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All Journal JEPA (Jurnal Ekonomi Pertanian dan Agribisnis) JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Fadilah, Zahra Rizky
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Perbandingan Metode Klasterisasi Data Bertipe Campuran: One-Hot-Encoding, Gower Distance, dan K-Prototype Berdasarkan Akurasi (Studi Kasus: Chronic Kidney Disease Dataset) Fadilah, Zahra Rizky; Wijayanto, Arie Wahyu
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5857

Abstract

This study aims to compare the one-hot-encoding method, Gower distance combined with k-means, DBSCAN, and OPTICS algorithms, and k-prototype for clustering mixed data types based on accuracy. The dataset used in this research is the chronic kidney disease (CKD) dataset sourced from the UCI Machine Learning Repository. Based on the evaluation using the silhouette index, it is found that k-prototype with the number of clusters k=2 is the most optimal clustering method because it provides the highest silhouette index value compared to the other four methods, with a value of 0,3796. Cluster 1 contains 175 observations, while cluster 2 contains 225 observations. When associated with the labels on the dataset, the clustering results provide an accuracy value of 81,25 percent.
Co-Authors Baehaqi Herida, Isra Syukria Kamal, Raihan Bariq Kusuma, Arya Candra Syifaulhaq, Annisa