Palm Oil Quality Based on Free Fatty Acid Using SVM

Andi Prayogi; Moustafa H. Aly; Ali Ikhwan; Muhammad Akbar Syahbana Pane; Ratu Mutiara Siregar

doi:10.29407/intensif.v9i2.24797

Authors

Andi Prayogi Institut Teknologi Sawit Indonesia https://orcid.org/0000-0002-8237-3454
Moustafa H. Aly Arab Academy for Science Technology and Maritime Transport https://orcid.org/0000-0003-1966-3755
Ali Ikhwan School of Computer and Communication Engineering https://orcid.org/0000-0003-3979-1834
Muhammad Akbar Syahbana Pane Institut Teknologi Sawit Indonesia https://orcid.org/0009-0001-6314-2386
Ratu Mutiara Siregar Institut Teknologi Sawit Indonesia https://orcid.org/0009-0000-8015-5261

DOI:

https://doi.org/10.29407/intensif.v9i2.24797

Keywords:

Classification, FFA, SVM

Abstract

Background: Palm oil is one of the key commodities in both the food and non-food industries, with its quality largely influenced by the level of Free Fatty Acid (FFA). Obejctive: High FFA content can reduce the stability and market value of the oil. Classify palm oil quality based on FFA levels using the Support Vector Machine (SVM) algorithm. Methods: FFA levels were measured across multiple samples with varying usage frequencies (0, 5, 7, and 9 cycles) using the alkalimetric titration method. The measured data was categorized as "Suitable" if FFA ≤ 0.3% and "Unsuitable" if it exceeded this threshold. The developed SVM model was trained using 70% of the data and tested with the remaining 30%. Results: Evaluation results indicate that the model achieved an accuracy of 95%, a precision of 92%, and a recall of 94%, demonstrating SVM's effectiveness in classifying data. Additionally, hyperplane visualization using PCA provided a clearer distinction between oil categories based on FFA levels. Conclusion: This study highlights that SVM can serve as an effective alternative for FFA-based palm oil quality classification. The implementation of this model is expected to enhance efficiency in the palm oil industry, particularly.

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Author Biographies

Andi Prayogi, Institut Teknologi Sawit Indonesia

Sistem dan Teknologi Informasi, Institut Teknologi Sawit Indonesia
Moustafa H. Aly, Arab Academy for Science Technology and Maritime Transport

College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Egypt
Ali Ikhwan, School of Computer and Communication Engineering

Embedded, Networks and Advanced Computing (ENAC) Research Cluster, School of Computer and Communication Engineering, Perlis, Malaysia
Muhammad Akbar Syahbana Pane, Institut Teknologi Sawit Indonesia

Sistem dan Teknologi Informasi, Institut Teknologi Sawit Indonesia
Ratu Mutiara Siregar, Institut Teknologi Sawit Indonesia

Sistem dan Teknologi Informasi, Institut Teknologi Sawit Indonesia

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