EARLY DETECTION OF BREAST CANCER USING THE K-NEAREST NEIGHBOUR (K-NN) ALGORITHM

Authors

  • Refli Tiarma Ariani Panggabean Dian Nuswantoro University
  • Ledy Octavia Dian Nuswantoro University
  • Noormala Dwi Dian Nuswantoro University
  • Aripin - Dian Nuswantoro University

DOI:

https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v6i2.3194

Abstract

ABSTRACT- Cancer is one of the Non-Communicable Disease groups whose growth and development are high-speed. One type of cancer is breast cancer (carcinoma mammae). Breast cancer is the leading cause of death for women. The first breast cancer cells can grow into tumors as large as 1 cm, spanning 8-12 years. The prevalence rate of breast cancer in Indonesia is 50 per 100,000 female population. The method used in this study uses the K-Nearest Neighbor (K-NN) algorithm by comparing k values, namely 3, 5, and 7. The dataset used was obtained from the UCI Machine Learning Repository with the Number of datasets after preprocessing, namely 653 data with a class consisting of benign tumors (benign) and malignant tumors (malignant). The variables used in this study take into account the variables of clump thickness, cell size uniformity, cell shape uniformity, marginal adhesion, single epithelial cell size, cell nucleus size, chromatin, normal cell nucleus, and mitosis. The results of the most influential classification for training and testing are using k = 3 with an accuracy of training and testing at a proportion of 70:30 of 83.8074% and 75%; the ratio of 80:20 is 84.6743% and 74.8092%; the percentage of 90:10 is 84.0136% and 84.6154%. Using the value of k = 3, the resulting gap between training and testing is similar.

Downloads

Published

2023-02-09

How to Cite

[1]
R. T. A. Panggabean, L. Octavia, N. Dwi, and A. -, “EARLY DETECTION OF BREAST CANCER USING THE K-NEAREST NEIGHBOUR (K-NN) ALGORITHM”, JUSIKOM PRIMA, vol. 6, no. 2, pp. 38-42, Feb. 2023.

Issue

Vol. 6 No. 2 (2023): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM Prima)

Section

Articles

License

Copyright (c) 2023 Refli Tiarma Ariani Panggabean, Ledy Octavia, Noormala Dwi, Aripin -

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish their manuscripts through the Journal of Information Systems and Computer Science agree to the following:

  • Copyright to the manuscripts of scientific papers in this Journal is held by the author.
  • The author surrenders the rights when first publishing the manuscript of his scientific work and simultaneously the author grants permission / license by referring to the Creative Commons Attribution-ShareAlike 4.0 International License to other parties to distribute his scientific work while still giving credit to the author and the Journal of Information Systems and Computer Science as the first publication medium for the work.
  • Matters relating to the non-exclusivity of the distribution of the Journal that publishes the author's scientific work can be agreed separately (for example: requests to place the work in the library of an institution or publish it as a book) with the author as one of the parties to the agreement and with credit to sJournal of Information Systems and Computer Science as the first publication medium for the work in question.
  • Authors can and are expected to publish their work online (e.g. in a Repository or on their Organization's/Institution's website) before and during the manuscript submission process, as such efforts can increase citation exchange earlier and with a wider scope.