Optimization of Support Vector Machine Model Performance in Image Classification through Dimension Reduction with Principal Component Analysis (PCA)
DOI:
https://doi.org/10.30871/jaic.v10i1.11675Keywords:
Support Vector Machine, Principal Component Analysis, Image Classification, Dimensionality Reduction, Feature ReductionAbstract
This study examines how to optimize a Support Vector Machine (SVM) model using a dimensionality reduction method called Principal Component Analysis (PCA) to classify images with multiple dimensions. The dataset used is Chessman images with an initial number of features of 12,288. PCA was applied with the aim of retaining 99% of the total variation, resulting in 312 principal components. The results show a significant improvement in computational efficiency: training time was drastically reduced from 29.85 seconds to just 0.17 seconds (168 times faster), and memory usage decreased from 25.83 MB to 0.66 MB (97% more efficient). Although the accuracy experienced a small decrease, namely from 31.58% to 31.22%, PCA still functions as a noise filter that helps improve performance, especially in classes with complex visual patterns, such as an increase in the F1-score of the "Rook" class from 0.32 to 0.37. The conclusions of this study indicate that PCA provides important efficiency improvements without significantly sacrificing classification performance.
Downloads
References
[1] D. Anggriandi, E. Utami, and D. Ariatmanto, “Comparative Analysis of CNN and CNN-SVM Methods For Classification Types of Human Skin Disease,” sinkron, vol. 8, no. 4, pp. 2168–2178, Oct. 2023, doi: 10.33395/sinkron.v8i4.12831.
[2] F. Ennaama, S. Ennaama, and S. Chakri, “Evaluating geometrically-approximated principal component analysis vs. classical eigenfaces: a quantitative study using image quality metrics,” International Journal of Electrical and Computer Engineering, vol. 15, no. 1, pp. 311–318, Feb. 2025, doi: 10.11591/ijece.v15i1.pp311-318.
[3] A. Aristo Jansen Sinlae, D. Alamsyah, L. Suhery, and F. Fatmayati, “Classification of Broadleaf Weeds Using a Combination of K-Nearest Neighbor (KNN) and Principal Component Analysis (PCA),” Sinkron, vol. 7, no. 1, pp. 93–100, Jan. 2022, doi: 10.33395/sinkron.v7i1.11237.
[4] L. G. S. Kartika, P. K. L. Utama, I. D. G. Budiastawa, and K. Rinartha, “Comparison of the Sentiment Analysis Model’s Code Complexity and Processing Time,” Sinkron, vol. 8, no. 1, pp. 109–118, Jan. 2023, doi: 10.33395/sinkron.v8i1.11894.
[5] D. I. Sumantiawan, J. E. Suseno, and W. A. Syafei, “Sentiment Analysis of Customer Reviews Using Support Vector Machine and Smote-Tomek Links For Identify Customer Satisfaction,” J. Sistem Info. Bisnis, vol. 13, no. 1, pp. 1–9, Jun. 2023, doi: 10.21456/vol13iss1pp1-9.
[6] I. B. Sya’idah, S. Surono, and G. Khang Wen, “DynamicWeighted Particle Swarm Optimization - Support Vector Machine Optimization in Recursive Feature Elimination Feature Selection,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 3, pp. 627–640, Jul. 2024, doi: 10.30812/matrik.v23i3.3963.
[7] M. Ikhsan and M. Rahardi, “Image-Based Classification of Indonesian Traditional Houses Using a Hybrid CNN-SVM Algorithm,” 2025. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[8] Z. Elkhadir and M. A. Begdouri, “Enhancing internet of things attack detection using principal component analysis and kernel principal component analysis with cosine distance and sigmoid kernel,” International Journal of Electrical and Computer Engineering, vol. 15, no. 1, pp. 1099–1108, Feb. 2025, doi: 10.11591/ijece.v15i1.pp1099-1108.
[9] M. Fahmi, A. Yudhana, and S. Sunardi, “Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 22, no. 3, pp. 553–566, Jul. 2023, doi: 10.30812/matrik.v22i3.2819.
[10] I. Zainan Nisa, S. Nur Endah, P. Sidik Sasongko, and R. Kusumaningrum, “Klasifikasi Citra Sampah Menggunakan Support Vector Machine Dengan Ekstraksi Fitur Gray Level Co-Occurrence Matrix Dan Color Moments”, doi: 10.25126/jtiik.202294868.
[11] Sugeng and H. Praminiarto, “Detection of Drowsiness in Drivers Using Image Processing and Support Vector Machine (SVM) Classification,” Jurnal Online Informatika, vol. 9, no. 2, pp. 238–248, Aug. 2024, doi: 10.15575/join.v9i2.1076.
[12] B. Oktaviana, “Implementation of the Support Vector Machine (SVM) Method for Classifying the Maturity Level of Oil Palm Fruit,” 2025. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[13] M. Desiawan and A. Solichin, “SVM Optimization with Grid Search Cross Validation for Improving Accuracy of Schizophrenia Classification Based on EEG Signal,” JURNAL TEKNIK INFORMATIKA, vol. 17, no. 1, pp. 10–20, May 2024, doi: 10.15408/jti.v17i1.37422.
[14] Y. Irawan, R. Pramitasari, W. M. Ashari, A. Nur, and H. Yansyah, “Support Vector Machine Classification Algorithm for Detecting DDoS Attacks on Network Traffic,” 2025. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[15] T. M. Murugan, C. R. Lenus, S. Sridharan, and A. Malligarjun, “Life Time Prediction Of An Electromagnet Relay Using Clustering Based Principal Component Analysis With Hybrid Deep Learning Model.”
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Zahfar Aziz Ferdian, Joko Sutopo
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
