SHAPE AND TEXTURE INTEGRATION FOR JAVA SEA FISH CLASSIFICATION USING K-NEAREST NEIGHBORS ALGORITHM

Pingkan Putri Nazarina; Arif Pramudwiatmoko

doi:10.34288/jri.v8i1.464

Authors

Pingkan Putri Nazarina Universitas Teknologi Yogyakarta
Arif Pramudwiatmoko Universitas Teknologi Yogyakarta

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v8i1.464

Keywords:

fish classification, k-nearest neighbors, hu moments, local binary pattern, digital image processing

Abstract

Manual identification of fish species at fish auction sites (TPI) was often time-consuming and prone to inconsistencies, which affected economic valuation and data recording accuracy. This study proposed an automated fish classification system to address these challenges using the K-Nearest Neighbors (KNN) method. The system was designed to assist the fish identification process in the Java Sea, with a case study conducted at the Karanganyar Fish Auction Site. The proposed approach employed computer vision techniques, beginning with image pre-processing steps such as segmentation and cropping to isolate fish objects. Subsequently, two complementary feature extraction methods were combined to obtain a robust representation of each fish image: Hu Moments for capturing holistic shape features that are invariant to scale and rotation, and Local Binary Pattern (LBP) for extracting detailed surface texture information. This hybrid feature representation provided a comprehensive descriptor for every fish instance. The dataset consisted of 1,000 images categorized into 10 main fish species (e.g., tongkol, bawal, and others). Model training and hyperparameter optimization were performed using a k-fold cross-validation scheme, followed by an 80:20 train-test evaluation. The experimental results demonstrated that the KNN model with the optimal k value achieved an overall classification accuracy of 98.50% on the unseen test set. These findings indicated that the integration of Hu Moments and LBP features was highly effective in distinguishing fish species and showed strong potential for practical implementation as a fast, objective, and reliable identification tool at fish auction sites such as Karanganyar Fish Auction Site

Downloads

Download data is not yet available.

References

B, V., & Gangula, R. (2024). Exploring the Power and Practical Applications of K-Nearest Neighbours (KNN) in Machine Learning. Journal of Computer Allied Intelligence, 2(1), 8–15. https://doi.org/10.69996/jcai.2024002

Daulay, R. S. A., Efendi, S., & Suherman. (2023). Review of Literature on Improving the KNN Algorithm. Transactions on Machine Learning and Artificial Intelligence, 11(3), 63–72. https://doi.org/10.14738/tecs.113.14768

Dewan, J., Gele, A., Fulari, O., Kabade, B., & Joshi, A. (2022). Fish Detection and Classification. 2022 6th International Conference on Computing, Communication, Control and Automation (ICCUBEA, 1–5. https://doi.org/10.1109/ICCUBEA54992.2022.10010836

Dhabliya, D., Thangarasu, N., Reddy, B., Nirmala, D., Parmar, Y., & Ganesan, S. (2024). Feature Extraction Techniques in Document Image Analysis an Analysis. 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. https://doi.org/10.1109/ICCCNT61001.2024.10724751

Fajri, F. N., Dzikrillah, M., & Khairi, A. (2024). Digital Fish Image Segmentation Using U-Net for Shape Feature Extraction. JURNAL TEKNOLOGI DAN OPEN SOURCE, 7(2), 195–201. https://doi.org/10.36378/jtos.v7i2.3968

Iman, Y. D. Al, Isnanto, R. R., & Nurhayati, O. D. (2023). Klasifikasi Jenis Ikan Laut K-Nearest Neighbor Berdasarkan Ekstraksi Ciri 2-Dimensional Linear Discriminant Analysis. Jurnal Teknologi Informasi Dan Ilmu Komputer, 10(4), 919–926. https://doi.org/10.25126/jtiik.20241046787

Jannah, M., Nababan, A. A., & Ningsi, Y. S. (2023). Penerapan Metode K-Nearest Neighbor dalam Identifikasi Jenis Ikan Salmon yang Dapat Dikomsumsi Untuk Bahan Mpasi Bayi. J-SISKO TECH (Jurnal Teknologi Sistem Informasi Dan Sistem Komputer TGD), 6(2), 636. https://doi.org/10.53513/jsk.v6i2.8716

Khan, M. A., Jamil, S. A., Vellanki, S., Latha, M., Singh, K. U., & Batra, M. (2024). Creation of Machine Learning-Based Fish Classification Systems Based on Morphometric and Mathematical Transform Data. J. Electrical Systems, 20(5), 2325–2332.

