ac

Comparison of Lexical and Semantic Approaches for Relevance Measurement in Quranic Verse Translation Retrieval

Authors

  • Abd. Charis Fauzan Deparment of Computer Science, Universitas Nahdlatul Ulama Blitar, Indonesia
  • M. Abd. Rouf Deparment of Quranic Studies and Tafsir, Universitas Nahdlatul Ulama Blitar, Indonesia
  • Tito Prabowo Deparment of Computer Science, Universitas Nahdlatul Ulama Blitar, Indonesia
  • Utrodus Said Al Baqi Deparment of Computer Science, Universitas Nahdlatul Ulama Blitar, Indonesia

DOI:

10.47709/cnahpc.v7i1.5194

Keywords:

lexical, semantic, relevance measurement, quranic verce translation

Dimension Badge Record



Abstract

This research explores the effectiveness of lexical and semantic approaches for relevance measurement in Quranic verse translation retrieval, focusing on Indonesian translations. Quranic verses encompass complex linguistic structures and diverse contexts, making precise retrieval challenging. Two retrieval methods were evaluated: lexical similarity, which focuses on exact word matches, and semantic similarity, which captures contextual meaning using word embeddings. The study utilized a dataset of Indonesian Quranic translations, preprocessed to normalize and tokenize text, with experimental queries derived from thematic exegesis on social responsibility. Evaluation was performed using precision, recall, and F1-score on top-5, top-10, and top-15 retrieved results. The lexical approach achieved perfect precision (100%) but exhibited lower recall (46%-58%), as it failed to retrieve relevant verses lacking exact matches. Conversely, the semantic approach demonstrated higher recall (56%-59%) and F1-scores (73%-74%) by identifying verses with contextual relevance, even in the absence of lexical similarity. The results reveal that while the lexical approach ensures precise matches, it overlooks semantic richness. The semantic approach, although computationally intensive, achieves greater contextual understanding. These findings highlight the potential for hybrid retrieval systems combining both approaches to enhance accuracy and relevance in Quranic information retrieval, supporting scholarly research and user engagement with Quranic content.

Downloads

Download data is not yet available.
Google Scholar Cite Analysis
Abstract viewed = 38 times

References

Abriani, G. U., & Yaqin, M. A. (2019). Analisis Implementasi Metode Semantic Similarity untuk Pengukuran Kemiripan Makna antar Kalimat. ILKOMNIKA: Journal of Computer Science and Applied Informatics, 1(2), 47–57. https://doi.org/10.28926/ilkomnika.v1i2.15

Afzal, H., & Mukhtar, T. (2019). Semantically Enhanced Concept Search of the Holy Quran: Qur’anic English WordNet. Arabian Journal for Science and Engineering, 44(4), 3953–3966. https://doi.org/10.1007/s13369-018-03709-2

Ahmad, R., Khan, F. Z., & Khan, M. A. (2021). Ontology Based Knowledge Retrieval and Semantic Modelling of Qur’an with Contextual Information. International Journal on Islamic Applications in Computer Science And Technology, 9(1), 10–25.

Amrizal, V. (2018). Penerapan Metode Term Frequency Inverse Document Frequency (Tf-Idf) Dan Cosine Similarity Pada Sistem Temu Kembali Informasi Untuk Mengetahui Syarah Hadits Berbasis Web (Studi Kasus: Hadits Shahih Bukhari-Muslim). Jurnal Teknik Informatika, 11(2), 149–164. https://doi.org/10.15408/jti.v11i2.8623

Anggraini, S., Purwitasari, D., & Arifin, A. Z. (2022). Pengukuran Kemiripan berbasis Leksikal dan Semantik untuk Perangkingan Dokumen Berbahasa Arab. ILKOMNIKA: Journal of Computer Science and Applied Informatics, 4(2), 134–145. https://doi.org/10.28926/ilkomnika.v4i2.495

Darmalaksana, W., Slamet, C., Zulfikar, W. B., Fadillah, I. F., Maylawati, D. S. adillah, & Ali, H. (2020). Latent semantic analysis and cosine similarity for hadith search engine. Telkomnika (Telecommunication Computing Electronics and Control), 18(1), 217–227. https://doi.org/10.12928/TELKOMNIKA.V18I1.14874

Goddard, C., & Schalley, A. C. (2010). Semantic Analysis. In Handbook of Natural Language Processing (2nd Editio). CRC Press.

