K-means variations analysis for translation of English Tafseer Al-Quran text

Ahmed, Mohammed A.; Baharin, Hanif; Nohuddin, Puteri N. E.

doi:10.11591/ijece.v13i3.pp3255-3265

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…According to the studies [26], [29], [30], [43], chapter Al-Baqarah can be clustered using , hence the study experments applied this value for the all five translotor (TR1-TR5). Table 3 shown k-means algorithm results of internal validation metrics (SC, DBI) and the execution time (ET) in addition to features statistics.…”

Section: A Text Cleansing and Internal Validation Metrics Resultsmentioning

confidence: 99%

Prioritise Five Tafseer Translators Using Clustering Technique for Surah Al-Baqarah

2024

IJSER

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The English Tafseer Translation of the Holy Quran is essential for comprehending and interpreting Allah"s words for non-Arabic Muslims. This research adopted five different English translators (TR1-TR5) of chapter (Surah) Al-Baqarah and invested the advantages of the text clustering process to rank (prioritise) between these input five datasets. The absence of dataset ground truth (not standard datasets) requires the use of unsupervised learning (clustering technique) instead of other techniques (e.g. classification (supervised learning)). This study expanded the assessment to include both partitioning-based and hierarchical-based clustering algorithms. In a cluster based on partitioning, k-means is utilized. While for the hierarchical-based, the Agglomerative has been implemented. This research"s aim was achieved through a three-step procedure (stages). The first stage uses text cleansing to remove unnecessary words (Tokenisation, POS tagging, normalisation, stemming, and Stop-word removal). In addition, feature selection used VSM (Vector Space Model) and TF-IDF (Term Frequency-Inverse Document Frequency) to make the five corpora. The second stage implemented the clustering process. In the third stage, clustering validation was obtained using SC (Silhouette Coefficient) and DBI (Davies-Bouldin Index) metrics plus the execution time (ET). Principle Component Analysis (PCA) is used to visualise the clustering outputs. The results show, based on (ET, SC, and DBI) of the k-means algorithm, only ranks (1) and (3) demonstrate the same ranking for these five translators. In contrast, the Agglomerative algorithm shows the same five translators" positions; each (ET, SC, and DBI) has a distinct rank. However, to obtain the optimal union rank, it is crucial to use a modern approach technique such as MCDM (Multi-Criteria Decision-making Analysis) in future work.

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Section: A Text Cleansing and Internal Validation Metrics Resultsmentioning

confidence: 99%

Prioritise Five Tafseer Translators Using Clustering Technique for Surah Al-Baqarah

2024

IJSER

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“…K-means digunakan untuk melakukan clustering data, setiap data point kemudian diassign ke cluster yang memiliki centroid terdekat berdasarkan suatu metrik jarak, yang biasanya adalah jarak Euclidean. Dengan kata lain, setiap data point akan diatributkan ke cluster yang memiliki centroid paling dekat [10]. 1.…”

Section: K-meansunclassified

Analisis Klasterisasi Untuk Prediksi Jumlah Kasus DBD Berdasarkan Jenis Kelamin Dan Kabupaten/Kota Di Jawa Barat

Ikbal,

Irma Purnamasari,

Ali

2024

jati

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Penyakit Demam Berdarah Dengue (DBD) adalah penyakit menular yang sering fatal, terutama di daerah tropis dan subtropis, menjadi isu kesehatan global dengan penyebaran luas dan dampak serius. Lingkungan memiliki peran penting dalam kondisi ini. Penelitian ini menggunakan metode k-means, naive bayes, dan linear regresi, masing-masing memiliki fungsinya sendiri. K-means berhasil mengelompokkan kasus DBD menjadi 3 cluster (tinggi, sedang, rendah) dengan indeks kinerja Davies Bouldin rata-rata 0.71. Naive bayes digunakan untuk memprediksi hasil cluster 2023 dengan akurasi model 88.27%. Linear regresi untuk menentukan jumlah kasus DBD tahun 2023, dengan kasus tertinggi Kota Bandung 1590 laki laki, 1585 perempuan dan Kota Bekasi 1576 laki-laki dan 1572 perempuan, Kota Depok masuk kategori sedang 948 laki-laki dan 944 perempuan, 24 kota lainnya masuk dalam kategori rendah. Penyebaran DBD cenderung lebih tinggi pada kaum laki-laki, mencapai puncak tertinggi pada tahun 2022. Analisis data kasus DBD dapat memberikan informasi dan kontribusi penting untuk pembangunan strategi pencegahan dan penanggulangan yang lebih efektif oleh masyarakat, pemerintah, dan dinas kesehatan.

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“…The Qur'an contains important teachings that enable Muslims to follow and adhere to Islamic principles correctly [1]. Categorizing the verses of the Qur'an is an important aspect of its study [2]. This classification concept, as explained by Hasyim, is identified in research by M. Fauzan et al to cover various themes such as Tauhid, Worship, Muamalah, ethics, Tarikh (History), and Sharia in the teachings of the Qur'an [3].…”

Section: Introductionmentioning

confidence: 99%

Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText

Muslikh,

Akbar,

Setiadi

et al. 2024

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Studying the Qur'an is a pivotal act of worship in Islam, which necessitates a structured understanding of its verses to facilitate learning and referencing. Reflecting this complexity, each Quranic verse is rich with unique thematic elements and can be classified into a range of distinct categories. This study explores the enhancement of a multi-label classification model through the integration of FastText. Employing a CNN+Bi-LSTM architecture, the research undertakes the classification of Quranic translations across categories such as Tauhid, Ibadah, Akhlak, and Sejarah. Based on model evaluation using F1-Score, it shows significant differences between the CNN+Bi-LSTM model without FastText, with the highest result being 68.70% in the 80:20 testing configuration. Conversely, the CNN+Bi-LSTM+FastText model, combining embedding size and epoch parameters, achieves a result of 73.30% with an embedding size of 200, epoch of 100, and a 90:10 testing configuration. These findings underscore the significant impact of FastText on model optimization, with an enhancement margin of 4.6% over the base model.

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K-means variations analysis for translation of English Tafseer Al-Quran text

Cited by 3 publications

References 17 publications

Prioritise Five Tafseer Translators Using Clustering Technique for Surah Al-Baqarah

Prioritise Five Tafseer Translators Using Clustering Technique for Surah Al-Baqarah

Analisis Klasterisasi Untuk Prediksi Jumlah Kasus DBD Berdasarkan Jenis Kelamin Dan Kabupaten/Kota Di Jawa Barat

Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText

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