Khatami, A. M., Yonvitner, & Setyobudiandi, I. (2019). Karakteristik Biologi dan Laju Eksploitasi Ikan Pelagis Kecil di Perairan Utara Jawa. Jurnal Ilmu Dan Teknologi Kelautan Tropis, 11(3), 637–651. https://doi.org/10.29244/jitkt.v11i3.19159

Kusuma, F. H., Ms, A. U., Ibadillah, A. F., Nahari, V., Joni, K., & Saputro, A. K. (2023). Sistem Identifikasi Kesegaran dan Jenis Ikan dengan Metode K-Nearest Neighbor Berdasarkan Citra Mata dan Bentuk Ikan. Jurnal FORTECH, 4(1), 33–41. https://doi.org/10.56795/fortech.v4i1.383

Li, G. (2024). Machine learning. In Chemical Theory and Multiscale Simulation in Biomolecules (pp. 51–80). Elsevier. https://doi.org/10.1016/B978-0-323-95917-9.00004-3

Mubarok, M. I., Sulistyowati, B. I., Perangin-angin, R., & Nurlaela, E. (2023). Composition of The Catch of Mini Purse Seine in The Java Sea. Coastal and Marine Journal, 1(1), 23–28. https://doi.org/10.61548/cmj.v1i1.5

Nuraini, R., Wibowo, A., Warsito, B., Syafei, W. A., & Jaya, I. (2023). Combination of K-NN and PCA Algorithms on Image Classification of Fish Species. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(5), 1026–1032. https://doi.org/10.29207/resti.v7i5.5178

Nuralam, M. M., Hernawati, D., & Agustian, D. (2023). Keanekaragaman dan Potensi Jenis Ikan Hasil Tangkapan Nelayan di Tempat Pelelangan Ikan (TPI) Pamayangsari Kabupaten Tasikmalaya. Jurnal Biosilampari: Jurnal Biologi, 5(2), 154–162. https://doi.org/10.31540/biosilampari.v5i2.2000

Oktaviani, D., Prianto, E., & Nugroho, D. (2020). Length-Weight, Maturity, and Condition Factor of Torpedo Scads (Megalaspis cordyla Linnaeus, 1758) in the Java Sea, Indonesia. Biodiversitas, 21(4), 1527–1534. https://doi.org/10.13057/biodiv/d210433

P, R. K. (2024). Role of Digital Image Processing in Education and Medical Field. IOSR Journal of Computer Engineering, 26(5), 01–08. https://doi.org/10.9790/0661-2605030108

Patel, D. (2024). Computer Vision and Image Segmentation. International Journal for Research in Applied Science and Engineering Technology, 12(2), 915–925. https://doi.org/10.22214/ijraset.2024.58479

Regulwar, G. B., Mahalle, A., Pawar, R., Shamkuwar, S. K., Kakde, P. R., & Tiwari, S. (2024). Big Data Collection, Filtering, and Extraction of Features. In D. Darwish (Ed.), Big Data Analytics Techniques for Market Intelligence (pp. 136–158). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-0413-6.ch005

Suwarsito, Mustafidah, H., Pinandita, T., & Purnomo. (2022). Freshwater Fish Classification Based on Image Representation Using K-Nearest Neighbor Method. JUITA: Jurnal Informatika, 10(2), 183.

Zheng, T., Wu, J., Kong, H., Zhao, H., Qu, B., Liu, L., Yu, H., & Zhou, C. (2024). A video object segmentation-based fish individual recognition method for underwater complex environments. Ecological Informatics, 82(1), 102689. https://doi.org/https://doi.org/10.1016/j.ecoinf.2024.102689