DOI: https://doi.org/10.14710/j.gauss.15.1.89-97

PERBANDINGAN PERFORMA DISTANCE MEASURES PADA ALGORITMA NEAREST CENTROID NEIGHBOR DAN K-NEAREST NEIGHBOR DALAM PROSES KLASIFIKASI BINTANG

*Dewi Sri Susanti orcid scopus  -  Department of Statistics, Universitas Lambung Mangkurat, Jl. A Yani km 36, Banjarbaru, Indonesia, Indonesia
Nurul Azizah  -  Department of Statistics, Universitas Lambung Mangkurat, Jl. A Yani km 36, Banjarbaru, Indonesia, Indonesia
Selvi Annisa scopus  -  Department of Statistics, Universitas Lambung Mangkurat, Jl. A Yani km 36, Banjarbaru, Indonesia, Indonesia
Open Access Copyright 2026 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
Stars are celestial bodies that can be classified based on several characteristics, including temperature, luminosity, radius, magnitude, stellar color, and spectral class. Stars are generally grouped into six categories: brown dwarfs, red dwarfs, white dwarfs, main sequence stars, supergiants, and hypergiants. Stellar classification is important to astronomers because it can help identify new types of stars and improve our understanding of their composition, temperature, and evolutionary stages. This classification process can be done using the Nearest Centroid Neighbor (NCN) and k-Nearest Neighbor (k-NN) algorithms, by applying various distance measures such as Euclidean, Manhattan, Minkowski, Chebyshev, Cosine, Jaccard, and Hamming. This study aims to compare the performance between NCN and k-NN using these seven distance measures. The results show that Euclidean, Manhattan, and Minkowski distances produce a perfect performance of 100% in both algorithms. Chebyshev distance yielded perfect performance in k-NN but slightly lower in NCN with a performance of 92%. Thus, the k-NN algorithm provides superior performance compared to the NCN algorithm in the stellar classification process.
Fulltext View|Download Email colleagues
Keywords: Stellar Classification; Distance Measures; Nearest Centroid Neighbor; k-Nearest Neighbor

Article Metrics:

Article Info
Section: Articles
Language : EN
  1. Chaudhuri, B. B., 1996. A new definition of neighborhood of a point in multi-dimensional space. Pattern Recognition Letters, pp. 11-17
  2. Cholil, S. R., Handayani, T., Prathivi, R. & Ardianita, T., 2021. Implementasi algoritma klasifikasi K-Nearest Neighbor (KNN) untuk klasifikasi seleksi penerima beasiswa. Indonesian Journal on Computer and Information Technology, pp. 118-127
  3. Fix, E. & Hodges, J. L., 1951. Discriminatory analysis. nonparamteric discrimination: consistency properties, Texas: USAF School of Aviation
  4. Garrison, R. F. et al., 1953. Stellar classification (classification stellaire). A Checklist of Regional Archaelogical Journals, pp. 631-647
  5. Han, J., Kamber, M. & Pei, J., 2012. Data mining concepts and techniques. Waltham: Elsevier Inc.
  6. NASA Universe Web Team, 2024. The lives, times, and deaths of stars. [Online]
  7. Available at: https://science.nasa.gov/universe/the-lives-times-and-deaths-of-stars/
  8. [Diakses 22 September 2024]
  9. Nayak, S., Bhat, M., Reddy, N. V. S. & Rao, B. A., 2022. Study of distance metrics on k-nearest neighbor algorithma for star categorization. Journal of Physics: Conference Series, pp. 1-8
  10. Noble, J., 2026. What are distance metrics?. [Online]
  11. Available at: https://www.ibm.com/think/topics/distance-metrics
  12. [Diakses April 2026]
  13. Reihanah, K. N., Maruddani, D. A. I. & Widiharih, T., 2023. Clustering karakteristik industri kecil dan menengah di Kota Kendari menggunakan algoritma k-Prototypes. JURNAL GAUSSIAN, pp. 340-351
  14. Samuels, J. I., 2024. One-hot encoding and two-hot encoding: an introduction
  15. Saputra, I. & Kristiyanti, D. A., 2022. Machine learnig untuk pemula. Jakarta: Penerbit INFORMATIKA
  16. Tamatjita, E. N. & Mahastama, A. W., 2016. Comparison of music genre classification using nearest centroid classifier and k-nearest neighbours. International Conference on Information Management and Technology (ICIMTech), pp. 118-123
  17. Utomo, S., Subroto, I. M. I. & Riansyah, A., 2022. Deteksi plagiat tugas akhir dengan metode Jaccard Similarity. Jurnal Transistor Elektro dan Informatika, pp. 132-141
  18. Wahyono, et al., 2020. Perbandingan penghitungan jarak pada k-nearest neighbour dalam klasifikasi data tekstual. Jurnal Teknologi dan Sistem Komputer, pp. 54-58
  19. Widodo, R. B., 2022. Machine learning metode k-Nearest Neigbors klasifikasi angka bahasa isyarat. Malang: Media Nusa Creative
  20. Wulandari, T. K., Oktaviani, E. D. & Lestari, A., 2022. Penerapan metode binary search dan hamming distance pada e-library SMAN 2 Katingan Hilir. KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, pp. 33-42

Last update:

No citation recorded.

Last update:

No citation recorded.