average precision (mAP)50, and 0.616 of mAP50-95. This result is obtained from 70% training, 20% validation, and 10% testing data with epoch stop 85. These results indicate that the model can provide good performance in mangosteen export quality classification. This research contributes to the fields of agricultural technology and artificial intelligence by offering an innovative solution to a practical problem, enhancing efficiency, accuracy, and scalability in export-quality mangosteen sorting.

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17 penulis · 43 tautan kepenulisan bersama · 16 lintas organisasi

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  • Sunan Gunung Djati State Islamic University Bandung
  • University of Mataram
  • Universiti Teknikal Malaysia Melaka
  • Organisasi berbeda
  • Organisasi sama
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Rekan penulisOrganisasiArtikel bersama
Muhammad Ali RamdhaniSunan Gunung Djati State Islamic University Bandung2
Tedi PriatnaSunan Gunung Djati State Islamic University Bandung1
Hamdan SugilarSunan Gunung Djati State Islamic University Bandung1
Fauziah Binti KasminUniversiti Teknikal Malaysia Melaka1
Yogan Jaya KumarUniversiti Teknikal Malaysia Melaka1
Soni SetiadjiSunan Gunung Djati State Islamic University Bandung1
Frida Aziz DamayantiSunan Gunung Djati State Islamic University Bandung1
Fiona Putri SeptianiSunan Gunung Djati State Islamic University Bandung1
Tina Dewi RosahdiSunan Gunung Djati State Islamic University Bandung1
Rohmat MulyanaSunan Gunung Djati State Islamic University Bandung1

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">Real-time object detection to classify export quality of mangosteen using variants of you only look once version 8</dc:title>
	<dc:creator>Maylawati, Dian Sa'adillah</dc:creator>
	<dc:creator>Fuadi, Mi’raj</dc:creator>
	<dc:creator>Yniarto, Kurniawan</dc:creator>
	<dc:creator>AP, Yuhendra</dc:creator>
	<dc:creator>Nugraha, Rizky Rahmat</dc:creator>
	<dc:creator>Harahap, Akbar Hidayatullah</dc:creator>
	<dc:creator>Wahana, Agung</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Deep learning; Export quality; Mangosteen; Object detection; You only look once</dc:subject>
	<dc:description xml:lang="en-US">Mangosteen is one of the leading export commodities from Indonesia. Despite its great economic potential, only about 25% of Indonesian mangosteens meet export standards, mainly due to visual defects such as yellow sap and spots on the skin of the fruit. The process of sorting export worthy mangosteens has been done manually, which tends to be time consuming and inconsistent. Therefore, this study aims to utilize artificial intelligence technology in building a real-time image recognition model to improve the efficiency and accuracy of the export-quality mangosteen sorting process. This study uses you only look once version 8 (YOLOv8) as an image recognition model with YOLOv8 variants, including nano, small, medium, large, and extra large variants. The results of the study using 4,014 primary and 255 secondary data of mangosteen, the highest performance is reached by YOLOv8 medium 82% of accuracy, 0.856 of mean average precision (mAP)50, and 0.616 of mAP50-95. This result is obtained from 70% training, 20% validation, and 10% testing data with epoch stop 85. These results indicate that the model can provide good performance in mangosteen export quality classification. This research contributes to the fields of agricultural technology and artificial intelligence by offering an innovative solution to a practical problem, enhancing efficiency, accuracy, and scalability in export-quality mangosteen sorting.</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>2026-02-01</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/27710</dc:identifier>
	<dc:identifier>10.11591/ijai.v15.i1.pp116-128</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 1: February 2026; 116-128</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v15.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/27710/14921</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Dian Sa’adillah Maylawati, Mi’raj Fuadi, Kurniawan Yuniarto, Yuhendra AP,   Rizky Rahmat Nugraha, Akbar Hidayatullah Harahap, Agung Wahana</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>