Classification of Oil Loss Levels in Palm Oil Processing Using Near-Infrared Spectroscopy with Machine Learning
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. Free and Open Source Software Near Infrared Spectroscopy (FOSS-NIRS) technology has been proven capabl...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | Indonesian |
| Published: |
Universitas Dian Nuswantoro
2025-08-01
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| Series: | Techno.Com |
| Online Access: | https://publikasi.dinus.ac.id/index.php/technoc/article/view/13135 |
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| Summary: | 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. Free and Open Source Software Near Infrared Spectroscopy (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.
Keywords - Oil, Palm Oil, Losses, FOSS-NIRS. |
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| ISSN: | 2356-2579 |