A comparison of Neural Network and SVM on the multi-label classification of Quran verses topic in English translation

Nurfikri, Fahmi Salman; Adiwijaya, Adiwijaya

doi:10.1088/1742-6596/1192/1/012030

Cited by 10 publications

(7 citation statements)

References 6 publications

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“…It is also worth to mention that, the word ‫"هللا"‬ (Allah) is always among the top-two words on both scenarios. To be precise, it is the second most central word in the first scenario (Table 4, 5,6) and the top most central in the second scenario (Table 7, 8,9). Therefore, the word ‫"هللا"‬ (Allah) is the most central in The Quran according to the centrality measure.…”

Section: Quranic Word Centralitymentioning

confidence: 98%

“…Next, the study in 2019 was using Quranic verse in English translation dataset and compare any feature selection with any machine learning methods such as naïve Bayes, SVM, and ANN. The result shows that the best hamming loss score is 0.0938 when naïve Bayes method is used, and the type of feature selection is called mutual information [6]. Another research about topic classification in Al-Baqarah, one of the chapters of The Quran, with three topics, named iman (faith), ibadah (rituals), and akhlaq (morals).…”

Section: Introductionmentioning

confidence: 99%

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Classifying Quranic Verse Topics using Word Centrality Measure

Yulianto

2021

RESTI

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Muslims believe that, as the speech of Allah, The Quran is a miracle that has specialties in itself. Some of the specialties that have studied are the regularities in the number of letters, words, vocabularies, etc. In the past, the early Islamic scholars identify these regularities manually, i.e. by counting the occurrence of each vocabulary by hand. This research tackles this problem by utilizing centrality in quranic verse topic classification. The goal of this research is to analyze the effect of The Quran word centrality measure on the topic classification of The Quran verses. To achieve this objective, the method of this research is constructing the Quran word graph, then the score of centralities included as one of the features in the verse topic classification. The effect of centrality is observed along with support vector machine (SVM) and naïve Bayes classifiers by performing two scenarios (with stopword and without stopword removal). The result shows that according to the centrality measure the word “الله” (Allah) is the most central in The Quran. The performance evaluation of the classification models shows that the use of centrality improves the hamming loss score from 0.43 to 0.21 on naïve Bayes classifier with stopword removal. Finally, both of classification method has a better performance in word graph that use stopword removal.

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Section: Quranic Word Centralitymentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Classifying Quranic Verse Topics using Word Centrality Measure

Yulianto

2021

RESTI

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“…Nurfikri & Adiwijaya [5] presents an implementation based on four steps, namely case folding, tokenization, removal of stop words, and word lemmatization, the last two steps being common in both implementations presented.…”

Section: Data Processing -Nlpmentioning

confidence: 99%

“…Regarding the application of case folding, the transformation of the text by converting all existing letters to lower case letters as well as the possible removal of punctuation characters or even numbers [5] helps in removing existing noise in the data, and this removal makes it easier to perform the following steps to be performed.…”

Section: Data Processing -Nlpmentioning

confidence: 99%

“…For the execution of this step, mention is made of two different possibilities. As a first possibility for the removal of stop words, we have the adoption of stop word dictionaries such as WordNet [5] to compare and remove these words from the data if there is a correspondence between them and the dictionary. Another option would be the adoption of a library that, using internal methods, performs this removal automatically.…”

Section: Data Processing -Nlpmentioning

confidence: 99%

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Machine Learning and Natural Language Processing in Domain Classification of Scientific Knowledge Objects: A Review

Machado

Sá

2021

Advances in Intelligent Systems and Computing

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The domain classification of scientific knowledge objects has been continuously improved over the years. Systems that can automatically classify a scientific knowledge object, through the use of artificial intelligence, machine learning algorithms, natural language processing, and others, have been adopted in most scientific knowledge databases to maintain internal classification consistency as well as to simplify the information arrangement. However, the amount of available data has grown exponentially in the last few years and now it can be found in multiple platforms under different classifications due to the implementation of different classification systems. Thus, the process of searching and selecting relevant data in research studies and projects has become more complex and the time needed to find the right information has continuously grown as well. Therefore, machine learning and natural language processing play an important role in the development and achievement of automatic and standardized classification systems that will aid researchers in their research work.

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An Ensemble Multi-label Themes-Based Classification for Holy Qur’an Verses Using Word2Vec Embedding

Mohamed

El-Behaidy

2021

Arab J Sci Eng

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A comparison of Neural Network and SVM on the multi-label classification of Quran verses topic in English translation

Cited by 10 publications

References 6 publications

Classifying Quranic Verse Topics using Word Centrality Measure

Classifying Quranic Verse Topics using Word Centrality Measure

Machine Learning and Natural Language Processing in Domain Classification of Scientific Knowledge Objects: A Review

An Ensemble Multi-label Themes-Based Classification for Holy Qur’an Verses Using Word2Vec Embedding

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