Comparison of Decision Tree C4.5 Algorithm with K-Nearest Neighbor (KNN) Algorithm in Hadith Classification

Abstract

Previous scholars always made an effort to make various formulations that were used to categorize and calcify hadith. At present, the process of categorization or classification is facilitated by the process of text mining technology. In the study of text mining itself, there are various kinds of tools and methods or algorithms that can be used and also help provide maximum results in the process of mining information from a text. An example is the Decision Tree C4.5 and K-Nearest Neighbor algorithm. Based on that, the author wants to make research and this final project to compare the performance resulting from the classification process of text documents using Decision Tree C4.5 and K-Nearest Neighbor algorithm for the classification of Imam At- Tirmidzi hadith. With this research, it is expected to be knowledgeable about the process of classifying text documents along with the performance of the two algorithms. Based on testing that has been done, the Decision Tree C4.5 algorithm produces an average accuracy value of 70.53% with an average processing time of 0.083 seconds. While the K-Nearest Neighbor algorithm produces an average accuracy value of 66.36% with an average processing time of 0,03 seconds.