Author: Afrizal Nur, Mustakim, Suja’i Syarifandi, Saidul Amin

Publish: International Journal of Engineering and Advanced Technology (IJEAT)


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%.


From the discussion and analysis in this study, it can be concluded into two main parts, first, the comparison of KNN algorithms between previous research and this study has an increased of accuracy value by 0.31%, meaning that the more verses documents are used, the better the accuracy of KNN algorithm. Furthermore, the comparison between KNN, MKNN and FKNN has a high accuracy value in MKNN algorithm, which is equal to 98.12, higher than that of KNN and FKNN, 98.01% and 88.3%. But MKNN algorithm has a greater error than KNN which is 4.3% higher than KNN which is only 1.9%. Therefore, the implementation used in concluding the verses is KNN algorithm, the difference in accuracy between the best algorithms of MKNN is only 0.11% and the error ratio is better for KNN. Second, the application of KNN algorithm is applied using mobile application platform with value of blackbox testing is 100% and UAT testing is 98.8%, which means that this application is easy to use.

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