Published: 2026-10-01

Pikachu Image Classification Using Neural Network and Support Vector Machine with Painters, VGG-16, and Inception-V3 Feature Representations

DOI: 10.35870/jtik.v10i4.6905

Muthia Andriana Putri, Imam Yuadi
  • Muthia Andriana Putri: Universitas Airlangga
  • Imam Yuadi: Universitas Airlangga
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Abstract

This study aims to evaluate the effectiveness of multiple feature representations in classifying Pikachu images into three distinct visual categories: anime, action figures, and hand-drawn illustrations. The primary challenge lies in limited data availability and the high visual variability across styles, resulting in significant inter-class similarity and intra-class diversity. To address this issue, the study employs a transfer learning approach utilizing pre-trained Convolutional Neural Networks (CNNs), namely VGG-16 and Inception-V3, alongside painterly feature descriptors. The dataset comprises 351 images collected from open-access sources with balanced class distribution. Extracted features are subsequently classified using Support Vector Machines (SVM) and shallow Neural Networks. The findings demonstrate that integrating deep semantic features with artistic representations significantly improves classification accuracy compared to single-feature approaches. These results highlight the critical role of hybrid feature engineering and classifier selection in achieving robust image classification performance under data-constrained conditions.

Keywords

Image Classification; Transfer Learning; Convolutional Neural Networks; Support Vector Machine; Feature Representation

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How to Cite

Putri, M. A., & Yuadi, I. (2026). Pikachu Image Classification Using Neural Network and Support Vector Machine with Painters, VGG-16, and Inception-V3 Feature Representations. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 10(4), 1431-1443. https://doi.org/10.35870/jtik.v10i4.6905
Article Information

This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 10 No. 4 (2026)

  • Section: Computer & Communication Science

  • Published: 2026-10-01

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Author Biographies
Muthia Andriana Putri, Universitas Airlangga
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Universitas Airlangga, Surabaya City, East Java Province, Indonesia.

Imam Yuadi, Universitas Airlangga
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Universitas Airlangga, Surabaya City, East Java Province, Indonesia.

References
  • Chaturvedi, S. S., Tembhurne, J. V., & Diwan, T. (2020). A Multi-Class Skin Cancer Classification Using Deep Convolutional Neural Networks. Multimedia Tools and Applications, 79(39–40), 28477–28498. https://doi.org/10.1007/s11042-020-09388-2.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Chen, H., Wang, Y., Guo, J., & Tao, D. (2023). VanillaNet: the Power of Minimalism in Deep Learning. Advances in Neural Information Processing Systems, 36, 7050–7064.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Chen, M., Chen, W., Chen, W., Cai, L., & Chai, G. (2020). Skin Cancer Classification with Deep Convolutional Neural Networks. Journal of Medical Imaging and Health Informatics, 10(7), 1707–1713. https://doi.org/10.1166/jmihi.2020.3078.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Deng, J., Dong, W., Socher, R., Li, L.-J., Kai Li, & Li Fei-Fei. (2009). ImageNet: A Large-Scale Hierarchical Image Database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255. https://doi.org/10.1109/CVPR.2009.5206848
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Imran, S., Naqvi, R. A., Sajid, M., Malik, T. S., Ullah, S., Moqurrab, S. A., & Yon, D. K. (2023). Artistic Style Recognition: Combining Deep and Shallow Neural Networks for Painting Classification. Mathematics, 11(22), 4564. https://doi.org/10.3390/math11224564.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Iqbal, M. A., Wang, Z., Ali, Z. A., & Riaz, S. (2021). Automatic Fish Species Classification Using Deep Convolutional Neural Networks. Wireless Personal Communications, 116(2), 1043–1053. https://doi.org/10.1007/s11277-019-06634-1.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Jastrzebska, A. (2022). Time Series Classification Through Visual Pattern Recognition. Journal of King Saud University - Computer and Information Sciences, 34(2), 134–142. https://doi.org/10.1016/j.jksuci.2019.12.012.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A Survey of The Recent Architectures of Deep Convolutional Neural Networks. Artificial Intelligence Review, 53(8), 5455–5516. https://doi.org/10.1007/s10462-020-09825-6.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Kumar, N., Kaur, N., & Gupta, D. (2020). Major Convolutional Neural Networks in Image Classification: A Survey (pp. 243–258). https://doi.org/10.1007/978-981-15-3020-3_23.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Law, S., Seresinhe, C. I., Shen, Y., & Gutierrez-Roig, M. (2020). Street-Frontage-Net: Urban Image Classification Using Deep Convolutional Neural Networks. International Journal of Geographical Information Science, 34(4), 681–707. https://doi.org/10.1080/13658816.2018.1555832.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Liu, S., Niles-Weed, J., Razavian, N., & Fernandez-Granda, C. (2020). Early-Learning Regularization Prevents Memorization of Noisy Labels. Advances in Neural Information Processing Systems, 33, 20331–20342.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Nnamoko, N., Barrowclough, J., & Procter, J. (2022). Solid Waste Image Classification Using Deep Convolutional Neural Network. Infrastructures, 7(4), 47. https://doi.org/10.3390/infrastructures7040047.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Pinaya, W. H. L., Vieira, S., Garcia-Dias, R., & Mechelli, A. (2020). Convolutional Neural Networks. In Machine Learning (pp. 173–191). Elsevier. https://doi.org/10.1016/B978-0-12-815739-8.00010-9.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., & Dosovitskiy, A. (2021). Do Vision Transformers See Like Convolutional Neural Networks? Advances in Neural Information Processing Systems, 34, 12116–12128.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Raswa, F. H., Lu, C.-S., & Wang, J.-C. (2025). HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 30251–30260).
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Shen, X., Wang, Y., Lin, M., Huang, Y., Tang, H., Sun, X., & Wang, Y. (2023). DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6163–6173.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Singh, R., Bharti, V., Purohit, V., Kumar, A., Singh, A. K., & Singh, S. K. (2021). MetaMed: Few-Shot Medical Image Classification Using Gradient-Based Meta-Learning. Pattern Recognition, 120, 108111. https://doi.org/10.1016/j.patcog.2021.108111
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Tselentis, D. I., & Papadimitriou, E. (2023). Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions. IEEE Open Journal of Intelligent Transportation Systems, 4, 83–100. https://doi.org/10.1109/OJITS.2023.3237177
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Tsipras, D., Santurkar, S., Engstrom, L., Ilyas, A., & Madry, A. (2020). From ImageNet to Image Classification: Contextualizing Progress on Benchmarks. Proceedings of Machine Learning Research, 9625–9635.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Visalli, F., Bonacci, T., & Borghese, N. A. (2021). Insects Image Classification Through Deep Convolutional Neural Networks. In Progresses in Artificial Intelligence and Neural Systems (pp. 217–228). https://doi.org/10.1007/978-981-15-5093-5_21.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Zhang, Y., Gao, J., & Zhou, H. (2020). Breeds Classification with Deep Convolutional Neural Network. Proceedings of the 2020 12th International Conference on Machine Learning and Computing, 145–151. https://doi.org/10.1145/3383972.3383975.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Zhao, J., Fang, Y., & Li, G. (2021). Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation. Advances in Neural Information Processing Systems, 34, 10627–10640.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
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