The second fundamental source of law for Moslems is the Hadith. The Hadith can be used to explain Quranic texts. However, Hadith still needs to be translated according to each national language to easily understand its meaning [1]. In Indonesia Hadith more usually refers to a special class of relevance to more particular religious concern [1]. Base on that, this research will Classify the translation Hadith Text into three classes: Obligation, Prohibition, and Information. From previous research, the Back Propagation Neural Network (BPNN) showed good performance in classifying hadith text. Therefore, BPNN was used to solve the problem of hadith text classification in this study. However, the dataset has a huge number of varied bag-of-words, which are features that will be used in the classification process. Hence, Information Gain (IG) was utilized to select influential features, and as the sequential process before the classification process. To measure the performance of this system, the Macro F1-Score was used. The F1-Score enables one to observe exactness from precision and completeness from recall. The Macro F1-score is also needed for the performance evaluation of more than two classes. Based on the experiment conducted, the system was able to classify hadith text using BPNN, IG, and without stemming, yielding the highest F1-score of 84.63%. However, the system performance that included the stemming process yielded an F1-score of 80.92%. This shows that the stemming process could decrease classification performance. This decreasing performance is due to some influential words merging with more noninfluential words.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.