Classification of Hadith Topic of Indonesian Translation Using K-Nearest Neighbor and Chi-Square

Abstract

Hadith is the main way of life for Muslims besides the Qur'an whose can be applied in everyday life. Hadith also contains all the words or deeds of the Prophet Muhammad which are used as a source of the law of Islam. Therefore, many readers, especially Muslims, are interested in studying hadith. However, the large number of hadiths makes it difficult for readers or those who are still unfamiliar with Islam to read them. Therefore, we conducted a study to classify hadith textually based on the type of teaching, so that readers can get an overview or other reference in reading and searching for hadith based on the type of teaching more easily. This study uses KNN and chi-square methods as feature selection. We also carried out several test scenarios, including implementing stopword removal modifications in preprocessing and experimenting with selecting k values ​​for KNN to determine the best performance. The best performance was obtained by using the value of k = 7 on KNN without implementing chi-square and with stopword removal modification with a hammer loss value of 0.1042 or about 89.58% of the data correctly classified.

Sentiment Analysis Of Development Jakarta-Bandung High-Speed Train Using Twitter Social Media With BNN Method

“…TF-IDF (Term Frequency Inverse Document Frequency) is a weighting matrix that measures the relationship between words where the weighting process combines two parts, namely Term Frequency (TF) to count the number of occurrences of each word in the document and Inverse Document Frequency (IDF) the number of related documents containing a certain word [12]. The TF-IDF feature is one of the extraction techniques, namely by assigning a value to each word in the training data, so that we can find out how important the word that represents the sentence is, it will be given a calculation value.…”

Text Classification of Indonesian Translated Hadith Using XGBoost Model and Chi-Square Feature Selection

“…There are various principles in the Hadith that we might take to heart. These principles can be divided into three groups: the first group is recommendation, the second is prohibition, and the last group is information [3]. It is essential that we are able to recognize hadiths as well as their categories so that there are no inaccuracies when studying or reading hadith.…”

“…It is essential that we are able to recognize hadiths as well as their categories so that there are no inaccuracies when studying or reading hadith. So categorization is expected to make it efficient for us to read or study hadiths since it is important to be able to distinguish hadiths along with their categories [3].…”

“…Research on the classification of Indonesian translations of hadiths has been carried out by Ghiina Zain Nabiilah in 2021 [3]. The classification used for that research was a multi-label classification with Chi-Square feature extraction and the K-Nearest Neighbor model with an accuracy of 88.29%.…”

“…The classification model that will be used in this research is the XGBoost model which has previously been used for sentiment analysis research on Indonesian motorcycle taxi services by Sondang Tesalonika Simanjuntak in 2021 [4] which obtains an accuracy value of 92%, using the Chi-Square extraction feature which was previously also used in Indonesian translation of hadiths topic classification research by Ghiina Zain Nabiilah [3] for feature extraction in his research. By looking at the results of sentiment analysis research using XGBoost on Indonesian gojek services [4] and research on the topic classification of hadith translations using the KNN method [3], the XGBoost model is chosen as a model and Chi-Square as a feature selection because it has been proven effective and managed to get good results and optimal in previous studies. In this research, the researcher compared the accuracy of using and without using feature selection.…”