Tajweed Pattern Analysis in the 30th Juz of Qur’an Using the Apriori Algorithm
Article Sidebar
Main Article Content
Abstract
Tajweed knowledge plays an important role in maintaining the integrity and beauty of the recitation of the Qur'an. However, tajweed learning is often conventional and less data-driven. This study aims to analyze the pattern of the emergence of tajweed rules in the 30th Juz of the Qur'an using the Apriori algorithm, a data mining method capable of efficiently identifying associations between items. The dataset used consists of 564 verses, which are classified into 22 categories of tajweed rules based on manual analysis from trusted sources. Through the process of data transformation and the application of Apriori, several significant patterns of the emergence of tajweed rules were obtained. The Mad Thobii rule is the most dominant with the highest support value of 79.96%. The combination of patterns with the highest confidence value was found in the (Ghunnah) → (Mad Thobii) rule at 83.11%, indicating a strong relationship between the two rules. These findings are expected to provide new insights in data-based tajweed learning and become a reference in the development of interactive media for learning the Qur'an. In the future, this research can be expanded to encompass the entire Quran and other association algorithms, such as FP-Growth, can be applied to evaluate the effectiveness and efficiency of tajweed pattern analysis. This research represents an initial step toward utilizing data to strengthen adaptive and relevant religious learning.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
References
[1] M. Damier, W. Damier, H. Wahab, “Pembelajaran Ilmu Tajwid untuk Meningkatkan Kualitas Qira’atul Qur’an Pesantren NU Polewali Mandar,” Journal of Indonesian Scholars for Social Research, vol. 2, no. 2, pp. 147-154, 2022.
[2] V. Prabaningtyas, I. Tabroni, “Improving the Ability of Tajwid Science in Reading the Qur'an,” Jurnal Pengabdian Masyarakat Bestari (JPMB), vol. 1, no. 8, pp. 835-846, 2022, doi: 10.55927/jpmb.v1i8.1822.
[3] T. Sutrisno, dkk., “Pelatihan Ilmu Tajwid Dalam Tahsin Al-Qur’an Bagi Anak Usia SD/MI di Surau Bulangan Barat Kabupaten Pamekasan,” Kifah, vol. 1, no. 2, pp. 119-129, 2022, doi: 10.35878/kifah.v1i2.483.
[4] N. Sakinah, R. Kurniawati, I.Z. Nasution, “Pelatihan Ilmu Tajwid Dalam Tahsin Al-Qur’an Bagi Anak Usia SD/MI di Surau Bulangan Barat Kabupaten Pamekasan,” Maslahah, vol. 3, no. 2, 2022, doi: 10.56114/maslahah.v3i2.359.
[5] M.A. Ayu, E. Irawan, T. Mantoro, “Text mining approaches for analyzing an Indonesian tafseer and translation of the Holy Quran,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 25, no. 3, pp. 1469-1490, doi: 10.11591/ijeecs.v25.i3.pp1469-1480.
[6] D.I. Mulyana, M.A.I. Rowis, “Optimization of Text Mining Detection of Tajweed Reading Laws using the YOLOV8 Method on the Qur’an,” Qalamuna - Jurnal Pendidikan, Sosial, dan Agama, vol. 14, no. 2, pp. 1089-1110, 2022, doi: 10.37680/qalamuna.v14i2.3866.
[7] M.A. Ahmed, H. Baharin, and P.N.E. Nohuddin, “Text Clustering of Tafseer Translations by Using k-means Algorithm: An Al-Baqarah Chapter View” Annals of Emerging Technologies in Computing (AETiC), vol. 7, no. 4, pp. 27-34, 2023, doi: 10.33166/AETiC.2023.04.003.
[8] Suardi, “Implementasi Algoritma Apriori Untuk Analisis Data Transaksi Penjualan Pada Toko Berbasis Desktop,” Teknokompak, vol. 17, no. 1, 2023, doi: 10.33365/jtk.v17i1.2148.
