Klasifikasi Kanker Payudara Menggunakan Algoritma K-Nearest Neighbor dan Metode Naive Bayes
DOI:
https://doi.org/10.54259/satesi.v4i2.3167Keywords:
Data Mining, Classification, Naïve Bayes, K-NNAbstract
Breast cancer is one of the diseases that causes death and is one of the most frightening leading causes worldwide. This disease falls under the category of highly dangerous cancers, ranking second after lung cancer. Breast cancer cases occur in large numbers across various regions of the world, raising significant concerns globally. Breast cancer not only affects the quality of life of patients but also contributes significantly to the global cancer mortality rate. It ranks as the fifth leading cause of cancer-related deaths, accounting for approximately 16.6% of the total cancer deaths worldwide. In this study, a classification of blood sample data from breast cancer patients was conducted. Various classification techniques and methods were applied, including the K-Nearest Neighbor (KNN) method and Naïve Bayes (NB). To achieve accurate results, this study tested accuracy using Cross Validation techniques and a Confusion Matrix to evaluate the test data. Of the total 569 data points collected, 70% were used as training data, amounting to 398 data points, while the remaining 30%, or 171 data points, were used as test data. The results of this study showed that the Naïve Bayes method produced an accuracy rate of 96%, with a precision of 94% and a recall of 91%. On the other hand, the K-Nearest Neighbor method yielded a lower accuracy rate of 73%, with a precision of 74% and a recall of 66%, using K=7.
Downloads
References
N. R. Muntiari and K. H. Hanif, “Klasifikasi Penyakit Kanker Payudara Menggunakan Perbandingan Algoritma Machine Learning,” J. Ilmu Komput. dan Teknol., vol. 3, no. 1, pp. 1–6, 2022, doi: 10.35960/ikomti.v3i1.766.
J. T. Wijaya, H. Oktavianto, H. Azizah, and A. Faruq, “Perbandingan Algoritma K-Nearest Neighbor (Knn) Dan Gaussian Naive Bayes (Gnb) Dalam Klasifikasi Breast Cancer Coimbra Comparison Between K-Nearest Neighbor (Knn) And Gaussian Naive Bayes (Gnb) Algorithm In The Coimbra Breast Cancer Classification,” J. Smart Teknol., vol. 3, no. 3, pp. 2774–1702, 2022, [Online]. Available: http://jurnal.unmuhjember.ac.id/index.php/JST
H. Harafani and H. A. Al-Kautsar, “Meningkatkan Kinerja K-Nn Untuk Klasifikasi Kanker Payudara Dengan Forward Selection,” J. Pendidik. Teknol. dan Kejuru., vol. 18, no. 1, p. 99, 2021, doi: 10.23887/jptk-undiksha.v18i1.29905.
L. W. Astuti, I. Saluza, F. Faradilla, and M. F. Alie, “Optimalisasi Klasifikasi Kanker Payudara Menggunakan Forward Selection pada Naive Bayes,” J. Ilm. Inform. Glob., vol. 11, no. 2, 2021, doi: 10.36982/jiig.v11i2.1235.
I. N. Atthalla, A. Jovandy, and H. Habibie, “Klasifikasi Penyakit Kanker Payudara Menggunakan Metode K Nearest Neighbor,” Pros. Annu. Res. Semin., vol. 4, no. 1, pp. 148–151, 2018.
R. Hidayat, D. Kartini, M. I. Mazdadi, I. Budiman, and R. Ramadhani, “Implementasi Seleksi Fitur Binary Particle Swarm Optimization pada Algoritma K-NN untuk Klasifikasi Kanker Payudara,” J. Sist. dan Teknol. Inf., vol. 11, no. 1, p. 135, 2023, doi: 10.26418/justin.v11i1.53608.
H. Oktavianto and R. P. Handri, “Analisis Klasifikasi Kanker Payudara Menggunakan Algoritma Naive Bayes,” INFORMAL Informatics J., vol. 4, no. 3, p. 117, 2020, doi: 10.19184/isj.v4i3.14170.
