Published: 2025-07-01

Klasifikasi Jenis Kucing Menggunakan Algoritma K-Nearest Neighbor

DOI: 10.35870/jtik.v9i3.3821

Dadang Iskandar Mulyana, Siti Nurhaliza
  • Dadang Iskandar Mulyana: Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika , Indonesia
  • Siti Nurhaliza: Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika , Indonesia
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Abstract

Cats in Latin is Felis silvestris catus is a kind of carnivorous animal. Cats are the most popular pets in the world that have many enthusiasts and fans. Cats that have bloodlines are officially registered as purebred cats or pure breeds. The number of cat breeds like this is only slightly, only 1% of the world's cat population, which is usually only bred in official animal husbandry. This study uses the Principal Component Analysis (PCA) and K-nearest Neighbor (KNN) algorithms with the aim of classifying cat images through the analysis stage on original images, binary images and grayscale images.The output of the feature extraction will be the input for the Principal Component Analysis (PCA) and K-nearest Neighbor (KNN) algorithms for cat species classification applications. The feature extraction that will be used in this research are RGB and HSV. The data that will be used in this study are 34 image data, consisting of 24 training data images and 10 test image data.So with this research, it is hoped that it can help people to more easily find out the classification of pets, namely cats. The output accuracy in the classification application uses the Multi Support Vector Machine (SVM) Algorithm with first- order feature extraction from the Principal Component Analysis (PCA) and K-nearest Neighbor (KNN) algorithms, which reaches an accuracy rate of 80%.

Keywords

Pure Breed ; K-NN ; PCA ; RGB ; HSV

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How to Cite

Mulyana, D. I., & Nurhaliza, S. (2025). Klasifikasi Jenis Kucing Menggunakan Algoritma K-Nearest Neighbor. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 9(3), 844-851. https://doi.org/10.35870/jtik.v9i3.3821
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. 9 No. 3 (2025)

  • Section: Computer & Communication Science

  • Published: %750 %e, %2025

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Author Biographies
Dadang Iskandar Mulyana
Google Scholar ResearchGate ORCID

Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

Siti Nurhaliza
Google Scholar ResearchGate ORCID

Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

References
  • Adiwijaya, W. U., Lisnawati, E., Aditsania, A., & Kusumo, D. S. (2018). Dimensionality reduction using principal component analysis for cancer detection based on microarray data classification. Journal of Computer Science, 14(11), 1521-1530.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Effendi, M. R. (2018). Sistem Deteksi Wajah Jenis Kucing Dengan Image Classification Menggunakan OpenCV. Jurnal Teknologi Informatika dan Komputer, 4(1), 27-35. https://doi.org/10.37012/jtik.v4i1.283.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Fawwaz, M. A. A., Ramadhani, K. N., & Sthevanie, F. (2021). Klasifikasi Ras pada Kucing menggunakan Algoritma Convolutional Neural Network (CNN). eProceedings of Engineering, 8(1).
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Gonzales, R. C., Woods, R. E., & Eddins, S. L. (2004). Digital image processing using MATLAB. Pearson Prentice Hall.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Hafizhah, D. N., & Hamdan, S. R. (2021). Hubungan pet attachment dengan psychological well-being pada pemelihara kucing kota Bandung. Prosiding Psikologi http://dx. doi. org, 7(1), 73-76.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Hasan, M. A., & Liliana, D. Y. (2020). Pengenalan Motif Songket Palembang Menggunakan Deteksi Tepi Canny, PCA dan KNN. vol, 6, 1-7.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Himmah, E. F., Widyaningsih, M., & Maysaroh, M. (2020). Identifikasi kematangan buah kelapa sawit berdasarkan warna RGB dan HSV menggunakan metode K-Means clustering. Jurnal Sains Dan Informatika, 6(2), 193-202. https://doi.org/10.34128/jsi.v6i2.242.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Jamala, A., Handayania, A., Septiandria, A. A., Jamal, A., Handayani, A., Septiandri, A. A., Ripmiatin, E., & Effendi, Y. (2018). Dimensionality reduction using pca and k-means clustering for breast cancer prediction. Lontar Komput. J. Ilm. Teknol. Inf, 9(3), 192-201.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Kurniati, N. I. (2022). Klasifikasi Jenis Buah Jambu Biji Menggunakan Algoritma Principal Component Analysis dan K-Nearest Neighbor. Generation Journal, 6(2), 106-113. https://doi.org/10.29407/gj.v6i2.17777.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Novianto, D., & Sugihartono, T. (2020). Sistem Deteksi Kualitas Buah Jambu Air Berdasarkan Warna Kulit Menggunakan Algoritma Principal Component Analysis (Pca) dan K-Nearest Neigbor (K-NN). Jurnal Ilmiah Informatika Global, 11(2). https://doi.org/10.36982/jiig.v11i2.1223.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Nugraha, R. A., Hidayat, E. W., & Shofa, R. N. (2023). Klasifikasi Jenis Buah Jambu Biji Menggunakan Algoritma Principal Component Analysis dan K-Nearest Neighbor. Generation Journal, 7(1), 1-7. https://doi.org/10.29407/gj.v7i1.17900.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Pawening, R. E., Shudiq, W. J., & Wahyuni. (2020). Klasifikasi kualitas jeruk lokal berdasarkan tekstur dan bentuk menggunakan metode k-Nearest Neighbor (K-NN).
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • RAHAYU, T. IDENTIFIKASI DAN PREVALENSI EKTOPARASIT PADA KUCING.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
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