Gunay, A., & Yolum, P. (2007). Structural and Semantic Similarity Metrics for Web Service Matchmaking. Lecture Notes in Computer Science (LNCS), 129–138.

Husni, H., & Arifin, B. (2019). Rancangan Dan Implementasi Aplikasi Pencarian Teks Terjemahan Al Qur’an Berbasis Model Ruang Vektor. Jurnal Simantec, 7(2), 90–96. https://doi.org/10.21107/simantec.v7i2.7224

Kesuma, H. W. A. (2016). Penerapan Metode TF-IDF dan Cosine Similarity dalam Aplikasi Kitab Undang-Undang Hukum Dagang.

Kuzi, S., Zhang, M., Li, C., Bendersky, M., & Najork, M. (2020). Leveraging Semantic and Lexical Matching to Improve the Recall of Document Retrieval Systems: A Hybrid Approach. In Proceedings of ACM Conference (Conference’17) (Vol. 1, Issue 1). Association for Computing Machinery.

Mhawi, D. N., Oleiwi, H. W., Saeed, N. H., & Al-Taie, H. L. (2022). An Efficient Information Retrieval System Using Evolutionary Algorithms. Network, 2(4), 583–605. https://doi.org/10.3390/network2040034

Mohamed, E. H., & Shokry, E. M. (2022). QSST: A Quranic Semantic Search Tool based on word embedding. Journal of King Saud University - Computer and Information Sciences, 34(3), 934–945. https://doi.org/10.1016/j.jksuci.2020.01.004

Mohd, M., Jan, R., & Shah, M. (2020). Text document summarization using word embedding. Expert Systems with Applications, 143. https://doi.org/10.1016/j.eswa.2019.112958

Naf’an, M. Z., Sari, Y., & Suyanto, Y. (2021). Word Embeddings Evaluation on Indonesian Translation of AI-Quran and Hadiths. IOP Conference Series: Materials Science and Engineering, 1077(1), 012025. https://doi.org/10.1088/1757-899x/1077/1/012025

Pardede, J., & Pamungkas, D. P. (2023). The Impact of Balanced Data Techniques on Classification Model Performance. Scientific Journal of Informatics, 11(2), 401–412. https://doi.org/10.15294/sji.v11i2.3649

Puspitasari, D. A. (2022). Kebijakan Pentashihan Aplikasi Al-Qur’an Digital di Indonesia: Studi Perkembangan Aplikasi “Al-Quran Kementerian Agama” dan Permasalahannya. J-PAI: Jurnal Pendidikan Agama Islam, 8(1), 12–22. https://doi.org/10.18860/jpai.v8i1.13425

Rofiqi, M. A., Fauzan, A. C., Agustin, A. P., & Saputra, A. A. (2019). Implementasi Term-Frequency Inverse Document Frequency ( TF- IDF ) Untuk Mencari Relevansi Dokumen Berdasarkan Query. Jurnal Computer Science and Applied Informatics, 1(2), 58–64.

Rouf Abd. M., & Fauzan Charis Abd. (2023). Relevansi Ayat al-Quran Secara Tematik Menggunakan Pendekatan Graph-Based Knowledgedan Lexical-Search. ILKOMNIKA: Journal of Computer Science and Applied Informatics, 5(1), 96–104.

Downloads

ARTICLE Published HISTORY

Submitted Date: 2024-12-29
Accepted Date: 2024-12-29
Published Date: 2025-01-17

How to Cite

Fauzan, A. C., Rouf, M. A., Prabowo, T., & Baqi, U. S. A. (2025). Comparison of Lexical and Semantic Approaches for Relevance Measurement in Quranic Verse Translation Retrieval. Journal of Computer Networks, Architecture and High Performance Computing, 7(1), 163-180. https://doi.org/10.47709/cnahpc.v7i1.5194