ANALISIS PERBANDINGAN ALGORITMA K-MEANS DAN K-MEDOIDS UNTUK KLUSTERISASI TEKS ULASAN PADA APLIKASI COOKPAD: COMPARATIVE ANALYSIS OF K-MEANS AND K-MEDOIDS ALGORITHMS FOR CLUSTERIZATION OF REVIEW TEXTS ON THE COOKPAD APPLICATION - Rifka Khairuna; Nurdin; Ar Razi (LPPM Universitas Abdurrab, 2025)
This study aims to analyze and compare the performance of the K-Means and K-Medoids algorithms in clustering Cookpad application user reviews obtained directly from the Google Play Store. A total of 15,330 reviews were collected as a representation of authentic user opinions. The review text was first processed through a pre-processing stage, then converted into a numeric representation using the TF-IDF method. The clustering process was carried out using the K-Means and K-Medoids algorithms, which were then evaluated using the Silhouette Score metric, both on the original data and after removing outliers with the DBSCAN algorithm. The results showed that K-Medoids performed better with a score of 0.2788 (with outliers) and 0.2966 (without outliers), compared to K-Means which obtained a score of 0.2625 (with outliers) and 0.2795 (without outliers). The clusters formed were dominated by positive reviews with words such as