Implementation of Text Mining Classification as a Model in the Conclusion of Tafsir Bil Ma'tsur and Bil RA'YI Contents

Abstract

Studies related to Tafsir Qur'an have only been carried out based on a manual system or on application-based development. The purpose of this study is to build an application which can classify type of interpretation automatically into two classes, tafsir Bil Ma'tsur and tafsir Bil Ra'yi, can provide user convenience in the Al-Qur-an. KNN algorithm is a reliable algorithm in the classification process, also has many parts of the algorithm. This study was done by applying K-Nearest Neighbor (KNN) algorithm with an accuracy of 98.12%. However KNN had been compared firstly with Modified K-Nearest Neighbor (MKNN) and Fuzzy K-Nearest Neighbor (FKNN) algorithms, where the two algorithms had 98.01% and 88.3% accuracy respectively. MKNN is the best algorithm with the highest accuracy, but also has a high error value by 4.3% which is higher than KNN, 1.9%. From the research conducted, the more text documents used in KNN modeling, the higher accuracy will be. Therefore, in the implementation of this application KNN is used as modeling in the conclusion of Tafsir Al-Qur'an. Tests performed with BlackBox Testing and User Aceptance Test reached value of 100% and 98.8%.