Algoritma K-Means Clustering Untuk Pengelompokan Ayat Al Quran Pada Terjemahan Bahasa Indonesia - Robani, Miftachur; Widodo, Achmad (Universitas Diponegoro, 2016)
Clustering process can make the process of grouping data so that the data in the same cluster have high similarity with the data in the same cluster. One of the clustering algorithm that is widely used is the K-Means because it has advantages such as simple, efficient, easy to understand and easy to apply. Grouping paragraph dealing with similar themes will allow users to find a theme in the Qur'an. This study aims to produce an information system that can perform grouping Quran with K-Means method. This research was conducted with a pre-processing stage process for text data, weighting by TFIDF, grouping data with K-Means clustering, labeling data for keywords. The resulting system is able to display a verse in groups associated with the keyword. The test results by using the index on the silhouette of Surah Al Fatihah generate positive value of 0.336 which means that the data in the right group, while the frequency of keywords versus the amount of data to produce a percentage of 53%, which means the keyword represents half of the data in the cluster. Tests also showed that the test results silhouette will be directly proportional to the number of clusters and inversely proportional to the number of data dimensions. To increase the value of testing required centroid method for early elections, the reduction of data dimensions and methods of measurement of distance and similarity.