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All Journal Biologi Edukasi : Jurnal Ilmiah Pendidikan Biologi BIOTIK: Jurnal Ilmiah Biologi Teknologi dan Kependidikan OPERATION EXCELLENCE: Journal of Applied Industrial Engineering JAS-PT Jurnal Analisis Sistem Pendidikan Tinggi JURNAL EKSAKTA PENDIDIKAN (JEP) Jurnal Pembelajaran Biologi : Kajian Biologi dan Pembelajarannya Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Sains dan Edukasi Sains Jurnal Jeumpa Jurnal Industri dan Inovasi (INVASI) JIPI (Jurnal IPA dan Pembelajaran IPA)
Fajar Okta Widarta
Universitas Teuku Umar
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Physics Public Health Transportation Other

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Journal : OPERATION EXCELLENCE: Journal of Applied Industrial Engineering

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The application of machine learning algorithms for assessing the maturity level of palm fruits as the prominent commodity in the Western-Southern Area of Aceh Syahputra, Rizki Agam; Widarta, Fajar Okta
Operations Excellence: Journal of Applied Industrial Engineering Vol. 16, No. 1, (2024): OE March 2024
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/oe.2024.v16.i1.102

Abstract

The potential of palm oil plantations in Aceh is substantial, with the province ranking eighth in Indonesia for palm oil cultivation. Aceh boasts a vast oil palm plantation area of 470.8 thousand hectares, comprising 44% of Aceh's total plantation land. Palm fruit quality directly impacts palm oil production, emphasizing the need for consistent maturity levels. To address this, computer algorithms, especially machine learning, have been applied. This study introduces the Self-Organizing Map (SOM) Algorithm for palm fruit maturity determination. SOM's reliability in capturing dataset topology offers a diverse classification process, revolutionizing palm fruit maturity detection and optimizing palm oil production. This study uses 40 dataset consisted of 20 mature and 20 unmature palm fruit image as the basis data which then converted into RGB and HSV value with Matlab engine. The result of the study indicates that the SOM algorithm is capable of classifying the maturity detection with 100% precision result. The SOM algorithm is synthesized in a Graphical User Interface that is capable of reading and classifying the input data into the output cluster.
Co-Authors Abdiel Khaliel Akmal Adib Adib, Adib Akrom, Akrom Anggita Rahman Arrazy Elba Ridha Bambang Bambang Fajri Fajri Fitriadi Fitriadi Iing Pamungkas Ikhsan Ikhsan Ikhsan Ikhsan Ikhsan Ikhsan Irawan, Heri Tri Kasmawati Kasmawati Mahmud Basuki Muhammad Dekar Nir Fathiya Nursafiah, Nursafiah Putra, Gaustama Rahman, Anggita riza ulhaq Rizki Agam Syahputra Sofiyanurriyanti Wiwit Artika Yaumil Istiqlal M. Nur