Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
November 4, 2025˜Sœcience, Technology, and Communication Journal.Open Access

A classification of Quran translations using K-nearest neighbors, support vector machine and random forest method

View Full Paper
Ask AI
Bookmark
Share

Authors

NDNur DelifahNHNazruddin Safaat HarahapSASurya Agustian

Discussion

Loading...

Member takes

Overview

Classification of Quranic verses into topics reveals random forest outperforms support vector machine and K-nearest neighbors, indicating enhanced text categorization.

Key Points

  • Random forest achieved a testing accuracy of 90.81% in classifying topics from Quranic verses, highlighting its effectiveness.
  • Among the methods tested, random forest demonstrated the highest average F1-score of 58.48% in this classification task.
  • The analysis utilized a dataset of English translations of the Quran with a data split of 80:20 for training and testing purposes.
  • Optimized classification approaches like support vector machine performed less effectively compared to random forest and K-nearest neighbors.

Cite This Study

Delifah et al. (2025) studied this question.

synapsesocial.com/papers/690945348f2297dc13532dd4https://doi.org/10.59190/stc.v6i1.337
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Classification of Quran Verses Using Deep Learning2024 · 7 citations
  2. 2Topic Classification of Arabic Text Using Majority Voting2024
  3. 3Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText2024
  4. 4Enhancing Quranic Recitation Accuracy Using State-of-the-Art Audio Classification Techniques2025
  5. 5Cluster Analysis Of Emotions In Quranic Translations Using K-Means Clustering2024