B. Aisyah and Y. Sulistyo, “Klasifikasi Kanker Payudara Menggunakan Algoritma Gain Ratio,” J. Tek. Elektro, vol. 8, no. 2, pp. 43–46, 2016.
Fahrurrozi and Wasilah, “Deteksi Dini Kanker Payudara Menggunakan Algoritma K-Nearest Neighbor (KNN) Dan Decision Tree C-45,” Teknika, vol. 17, no. 2, pp. 427–434, 2023, [Online]. Available: https://jurnal.polsri.ac.id/index.php/teknika/article/view/7565
J. KUSUMA, B. H. HAYADI, W. WANAYUMINI, and R. ROSNELLY, “Komparasi Metode Multi Layer Perceptron (MLP) dan Support Vector Machine (SVM) untuk Klasifikasi Kanker Payudara,” MIND J., vol. 7, no. 1, pp. 51–60, 2022, doi: 10.26760/mindjournal.v7i1.51-60.
F. S. Nugraha, M. J. Shidiq, and S. Rahayu, “Analisis Algoritma Klasifikasi Neural Network Untuk Diagnosis Penyakit Kanker Payudara,” J. Pilar Nusa Mandiri, vol. 15, no. 2, pp. 149–156, 2019, doi: 10.33480/pilar.v15i2.601.
A. Nugraheni, R. D. Ramadhani, A. B. Arifa, and A. Prasetiadi, “Perbandingan Performa Antara Algoritma Naive Bayes Dan K-Nearest Neighbour Pada Klasifikasi Kanker Payudara,” J. Dinda Data Sci. Inf. Technol. Data Anal., vol. 2, no. 1, pp. 11–20, 2022, doi: 10.20895/dinda.v2i1.391.
M. Abdul Jabbar, E. Hasmin, C. Susanto, W. Musu, and I. Artikel, “Komparasi Algoritma Decision Tree, Naive Bayes, dan K-Nearest Neighbors dalam Klasifikasi Kanker Payudara Comparison of Decision Tree Algorithms, Naive Bayes, and K-Nearest Neighbors in Breast Cancer Classification,” Oktober, vol. 14, no. 3, pp. 258–270, 2022, [Online]. Available: https://www.doi.org/10.22303/csrid.14.3.2022.258-270
T. A. Yoga and Prihandoko, “Penerapan Optimasi Berbasis Particle Swarm Optimization (Pso) Algoritma Naïve Bayes Dan K-Nearest Neighbor Sebagai Perbandingan Untuk Mencari Kinerja Terbaik Dalam Mendeteksi Kanker Payudara,” J. Bangkit Indones., vol. 7, no. 2, p. 1, 2018, [Online]. Available: http://journal.universitasmulia.ac.id/index.php/metik/article/view/62
M. Alwohaibi, M. Alzaqebah, N. M. Alotaibi, A. M. Alzahrani, and M. Zouch, “A hybrid multi-stage learning technique based on brain storming optimization algorithm for breast cancer recurrence prediction,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 5192–5203, 2022, doi: 10.1016/j.jksuci.2021.05.004.
Y. Feng et al., “Predicting breast cancer-specific survival in metaplastic breast cancer patients using machine learning algorithms,” J. Pathol. Inform., vol. 14, no. August, p. 100329, 2023, doi: 10.1016/j.jpi.2023.100329.
S. Kumari, D. Kumar, and M. Mittal, “An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier,” Int. J. Cogn. Comput. Eng., vol. 2, no. November 2020, pp. 40–46, 2021, doi: 10.1016/j.ijcce.2021.01.001.
M. Bansal, A. Goyal, and A. Choudhary, “A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning,” Decis. Anal. J., vol. 3, no. May, p. 100071, 2022, doi: 10.1016/j.dajour.2022.100071.
V. Jaiswal, P. Saurabh, U. K. Lilhore, M. Pathak, S. Simaiya, and S. Dalal, “A breast cancer risk predication and classification model with ensemble learning and big data fusion,” Decis. Anal. J., vol. 8, no. April, p. 100298, 2023, doi: 10.1016/j.dajour.2023.100298.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Tresi Aprilia
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
