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All Journal JAIS (Journal of Applied Intelligent System) Jurnal Teknologi Bank Darah
Muhamad Ilham Fauzan
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Biochemistry, Genetics & Molecular Biology Health Professions Immunology & microbiology Medicine & Pharmacology Public Health

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Journal : JAIS (Journal of Applied Intelligent System)

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Classification of Oil Loss Levels in Palm Oil Processing Using Near-Infrared Spectroscopy with Machine Learning Muhamad Ilham Fauzan; BAskara, Jaka Adi; Putri, Wahyuningdiah Trisari Harsanti; Pranoto, Gatot Tri
(JAIS) Journal of Applied Intelligent System Vol. 10 No. 1 (2025): April 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v10i1.13037

Abstract

Oil losses in palm oil processing materials, such as Final Effluent, Empty Fruit Bunches, Kernels, Pressed Fiber, and Decanter Solids, pose significant challenges in ensuring production efficiency. FOSS-NIRS technology has been proven capable of quickly and efficiently detecting oil content, but its detection accuracy requires further analytical support. This study aims to develop a machine learning model that can accurately classify FOSS-NIRS data to detect oil losses that are either above the standard (red category) or below the standard (green category). By utilizing FOSS-NIRS data across five material categories, the proposed model is expected to provide precise predictions and support decision-making in palm oil production processes. The results of the study indicate that applying machine learning methods to FOSS-NIRS data can enhance the accuracy of oil loss classification, making it a potential solution for broader implementation in the palm oil processing industry to optimize production efficiency.
Co-Authors Ayu Suraduhita Baskara, Jaka Adi Gatot Tri Pranoto Putri, Wahyuningdiah Trisari Harsanti