Published: 2025-11-30

Classification of Skipjack Freshness Quality Based on Local Binary Pattern and Gray Level Co-Occurrence Matrix Using K-Nearest Neighbor

DOI: 10.35870/ijecs.v5i3.5791

Zulfrianto Y Lamasigi, Mohamad Efendi Lasulika, Sarlis Mooduto
  • Zulfrianto Y Lamasigi: Universitas Ichsan Gorontalo
  • Mohamad Efendi Lasulika: Universitas Ichsan Gorontalo
  • Sarlis Mooduto: Universitas Ichsan Gorontalo
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Abstract

Katsuwonus pelamis or skipjack tuna is one of the results of fishing commodities from Gorontalo Province. The quality of fresh fish can be degraded easily if not handled and stored properly. Thus, in this study an automatic system for classifying the freshness level of skipjack tuna based on digital image processing techniques was introduced. It uses Local Binary Pattern (LBP) to extract local texture features and Gray Level Co-occurrence Matrix (GLCM) for statistical texture analysis with classification done by K-Nearest Neighbor (K-NN) algorithm using Euclidean distance as a measurement between features. There were 819 training images and 140 test images used in four categories: Fresh, Not Fresh, Worth Consuming, and Rotten. Tests on several values of k showed that the highest accuracy was at k = 1 with an accuracy rate of 86.42% while the lowest was at k = 9 with a rate of 49.28%. This indicates that the combination LBP-GLCM applied in K-NN has potentiality to capture texture difference effect from various levels fish freshness. This method is non-destructive and could be onboard application for fish quality monitoring as well as automatic system for freshness evaluation.

Keywords

Skipjack Tuna ; Image Processing ; Local Binary Pattern ; Gray Level Co-occurrence Matrix ; K-Nearest Neighbor

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How to Cite

Y Lamasigi, Z., Efendi Lasulika, M., & Mooduto, S. (2025). Classification of Skipjack Freshness Quality Based on Local Binary Pattern and Gray Level Co-Occurrence Matrix Using K-Nearest Neighbor. International Journal Education and Computer Studies (IJECS), 5(3), 246-257. https://doi.org/10.35870/ijecs.v5i3.5791
Article Information

This article has been peer-reviewed and published in the International Journal Education and Computer Studies (IJECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 5 No. 3 (2025)

  • Section: Special Issue

  • Published: %750 %e, %2025

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Author Biographies
Zulfrianto Y Lamasigi
Google Scholar ResearchGate ORCID

Informatics Engineering Department, Universitas Ichsan Gorontalo, Gorontalo City, Gorontalo Province, Indonesia.

Mohamad Efendi Lasulika
Google Scholar ResearchGate ORCID

Informatics Engineering Department, Universitas Ichsan Gorontalo, Gorontalo City, Gorontalo Province, Indonesia.

Sarlis Mooduto
Google Scholar ResearchGate ORCID

Informatics Engineering Department, Universitas Ichsan Gorontalo, Gorontalo City, Gorontalo Province, Indonesia.

References
  • Pemerintah Provinsi Gorontalo. (2017). Rencana strategis (Renstra) Dinas Kelautan dan Perikanan Provinsi Gorontalo. https://dinaskp.gorontaloprov.go.id
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Gobel, M. R., Baruwadi, M., & Rauf, A. (2019). Analisis daya saing ikan tuna di Provinsi Gorontalo. Jambura Agribusiness Journal, 1(1), 36–42. https://doi.org/10.37046/jaj.v1i1.2448
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Rindengan, A. J., & Mananohas, M. (2017). Perancangan sistem penentuan tingkat kesegaran ikan cakalang menggunakan metode curve fitting berbasis citra digital mata ikan. Jurnal Ilmiah Sains, 17(2), 161. https://doi.org/10.35799/jis.17.2.2017.18128
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Lamasigi, Z. Y., Hasan, M., & Lasena, Y. (2020). Local Binary Pattern untuk pengenalan jenis daun tanaman obat menggunakan K-Nearest Neighbor. ILKOM Jurnal Ilmiah, 12(3), 208–218. https://doi.org/10.33096/ilkom.v12i3.667.208-218
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Novitasari, A., Purwandari, E. P., & Coastera, F. F. (2018). Identifikasi citra daun tanaman jeruk dengan Local Binary Pattern dan Moment Invariant. Jurnal Informatika dan Komputer, 3(2), 76–83.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Novianto, T. D., & Erawan, I. M. S. (2020). Perbandingan metode klasifikasi pada pengolahan citra mata ikan tuna. Prosiding Seminar Nasional Fisika dan Aplikasinya (SNFA), 5, 216–223. https://doi.org/10.20961/prosidingsnfa.v5i0.46615
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Lamasigi, Z. Y. (2022). Identifikasi tingkat kesegaran ikan tuna menggunakan metode GLCM dan KNN. Jambura Journal of Electrical and Electronics Engineering, 4, 70–76.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Fitriyah, H., Syauqy, D., & Susilo, F. A. (2020). Deteksi kesegaran ikan tongkol (Euthynnus affinis) secara otomatis berdasarkan citra mata menggunakan Binary Similarity. Jurnal Teknologi Informasi dan Ilmu Komputer, 7(5), 879. https://doi.org/10.25126/jtiik.2020753839
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Sultoni, M. I., Hidayat, B., & Subandrio, A. S. (2019). Klasifikasi jenis batuan beku melalui citra berwarna dengan menggunakan metode Local Binary Pattern dan K-Nearest Neighbor. Geol. Sains, 4(1), 10–15.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Lamasigi, Z. Y. (2021). DCT untuk ekstraksi fitur berbasis GLCM pada identifikasi batik menggunakan K-NN. Jambura Journal of Electrical and Electronics Engineering, 3(1), 1–6. https://doi.org/10.37905/jjeee.v3i1.7113
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Sarimin, M., Hayaty, N., Bettiza, M., & Nugraha, S. (2019). Implementasi HSV dan GLCM untuk deteksi kesegaran ikan bawal menggunakan Radial Basis Function berbasis Android. Sustainable: Jurnal Hasil Penelitian dan Industri Terapan, 8(1), 1–7. https://doi.org/10.31629/sustainable.v8i1.1319
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Lamasigi, Z. Y., & Bode, A. (2021). Influence of gray level co-occurrence matrix for texture feature extraction on identification of batik motifs using K-Nearest Neighbor. Jurnal Ilmiah, 13(3), 208–218.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Roberto, N. W. (2019). Penerapan metode K-Nearest Neighbor dalam identifikasi kesegaran ikan.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
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