%0 Thesis
%9 Skripsi
%A AHMAD SALAM WAHID FAIZIN, NIM. 12650026
%B FAKULTAS SAINS DAN TEKNOLOGI
%D 2018
%F digilib:31679
%I UIN SUNAN KALIJAGA YOGYAKARTA
%K Clustering, K-Means Clustering, Al-Qur’an, Text Mining
%T IMPLEMENTASI K-MEANS CLUSTERING PADA TERJEMAHAN AL-QUR’AN BERDASARKAN KETERKAITAN TOPIK
%U https://digilib.uin-suka.ac.id/id/eprint/31679/
%X The clustering process can perform grouping of data, so data which have high simmilarity will be grouped into the same cluster. One of the most commonly used clustering algorithms is K-Means. Grouping related paragraph will allow the user to find a theme in the Qur’an. This study aims to see the accuracy of the K-Means algorithm for clustering the verses of the Qur’an This research was conducted with pre-processing text of Qur’an verse, term-weighting with TF-IDF, normalize using cosine normalization, and then clustering using K-Means algorithm. Based on the test result using K-Means algorithm successfully perform clustering on Al-Baqarah verses with accuracy of 43%. To increase the value of testing required centroid selection algorithms for initial values, reduction of data dimensions, and algoritms for distance measurement and similiarity.
%Z M. Didik Rohmad Wahyudi, ST., MT