Palm Oil Quality Based on Free Fatty Acid Using SVM Article Swipe
Andi Prayogi
,
Moustafa H. Aly
,
Ali Ikhwan
,
Muhammad Akbar Syahbana Pane
,
Ratu Mutiara Siregar
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.29407/intensif.v9i2.24797
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.29407/intensif.v9i2.24797
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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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.29407/intensif.v9i2.24797
- https://ojs.unpkediri.ac.id/index.php/intensif/article/download/24797/4721
- OA Status
- gold
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413857586
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413857586Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.29407/intensif.v9i2.24797Digital Object Identifier
- Title
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Palm Oil Quality Based on Free Fatty Acid Using SVMWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-31Full publication date if available
- Authors
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Andi Prayogi, Moustafa H. Aly, Ali Ikhwan, Muhammad Akbar Syahbana Pane, Ratu Mutiara SiregarList of authors in order
- Landing page
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https://doi.org/10.29407/intensif.v9i2.24797Publisher landing page
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https://ojs.unpkediri.ac.id/index.php/intensif/article/download/24797/4721Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://ojs.unpkediri.ac.id/index.php/intensif/article/download/24797/4721Direct OA link when available
- Concepts
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Palm oil, Palm, Quality (philosophy), Fatty acid, Food science, Support vector machine, Pulp and paper industry, Computer science, Chemistry, Artificial intelligence, Engineering, Biochemistry, Physics, Epistemology, Philosophy, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.≤ | 90 |
| abstract_inverted_index.0.3% | 91 |
| abstract_inverted_index.30%. | 115 |
| abstract_inverted_index.92%, | 131 |
| abstract_inverted_index.94%, | 136 |
| abstract_inverted_index.95%, | 127 |
| abstract_inverted_index.Acid | 27 |
| abstract_inverted_index.Free | 25 |
| abstract_inverted_index.High | 30 |
| abstract_inverted_index.Palm | 1 |
| abstract_inverted_index.This | 160 |
| abstract_inverted_index.both | 10 |
| abstract_inverted_index.data | 83, 109 |
| abstract_inverted_index.food | 12 |
| abstract_inverted_index.oil. | 42 |
| abstract_inverted_index.palm | 44, 173, 189 |
| abstract_inverted_index.that | 120, 163 |
| abstract_inverted_index.this | 97, 180 |
| abstract_inverted_index.were | 61 |
| abstract_inverted_index.with | 16, 66, 112 |
| abstract_inverted_index.(SVM) | 56 |
| abstract_inverted_index.Fatty | 26 |
| abstract_inverted_index.SVM's | 138 |
| abstract_inverted_index.based | 47, 155 |
| abstract_inverted_index.data. | 142 |
| abstract_inverted_index.level | 23 |
| abstract_inverted_index.model | 102, 122, 181 |
| abstract_inverted_index.serve | 166 |
| abstract_inverted_index.study | 161 |
| abstract_inverted_index.usage | 68 |
| abstract_inverted_index.using | 51, 76, 105, 146 |
| abstract_inverted_index.value | 39 |
| abstract_inverted_index.(FFA). | 28 |
| abstract_inverted_index.Vector | 54 |
| abstract_inverted_index.across | 63 |
| abstract_inverted_index.levels | 50, 60 |
| abstract_inverted_index.market | 38 |
| abstract_inverted_index.recall | 134 |
| abstract_inverted_index.reduce | 34 |
| abstract_inverted_index.tested | 111 |
| abstract_inverted_index.Machine | 55 |
| abstract_inverted_index.Support | 53 |
| abstract_inverted_index.between | 152 |
| abstract_inverted_index.clearer | 150 |
| abstract_inverted_index.content | 32 |
| abstract_inverted_index.cycles) | 75 |
| abstract_inverted_index.enhance | 185 |
| abstract_inverted_index.largely | 19 |
| abstract_inverted_index.levels. | 158 |
| abstract_inverted_index.method. | 80 |
| abstract_inverted_index.quality | 18, 46, 175 |
| abstract_inverted_index.results | 118 |
| abstract_inverted_index.samples | 65 |
| abstract_inverted_index.trained | 104 |
| abstract_inverted_index.varying | 67 |
| abstract_inverted_index.Classify | 43 |
| abstract_inverted_index.Methods: | 58 |
| abstract_inverted_index.Results: | 116 |
| abstract_inverted_index.accuracy | 125 |
| abstract_inverted_index.achieved | 123 |
| abstract_inverted_index.exceeded | 96 |
| abstract_inverted_index.expected | 183 |
| abstract_inverted_index.indicate | 119 |
| abstract_inverted_index.measured | 62, 82 |
| abstract_inverted_index.multiple | 64 |
| abstract_inverted_index.non-food | 14 |
| abstract_inverted_index.provided | 148 |
| abstract_inverted_index.FFA-based | 172 |
| abstract_inverted_index.developed | 100 |
| abstract_inverted_index.effective | 169 |
| abstract_inverted_index.industry, | 191 |
| abstract_inverted_index.precision | 129 |
| abstract_inverted_index.remaining | 114 |
| abstract_inverted_index.stability | 36 |
| abstract_inverted_index.titration | 79 |
| abstract_inverted_index."Suitable" | 87 |
| abstract_inverted_index.Evaluation | 117 |
| abstract_inverted_index.Obejctive: | 29 |
| abstract_inverted_index.algorithm. | 57 |
| abstract_inverted_index.categories | 154 |
| abstract_inverted_index.efficiency | 186 |
| abstract_inverted_index.highlights | 162 |
| abstract_inverted_index.hyperplane | 144 |
| abstract_inverted_index.influenced | 20 |
| abstract_inverted_index.threshold. | 98 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.Conclusion: | 159 |
| abstract_inverted_index.alternative | 170 |
| abstract_inverted_index.categorized | 85 |
| abstract_inverted_index.classifying | 141 |
| abstract_inverted_index.commodities | 8 |
| abstract_inverted_index.distinction | 151 |
| abstract_inverted_index.frequencies | 69 |
| abstract_inverted_index.industries, | 15 |
| abstract_inverted_index."Unsuitable" | 93 |
| abstract_inverted_index.alkalimetric | 78 |
| abstract_inverted_index.Additionally, | 143 |
| abstract_inverted_index.demonstrating | 137 |
| abstract_inverted_index.effectiveness | 139 |
| abstract_inverted_index.particularly. | 192 |
| abstract_inverted_index.visualization | 145 |
| abstract_inverted_index.implementation | 178 |
| abstract_inverted_index.classification. | 176 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.37361372 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |