Melola Discovery

← Melola Discovery

Akses terbuka Jurnal SINTA 1

Data-driven analysis of growth factors in oyster mushroom cultivation: a case study from Indonesia’s market

IAES International Journal of Artificial Intelligence · Vol. 15 No. 4 (2026) · hlm. 3568–3580

DOI
10.11591/ijai.v15.i4.pp3568-3580
ISSN
2252-8938, 2089-4872
Terbit
2026-08-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The oyster mushroom is one of the potential agricultural products that can be developed as an alternative to other agricultural products, to maintain Indonesia's economic condition. However, the production of oyster mushrooms remains low and falls short of the minimum amount of market demand. This study employs a machine learning (ML)–based approach to identify the key parameters influencing oyster mushroom production rates. Recursive feature elimination (RFE) was applied to reduce the initial 19 features to nine, enabling faster processing while maintaining high predictive accuracy. The results showed that agricultural features showed a high contribution rather than environmental, economic, and demographic features. Furthermore, these parameters were related to the train-test analysis to visualize the statistical analysis shown by the best method, adaptive boosting (AdaBoost), with coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE) values of 0.997575, 0.009841, and 0.085884, respectively. Related research relevant to the research findings was analyzed to validate that agricultural product features affect the decline of oyster mushroom production. Other supported research conducted by integrating real-time analysis and twin digital models, which can enhance substrate quality.

Kata kunci

Jaringan kolaborasi para penulis

35 penulis · 140 tautan kepenulisan bersama · 1 lintas organisasi

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • Yogyakarta State University
  • Indonesia Open University
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Dian NurdianaIndonesia Open University3
Siti Hadijah HasanahIndonesia Open University2
Heri RetnawatiYogyakarta State University2
Firdaus Amruzain Satiranandi WibowoYogyakarta State University2
Muhammad Lintang Damar SaktiYogyakarta State University2
Angella Ananta BatubaraYogyakarta State University2
Miftah Okta BerlianYogyakarta State University2
Zulfa Safina IbrahimYogyakarta State University2
Arya YusufAfiliasi tidak diketahui2
Siswantoyo SiswantoyoAfiliasi tidak diketahui2
Ria DymyatiAfiliasi tidak diketahui2
Putu SudiraAfiliasi tidak diketahui2
Yoga SyahriaAfiliasi tidak diketahui2
Dwi Astuti AprijaniAfiliasi tidak diketahui1
Fitria AmastiniAfiliasi tidak diketahui1
Moh. Rizky Putra Aji UtamaAfiliasi tidak diketahui1
Sumaryanto SumaryantoYogyakarta State University1
Jailani JailaniYogyakarta State University1
Lantip Diat PrasojoYogyakarta State University1
Jailani JailaniYogyakarta State University1
Kana HidayatiYogyakarta State University1
Nur Hadi WaryantoYogyakarta State University1
Madiha Dzakiyyah ChairunnisaAfiliasi tidak diketahui1
Muhammad Rif'anAfiliasi tidak diketahui1
Avelyn Pingkan KomunaAfiliasi tidak diketahui1
Rinasa Agistya AnugrahAfiliasi tidak diketahui1
SudarjaAfiliasi tidak diketahui1
Muhammad Yusri IlyasAfiliasi tidak diketahui1

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) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Data-driven analysis of growth factors in oyster mushroom cultivation: a case study from Indonesia’s market</dc:title>
	<dc:creator>Budiman, Yosef</dc:creator>
	<dc:creator>Prasetyo, Gilang Adi</dc:creator>
	<dc:creator>Khoirunnisa’, Asma’</dc:creator>
	<dc:creator>Shalihah, Hanifah Mar’atush</dc:creator>
	<dc:creator>Maulana, Muhamad Riyan</dc:creator>
	<dc:creator>Fadlullah, Yanuar Agung</dc:creator>
	<dc:creator>Sugiri, Sugiri</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Agricultural production; Growth factors; Machine learning; Oyster mushroom; Recursive feature elimination</dc:subject>
	<dc:description xml:lang="en-US">The oyster mushroom is one of the potential agricultural products that can be developed as an alternative to other agricultural products, to maintain Indonesia's economic condition. However, the production of oyster mushrooms remains low and falls short of the minimum amount of market demand. This study employs a machine learning (ML)–based approach to identify the key parameters influencing oyster mushroom production rates. Recursive feature elimination (RFE) was applied to reduce the initial 19 features to nine, enabling faster processing while maintaining high predictive accuracy. The results showed that agricultural features showed a high contribution rather than environmental, economic, and demographic features. Furthermore, these parameters were related to the train-test analysis to visualize the statistical analysis shown by the best method, adaptive boosting (AdaBoost), with coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE) values of 0.997575, 0.009841, and 0.085884, respectively. Related research relevant to the research findings was analyzed to validate that agricultural product features affect the decline of oyster mushroom production. Other supported research conducted by integrating real-time analysis and twin digital models, which can enhance substrate quality.</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>2026-08-01</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/29716</dc:identifier>
	<dc:identifier>10.11591/ijai.v15.i4.pp3568-3580</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 4: August 2026; 3568-3580</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v15.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/29716/15322</dc:relation>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/downloadSuppFile/29716/4591</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Yosef Budiman, Gilang Adi Prasetyo, Asma’ Khoirunnisa’, Hanifah Mar’atush Shalihah, Muhamad Riyan Maulana, Yanuar Agung Fadlullah, Sugiri Sugiri</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>