Classification of Quranic topics based on imbalanced classification

Arkok, Bassam; Zeki, Ahmed M.

doi:10.11591/ijeecs.v22.i2.pp678-687

Cited by 6 publications

(7 citation statements)

References 21 publications

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“…Within this type of research, the researchers can compare two or more algorithm to extract the most valuable information inside the Quran. Several text mining studies on Quran explored and analyzed the classification of its content as reported by [12]- [16]. Another type of research in Quran text mining is focus in the specific dataset and analyzing the text mining result acted to the datasets, which are Indonesian Tafseer and Translation.…”

Section: Literature Reviewmentioning

confidence: 99%

Text mining approaches for analyzing an Indonesian tafseer and translation of the holy Quran

Ayu

Irawan

Mantoro

2022

IJEECS

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<span lang="EN-US">The Indonesian tafseer and translation of Holy Quran is an important source of information and knowledge for Indonesian muslims, since not many Indonesian muslims understand Arabic language in the Quran. However, the tafseer is full of the commentaries and explanation of each surah (chapter) and/or ayah (verse), which form a large document and not so easy to be accessed. Thus, the challenge is how to refer to both tafseer and translation in faster and accurate ways as one needs to always refer to them back and forth. Hence, this study proposes several text mining approaches, i.e. most frequent words, K-means clustering, and association rules, to analyze an Indonesian tafseer and translation of Quran and provide insights of hidden knowledge and relationships based on statistical information derived from it. These insights could be useful for muslims in general and for people that doing research in related areas. This study shows interesting results from combined analysis of the approaches used which can help people accessing information in tafseer more efficiently. As well, interesting relationships have been drawn from terms in the tafseer which could provide further and deeper knowledge on messages in the Quran.</span>

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Section: Literature Reviewmentioning

confidence: 99%

Text mining approaches for analyzing an Indonesian tafseer and translation of the holy Quran

Ayu

Irawan

Mantoro

2022

IJEECS

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“…The GOS algorithm was evaluated on 8 binary Quranic datasets with different imbalance ratios. The Quranic datasets were applied by other studies [9][10][11] that were extracted from the Quranic Index collected by Dr Abu Akhir [12]. Table I presents these datasets with their imbalanced ratios and the number of verses in the majority and minority classes.…”

Section: A Datasetsmentioning

confidence: 99%

GOS: A Genetic OverSampling Algorithm for Classification of Quranic Verses

Arkok

Zeki

2022

2022 13th International Conference on Information and Communication Systems (ICICS)

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Imbalanced classes problem is a problem in many datasets in real applications, where one class "minority class" contain few numbers of samples and the other "majority class" contain many numbers of samples. It is difficult to build a training model to classify the imbalanced classes correctly due to tending the accuracy of classification to the majority class. In this paper, a new technique is called "GOS: a Genetic OverSampling algorithm", is proposed using a genetic algorithm. A genetic algorithm is applied to oversample the imbalanced datasets and to improve the performance of imbalanced classification. This improvement is achieved due to adjusting the locations of samples in the minority class in the optimal places. According to the experimental results obtained, the GOS algorithm outperformed other techniques used widely in the imbalanced classification field.

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“…Tabel 6. Analisis Metode Representasi Teks dalam Arabic Natural Language Processing Metode Untuk Representasi Teks Peneliti Jumlah TF-IDF [4], [9], [48], [30], [23], [24], [29], [53], [54], [7], [38], [55], [32], [56], [25], [31], [59], [1], [51], [60] 20 Word2Vec [34], [24], [49], [7], [57], [39], [41], [43], [45], [46] 10 AraVec [34], [35], [37], [40], [41], [42] 6 FastText [34], [47], [35], [57], [41], [42], [50], [46] 8 mBERT [27], [22], [41], [46] 4 AraBERT [36],…”

Section: Tahap Pembuatan Rencana Awalmentioning

confidence: 99%

“…Untuk lebih mempertimbangkan konteks kata dan makna kalimat dapat diatasi dengan dynamic atau contextual embedding yang mempelajari representasi kalimat secara universal [27]. Contextual embedding yang pertama kali diusulkan adalah Embeddings from Language Model (ELMO) [68] [33], [34], [47], [48], [30], [23], [24], [54], [38], [27], [56], [31], [57], [39], [1], [42], [51], [46], [9] 20…”

Section: Tahap Pembuatan Rencana Awalunclassified

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Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Ariyanto

fatichah²,

Arifin³

2021

RESTI

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Hadith is the second source of reference for Islamic law after the Qur'an, which explains the sentences in the Qur'an which are still global by referring to the provisions of the Prophet Muhammad SAW. Classification of text documents can also be used to overcome the problem of interrelation between the Qur'an and hadith. The problem of interrelation between books of hadith needs to be done because some hadiths in certain hadith books have the same meaning as other hadith books. This study aims to analyze the development of text representation and classification methods suitable to overcome similarity meaning problems in detecting interrelationships between hadith books. The research method used is Systematic Literature Review (SLR) sourced from Google Scholar, Science Direct, and IEEE. There are 42 pieces of literature that have been studied successfully. The results showed that contextual embedding as the newest text representation method considered word context and sentence meaning better than static embedding. As a classification method, the ensemble method has better performance in classifying text documents than using only a single classifier model. Thus, future research can consider using a combination of contextual embedding and ensemble methods to detect interrelationships between books of hadith.

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Classification of Quranic topics based on imbalanced classification

Cited by 6 publications

References 21 publications

Text mining approaches for analyzing an Indonesian tafseer and translation of the holy Quran

Text mining approaches for analyzing an Indonesian tafseer and translation of the holy Quran

GOS: A Genetic OverSampling Algorithm for Classification of Quranic Verses

Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

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