Optimasi K-means Clustering Dengan Menggunakan Particle Swarm Optimization Untuk Menentukan Jumlah Cluster Pada Kanker Serviks

 (*)Indrawan Setiaji Mail (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Affandy Affandy (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Ahmad Zainul Fanani (Universitas Dian Nuswantoro, Semarang, Indonesia)

(*) Corresponding Author

Submitted: June 8, 2023; Published: July 31, 2023

Abstract

Cervical cancer is one of the most common cancers among women in the world. It is most common in developing countries. Cervical cancer develops slowly in the body. Clustering is needed so that cervical cancer can be treated quickly. The K-means method was chosen because of its ability to group large amounts of data and fast computation time. The K-means method is also very easy to implement, flexible, and uses simple principles, which can be explained non-statistically. The many advantages that K-means has, also has weaknesses because it uses random clustering numbers and the results are not optimal. The difficulty in accurately determining the amount of clustering in the dataset. The K-means method cannot provide an optimal solution for determining the number of clustering, so it needs to be improved in order to obtain an optimal solution. PSO was chosen because it has several advantages, namely requiring few parameters, easy to implement, fast convergence, more efficient because it requires little computation and is simple. The results showed that the PSO - K-means method can prove to provide a significant contribution by directly obtaining optimum clustering results without having to do repeated experiments with a Silhouette Coefficient value of 0.83 and a Davies Bouldien Index value of 1.91.

Keywords


PSO-K-Means; K-Means; Number of Clustering; Clustering; Particle Swarm Optimization; Silhouette Coefficients; Davies Bouldin Index

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