[9] S.M. Amanda, D. Setiawan, L. Trisnawati, “Penerapan Algoritma Apriori Dalam Menganalisis Pola Minat Beli Konsumen di Coffee Shop,” JEKIN (Jurnal Teknik Informatika), vol. 1, no. 2, pp. 25-32, 2023, doi: 10.58794/jekin.v3i1.483.
[10] M. Badaruddin, R. Rayendra, “Penerapan Algoritma Apriori Pada Analisa Data Penjualan Ecommerce,” Jurnal Media Informatika Budidarma, vol. 6, no. 2, 2022, doi: 10.30865/mib.v6i2.3976.
[11] Y.Y.Y. Turukay, et al., “Implementasi Algoritma Apriori Untuk Meningkatkan Pola Penjualan Barang (Studi Kasus: Toko DEPO TEGUH),” G-Tech : Jurnal Teknologi Terapan, vol. 7, no. 2, pp. 450-455, 2023, doi: 10.33379/gtech.v7i2.2007.
[12] D. Abdullah, M. Dewi, Fadlisyah, “Sistem Pendeteksi Pola Tajwid Al-Qur’an Hukum Idgham Mutaqaribain Pada Al-Qur’an Menggunakan Metode Gower & Legendre,” Jurnal Teknologi Terapan & Sains, vol. 1, no. 2, pp. 193-205, 2020, doi: 10.29103/tts.v1i2.3260.
[13] H. Lahuddin, R. Satra, “Data Mining Approach to Improve Minimarket Sales using Association Rule Method,” Jurnal Informatika, vol. 12, no. 1, pp. 10-14, 2025, doi: 10.31294/inf.v12i1.20835.
[14] Mairani, “Korelasi Permasalahan Kulit Wajah terhadap Jenis Produk yang Digunakan Menggunakan Metode Apriori (Studi Kasus : Queen Arabic),” BRIDGE : Jurnal publikasi Sistem Informasi dan Telekomunikasi, vol. 2, no. 4, pp. 288-294, 2024, doi: 10.62951/bridge.v2i4.256.
[15] Dr R.Naveenkumar, Md Aisaifi, “Deciphering Patterns in a small scale Case Analysis Study of the Apriori Algorithm in Market Basket Analysis using machine learning tools,” International Scientific Journal of Engineering and Management, vol. 3 no. 5, pp. 1-11, 2024, doi: 10.55041/ISJEM01603.
[16] A.D. Kuswanto, et al., “Penerapan Algoritma Apriori Dalam Analisis Keranjang Belanja Retail Di Wilayah Jawa Barat,”Saturnus : Jurnal Teknologi dan Sistem Informasi, vol. 2, no. 3, pp. 139-150, 2024, doi: 10.61132/saturnus.v2i3.208.
[17] I. Rosmayati, et al., “Implementasi Data Mining pada Penjualan Kopi Menggunakan Algoritma Apriori,” Jurnal Algoritma, vol. 20, no. 1, pp. 99-107, 2023, doi: 10.33364/algoritma/v.20-1.1259.
[18] B.H. Situmorang, et al., “Apriori Algorithm Application for Consumer Purchase Patterns Analysis,” Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika, vol. 21, no. 1, pp. 15-20, 2024, doi: 10.33751/komputasi.v21i1.9260.
[19] N.S. Poli, A.S. Sikder, “Predictive Analysis of Sales Using the Apriori Algorithm: A Comprehensive Study on Sales Forecasting and Business Strategies in the Retail Industry,” International Journal of Imminent Science & Technology, vol. 1, no. 1, pp. 1-15, 2023, doi: 10.70774/ijist.v1i1.1.
[20] S. Raschka“TransactionEncoder: Convert item lists into transaction data for frequent itemset mining,” Mlxtend. [Online]. Available: https://rasbt.github.io/mlxtend/user_guide/preprocessing/TransactionEncoder/.
[21] Data Camp, “One-hot encoding transaction data,”. [Online]. Available: https://campus.datacamp.com/courses/market-basket-analysis-in-python/introduction-to-market-basket-analysis-1?ex=9.