Analysis of Fuzzy C-Means Algorithm on Indonesian Translation of Hadits Text

Pratama, Rizky Sam; Huda, Arief Fatchul; Wahana, Agung; Darmalaksana, Wahyudin; Safitri, Q. U.; Rahman, Ali

doi:10.1109/icwt47785.2019.8978264

Cited by 6 publications

(7 citation statements)

References 5 publications

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“…The corresponding verses are then organised into seven key themes or issues (see Figure 1). Therefore, the ideal value of k for clustering the Al-Baqarah chapter for the five cluster algorithms is seven and applied for all five TR [4], [26], [28]- [30], [38]. Hence, two algorithms are used and mentioned in the introduction before as follows:-1.k-means The k-means algorithm is the fundamental form of cluster partitioning algorithms.…”

Section: Feature Selection (Weighting Terms)mentioning

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: Feature Selection (Weighting Terms)mentioning

confidence: 99%

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

2024

IJSER

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“…However, in the chapter Al-Baqarah clustering process experiments, the best cluster consists of 286 verses formed using cosine similarity with k-medoid. Pratama et al [5] classified the Indonesian translations of Hadith text and compared the performance of the fuzzy c-means and k-means algorithms using a number of predetermined parameters and term frequency-inverse document frequency (TF-IDF). Silhouette coefficient (SC) and F-measure computations are utilized for clustering validation.…”

Section: Related Workmentioning

confidence: 99%

“…Numerous strategies for word weighting are reported in the literature. The vector space model (VSM) remains the most popular model for representing texts (corpus) and is used for these research experiments [5], [14], [15].…”

Section: Feature Selectionmentioning

confidence: 99%

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

Ahmed

Baharin

Nohuddin

2023

IJECE

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Text mining is a powerful modern technique used to obtain interesting information from huge datasets. Text clustering is used to distinguish between documents that have the same themes or topics. The absence of the datasets ground truth enforces the use of clustering (unsupervised learning) rather than others, such as classification (supervised learning). The “no free lunch” (NFL) theorem supposed that no algorithm outperformed the other in a variety of conditions (several datasets). This study aims to analyze the k-means cluster algorithm variations (three algorithms (k-means, mini-batch k-means, and k-medoids) at the clustering process stage. Six datasets were used/analyzed in chapter Al-Baqarah English translation (text) of 286 verses at the preprocessing stage. Moreover, feature selection used the term frequency–inverse document frequency (TF-IDF) to get the weighting term. At the final stage, five internal cluster validations metrics were implemented silhouette coefficient (SC), Calinski-Harabasz index (CHI), C-index (CI), Dunn’s indices (DI) and Davies Bouldin index (DBI) and regarding execution time (ET). The experiments proved that k-medoids outperformed the other two algorithms in terms of ET only. In contrast, no algorithm is superior to the other in terms of the clustering process for the six datasets, which confirms the NFL theorem assumption.

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“…The ideal cluster results, however, in chapter al -Baqarah clustering process given by cosine similarity of k-medoid. This research [11] tried to group Hadith texts of Indonesian text language to compare Fuzzy c-means and k-means algorithms with determined parameters and experiments. F-measure and Silhouette Coefficient calculations used as measure calculations to www.ijacsa.thesai.org evaluate clusters performances.…”

Section: Related Workmentioning

confidence: 99%

“…Zero scores show overlapping clusters. The scoring is higher when the clusters are dense and well separated, which is a standard cluster concept [24] [2] [11].…”

Section: ) Silhouette Coefficient (Sc): a Higher Silhouettementioning

confidence: 99%

Analysis of K-means, DBSCAN and OPTICS Cluster Algorithms on Al-Quran Verses

Ahmed¹,

Baharin²,

Nohuddin³

2020

IJACSA

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Chapter Al-Baqarah is the longest chapter in the Holy Quran, and it covers various topics. Al-Quran is the primary text of Islamic faith and practice. Millions of Muslims worldwide use Al-Quran as their reference book, and it, therefore, helps Muslims and Islamic scholars as guidance of the law life. Text clustering (unsupervised learning) is a process of separation that has to be divided text into the same section of similar documents. There are many text clustering algorithms and techniques used to make clusters, such as partitioning and density-based methods. In this paper, k-means preferred as a partitioning method and DBSCAN, OPTICS as a density-based method. This study aims to investigate and find which algorithm produced as the best accurate performance cluster for Al-Baqarah's English Tafseer chapter. Data preprocessing and feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) have applied for the dataset. The result shows k-means outperformed even has the smallest of Silhouette Coefficient (SC) score compared to others due to less implementation time with no noise production for seven clusters of Al-Baqarah chapter. OPTICS has no noise with the medium of SC score but has the longest implementation time due to its complexity.

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Analysis of Fuzzy C-Means Algorithm on Indonesian Translation of Hadits Text

Cited by 6 publications

References 5 publications

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

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

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

Analysis of K-means, DBSCAN and OPTICS Cluster Algorithms on Al-Quran Verses

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