Clustering Kanker Serviks Berdasarkan Perbandingan Euclidean dan Manhattan Menggunakan Metode K-Means
DOI:
https://doi.org/10.30865/mib.v5i2.2947Keywords:
Cervix, K-Means, Euclidean, Manhattan, ClusterAbstract
K-means a fairly simple and commonly used cluster of clusters to partition datasets into multiple clusters. Distance calculations are used to find similar data objects that lead to developing powerful algorithms for datamining such as classification and grouping. Some studies apply k-means algorithms using distance calculations such as Euclidean, Manhattan and Minkowski. The study used datasets from gynecological patients with a total of 401 patients examined and as many as 205 patients detected cervical cancer, while 196 other patients did not have cervical cancer. The results were shown with the help of confusion matrix and ROC curve, accuracy value obtained by 79.30% with ROC 79.17% on K-Means Euclidean Metric while K-Means Manhattan Metric by 67.83% with ROC 65.94%. Thus it can be concluded that the Euclidean method is the best method to be applied in the K-Means Clustering algorithm on cervical cancer datasets.References
B. Budiman, Y. Mulyana Hidayat, and A. Budi Harsono, “Evaluasi Program Deteksi Dini Kanker Serviks dengan Metode See and Treat di Kabupaten Karawang,†Indones. J. Obstet. Gynecol. Sci., vol. 2, no. 1, pp. 72–80, 2019, doi: 10.24198/obgynia.v2n1.77.
A. Rosiana and N. Tiara, “Pengaruh Psikoedukasi Keluarga Terhadap Kemampuan Perawtan Kebersihan Diri Pada anak Retardasi Mental Di SDLB P urwosari Kudus Tahun 2015,†Indones. J. Perawat, vol. 2, no. I, pp. 50–56, 2017, [Online]. Available: https://jurnal.ugm.ac.id/buletinpsikologi/article/view/12679.
P. A. Cohen, A. Jhingran, A. Oaknin, and L. Denny, “Cervical cancer,†Lancet, vol. 393, no. 10167, pp. 169–182, 2019, doi: 10.1016/S0140-6736(18)32470-X.
C.-J. Chen et al., Epidemiology of Virus Infection and Human Cancer BT - Viruses and Human Cancer: From Basic Science to Clinical Prevention. 2021.
S. Rio, E. Sri, and T. Suci, “Persepsi tentang Kanker Serviks dan Upaya Prevensinya pada Perempuan yang Memiliki Keluarga dengan Riwayat Kanker,†J. Kesehat. Reproduksi, vol. 4, no. 3, pp. 159–169, 2017, doi: 10.22146/jkr.36511.
D. Makassari, “Sebaran Kanker di Indonesia, Riset Kesehatan Dasar 2007,†Indones. J. Cancer, vol. 11, no. 29, pp. 1–8, 2017, [Online]. Available: https://media.neliti.com/media/publications/197251-ID-sebaran-kanker-di-indonesia-riset-keseha.pdf.
I. H. Witten, Data Mining (Fourth Edition). 2017.
A. Javed, B. S. Lee, and D. M. Rizzo, “A benchmark study on time series clustering,†arXiv, vol. 1, no. June, p. 100001, 2020, doi: 10.1016/j.mlwa.2020.100001.
N. Nidheesh, K. A. Abdul Nazeer, and P. M. Ameer, “An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data,†Comput. Biol. Med., vol. 91, pp. 213–221, 2017, doi: 10.1016/j.compbiomed.2017.10.014.
S. Kapil and M. Chawla, “Performance Evaluation of K-means Clustering Algorithm with Various Distance Metrics,†1 st IEEE Int. Conf. Power Electron. Intell. Control Energy Syst., vol. 110, no. 11, pp. 12–16, 2016, doi: 10.5120/19360-0929.
D. P. P. Mesquita, J. P. P. Gomes, A. H. Souza Junior, and J. S. Nobre, “Euclidean distance estimation in incomplete datasets,†Neurocomputing, vol. 248, pp. 11–18, 2017, doi: 10.1016/j.neucom.2016.12.081.
M. Nishom, “Perbandingan Akurasi Euclidean Distance, Minkowski Distance, dan Manhattan Distance pada Algoritma K-Means Clustering berbasis Chi-Square,†J. Inform. J. Pengemb. IT, vol. 4, no. 1, pp. 20–24, 2019, doi: 10.30591/jpit.v4i1.1253.
D. T. Larose and C. D. Larose, Data Mining and Predictive Analytics, Second. New Jersey: Wiley, 2015.
C. Yuan and H. Yang, “Research on K-Value Selection Method of K-Means Clustering Algorithm,†J, vol. 2, no. 2, pp. 226–235, 2019, doi: 10.3390/j2020016.
J. L. Suárez, S. GarcÃa, and F. Herrera, “A tutorial on distance metric learning: Mathematical foundations, algorithms, experimental analysis, prospects and challenges,†Neurocomputing, vol. 425, pp. 300–322, 2021, doi: 10.1016/j.neucom.2020.08.017.
G. Florin, Data Mining Concepts,Models and Techniques. Berlin: Springer, 2011.
Downloads
Published
Issue
Section
License
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).
