Flexibility of Indonesian text pre-processing library

Maylawati, Dian Sa’adillah; Aulawi, Hilmi; Ramdhani, Muhammad Ali

doi:10.11591/ijeecs.v13.i1.pp420-426

Cited by 14 publications

(4 citation statements)

References 22 publications

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“…Test pre-processing is an important phase in Text Mining to prepare text data well befor conducting the mining process [41,42], among others tokenizing, casefolding, cleaning text data, stopwords removal and stemming. Tokenizing and casefolding prepare text data to be easy to change into structured representation with specific and uniform term.…”

Section: Text Pre-processingmentioning

confidence: 99%

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

Pratama

Darmalaksana

Maylawati

et al. 2020

IJEECS

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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).

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Section: Text Pre-processingmentioning

confidence: 99%

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

Pratama

Darmalaksana

Maylawati

et al. 2020

IJEECS

Self Cite

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“…In the Implementation phase, to build the chatbot there are several text pre-processing that conducted before running MNB and RAKE algorithm. Text data is unstructured data that must be transformed into structured data, so that the text pre-processing is an importan phase to get good result [37], [38]. Text pre-processing means to prepare initial text data that is still various to be used as regular data that can be subjected to or applied by several existing text mining methods [39].…”

Section: Fig 2 Work Flow Of Chatbot In Quetion Answering Systemmentioning

confidence: 99%

Multinomial Naïve Bayes and Rapid Automatic Keywords Extraction for Taharah (Purify) Law Chatbot

Muhtar¹,

Gerhana²,

Maylawati³

et al. 2020

Proceedings of the 1st International Conference on Islam, Science and Technology, ICONISTECH 2019, 11-12 July 2019, Bandung, In

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“…Text Pre-processing is an important phase in natural language processing [85], [86]. Generally, text pre-processing prepares Indonesian document corpus with several process, among others tokenizing each paragraph and each sentence, lowering case, removing character non-letter and regular expression, removing Indonesian stopwords (remove unuseful words), and stemming using Porter algorithm for Indonesian language (where stemming process is the change of words with affixes into its basic words).…”

Section: Structured Representation For Textmentioning

confidence: 99%

Sequential Pattern Mining and Deep Learning to Enhance Readability of Indonesian Text Summarization

Maylawati¹

2019

IJATCSE

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Readability is a big challenge that must be solved in automatic text summarization research. The aim of this study is to comprehensively and systematically investigate the literatures that are related with text summarization needs, in particular, to prepare efficient algorithm for Indonesian text using Deep Learning and Sequential Pattern Mining. Evidence from previous literatures shows that there are several studies which use Sequential Pattern Mining to extract representation of text for document clustering and classification of Indonesian text. However, not much attention was given to text representation in document summarization, specifically for Indonesian language. As readability is a major concern in the text summarization community, determining a better text representation to maintain the meaning of the generated text summary is deemed necessary. This paper gives an opportunity to take a deeper look into how to design an efficient and effective text representation which can further enhance text summarization readability. Besides the general systematic literature review, we discuss an idea to combine Deep Learning and Sequential Pattern Mining to improve readability of summary result to develop in the future.

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Flexibility of Indonesian text pre-processing library

Cited by 14 publications

References 22 publications

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

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

Multinomial Naïve Bayes and Rapid Automatic Keywords Extraction for Taharah (Purify) Law Chatbot

Sequential Pattern Mining and Deep Learning to Enhance Readability of Indonesian Text Summarization

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