Open AccessEvaluation of TF-IDF Algorithm Weighting Scheme in The Qur'an Translation Clustering with K-Means Algorithm
Author(s) -
Mochamad Wahyudi
Publication year - 2021
Publication title -
jitecs (journal of information technology and computer science)
Language(s) - English
Resource type - Journals
eISSN - 2540-9824
pISSN - 2540-9433
DOI - 10.25126/jitecs.202162295
Subject(s) - weighting , algorithm , tf–idf , cluster analysis , normalization (sociology) , computer science , standard deviation , mathematics , statistics , artificial intelligence , physics , sociology , term (time) , quantum mechanics , acoustics , anthropology
The Al-Quran translation index issued by the Ministry of Religion can be used in text mining to search for similar patterns of Al-Quran translation. This study performs sentence grouping using the K-Means Clustering algorithm and three weighting scheme models of the TF-IDF algorithm to get the best performance of the Tf-IDF algorithm. From the three models of the TF-IDF algorithm weighting scheme, the highest percentage results were obtained in the traditional TF-IDF weighting scheme, namely 62.16% with an average percentage of 36.12% and a standard deviation of 12.77%. The smallest results are shown in the TF-IDF 1 normalization weighting scheme, namely 48.65% with an average percentage of 25.65% and a standard deviation of 10.16%. The smallest standard deviation results in a normalized 2 TF-IDF weighting of 8.27% with an average percentage of 28.15% and the largest percentage weighting of 48.65% which is the same as the normalized TF-IDF 1 weighting.