Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease Classification
Palm oil plantations in Indonesia face challenges in enhancing productivity and profitability, notably due to pest attacks that reduce production. Early identification and classification of plant conditions, particularly palm oil leaves, are crucial for mitigating losses. This study explores the app...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2024-12-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6049 |
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| author | Veri Julianto Ahmad Rusadi Arrahimi Oky Rahmanto Mohammad Sofwat Aldi |
| author_facet | Veri Julianto Ahmad Rusadi Arrahimi Oky Rahmanto Mohammad Sofwat Aldi |
| author_sort | Veri Julianto |
| collection | DOAJ |
| description | Palm oil plantations in Indonesia face challenges in enhancing productivity and profitability, notably due to pest attacks that reduce production. Early identification and classification of plant conditions, particularly palm oil leaves, are crucial for mitigating losses. This study explores the application of artificial intelligence, specifically computer vision and machine learning, for disease detection. Various machine learning techniques, including Local Binary Pattern (LBP), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), have been used in different studies with varying accuracy. This research focuses on modifying Particle Swarm Optimization (PSO) for feature selection in identifying diseases in palm oil leaves. The PSO modification combined with logistic regression and Bayesian Information Criterion (BIC) significantly enhances KNN performance. Accuracy improved from 95.75% to 97.85%, while precision, recall, and F1-score reached approximately 98.80%. Additionally, the modified KNN+PSO achieved the shortest computation time of 0.0872 seconds, indicating high computational efficiency. These results demonstrate that the PSO modification not only improves accuracy but also computational efficiency, making it an effective method for enhancing KNN performance in detecting palm oil leaf diseases. |
| format | Article |
| id | doaj-art-10cddc35726e4b5e891d2017b7d9b071 |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-10cddc35726e4b5e891d2017b7d9b0712025-01-13T03:30:32ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-12-018684685210.29207/resti.v8i6.60496049Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease ClassificationVeri Julianto0Ahmad Rusadi Arrahimi1Oky Rahmanto2Mohammad Sofwat Aldi3Politeknik Negeri Tanah LautPoliteknik Negeri Tanah LautPoliteknik Negeri Tanah LautPoliteknik Negeri Tanah LautPalm oil plantations in Indonesia face challenges in enhancing productivity and profitability, notably due to pest attacks that reduce production. Early identification and classification of plant conditions, particularly palm oil leaves, are crucial for mitigating losses. This study explores the application of artificial intelligence, specifically computer vision and machine learning, for disease detection. Various machine learning techniques, including Local Binary Pattern (LBP), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), have been used in different studies with varying accuracy. This research focuses on modifying Particle Swarm Optimization (PSO) for feature selection in identifying diseases in palm oil leaves. The PSO modification combined with logistic regression and Bayesian Information Criterion (BIC) significantly enhances KNN performance. Accuracy improved from 95.75% to 97.85%, while precision, recall, and F1-score reached approximately 98.80%. Additionally, the modified KNN+PSO achieved the shortest computation time of 0.0872 seconds, indicating high computational efficiency. These results demonstrate that the PSO modification not only improves accuracy but also computational efficiency, making it an effective method for enhancing KNN performance in detecting palm oil leaf diseases.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6049disease identificationleaf classificationk-nearest neighbors (knn)particle swarm optimization (pso)computational efficiency |
| spellingShingle | Veri Julianto Ahmad Rusadi Arrahimi Oky Rahmanto Mohammad Sofwat Aldi Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease Classification Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) disease identification leaf classification k-nearest neighbors (knn) particle swarm optimization (pso) computational efficiency |
| title | Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease Classification |
| title_full | Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease Classification |
| title_fullStr | Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease Classification |
| title_full_unstemmed | Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease Classification |
| title_short | Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease Classification |
| title_sort | modified particle swarm optimization on feature selection for palm leaf disease classification |
| topic | disease identification leaf classification k-nearest neighbors (knn) particle swarm optimization (pso) computational efficiency |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6049 |
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