Weighted inverse document frequency and vector space model for hadith search engine

Septya Egho Pratama, Wahyudin Darmalaksana, Dian Sa'adillah Maylawati, Hamdan Sugilar, Teddy Mantoro, Muhammad Ali Ramdhani

Abstract


Hadith is the second source of Islamic law after Qur’an which make many types and references of hadith need to be studied. However, there are not many Muslims know about it and many even have difficulties in studying hadiths. This study aims to build a hadith search engine from reliable source by utilizing Information Retrieval techniques. The structured representation of the text that used is Bag of Word (1-term) with the Weighted Inverse Document Frequency (WIDF) method to calculate the frequency of occurrence of each term before being converted in vector form with the Vector Space Model (VSM). Based on the experiment results using 380 texts of hadith, the recall value of WIDF and VSM is 96%, while precision value is just around 35.46%. This is because the structured representation for text that used is bag of words (1-gram) that can not maintain the meaning of text well).

Keywords


Classification; Convolutional neural network; Deep learning; Glove; Indonesian language process; Natural language processing; Text mining

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DOI: http://doi.org/10.11591/ijeecs.v18.i2.pp1004-1014
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