Finding Important Patterns of Sadaqah and Waqf Transactions in the Eid Charity Global Donation using Frequent Pattern Growth
DOI:
https://doi.org/10.15575/kjrt.v3i1.1641Keywords:
Association Rule Mining, Data Science, FP-Growth, Sadaqah, WaqfAbstract
This study aims to explore and uncover hidden patterns in charitable donation transactions (sadaqah and waqf) using the Frequent Pattern Growth (FP-Growth) algorithm as a method of association rule mining. The research employs an experimental design conducted on the Google Colaboratory platform using Python version 3.11.3. The dataset used is the global donation data from Eid Charity, available on the Kaggle platform, comprising 45,842 rows and 33 attributes. The experimental results yielded six association patterns, all of which have the consequence of "mosques," indicating that various forms of donations consistently culminate in fund allocations to mosques. The most significant pattern was found in the combination of “Ensuring preachers and imams are conditional” and “Well Drilling,” which produced the highest metric values in confidence (0.896), lift (3.39), conviction (7.08), and Zhang’s metric (0.714). These findings suggest that mosques serve as the central hub of philanthropic activity within the dataset. The discovered patterns provide deep insights into donor behavior tendencies and can serve as a foundation for more targeted and effective strategies in managing and developing future donation programs.
References
[1] E. Hikmawati, N. U. Maulidevi, and K. Surendro, “Minimum threshold determination method based on dataset characteristics in association rule mining,” J Big Data, vol. 8, no. 1, 2021, doi: 10.1186/s40537-021-00538-3.
[2] E. Munanda and S. Monalisa, “Penerapan Algoritma Fp-Growth Pada Data Transaksi Penjualan Untuk Penentuan Tataletak,” Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi, vol. 7, no. 2, pp. 173–184, 2021.
[3] W. P. Nurmayanti et al., “Market Basket Analysis with Apriori Algorithm and Frequent Pattern Growth (Fp-Growth) on Outdoor Product Sales Data,” International Journal of Educational Research & Social Sciences, vol. 2, no. 1, pp. 132–139, 2021, doi: 10.51601/ijersc.v2i1.45.
[4] P. Thanathamathee and S. Sawangarreerak, “Discovering Future Earnings Patterns through FP-Growth and ECLAT Algorithms with Optimized Discretization,” Emerging Science Journal, vol. 6, no. 6, pp. 1328–1345, 2022, doi: 10.28991/ESJ-2022-06-06-07.
[5] M. Amine, B. Ali, and O. Hammami, “Association Rule Mining for Market Basket Analysis in Retail Data : Enhancing Automated Knowledge Discovery with Apriori and FP-Growth Algorithms,” 2022.
[6] J. T. Atmojo et al., “Penggunaan Masker Dalam Pencegahan Dan Penanganan Covid-19: Rasionalitas, Efektivitas, Dan Isu Terkini,” Avicenna : Journal of Health Research, vol. 3, no. 2, pp. 84–95, 2020, doi: 10.36419/avicenna.v3i2.420.
[7] G. S. Nurohim, “Analisa Pola Belanja Alat Kesehatan di Shopee JoyoAlkes Menggunakan Algoritma FP-Growth,” Indonesian Journal Computer Science, vol. 1, no. 1, pp. 34–39, 2022, doi: 10.31294/ijcs.v1i1.1098.
[8] Nola Ritha, E. Suswaini, and W. Pebriadi, “Penerapan Association Rule Menggunakan Algoritma Apriori Pada Poliklinik Penyakit Dalam (Studi Kasus: Rumah Sakit Umum Daerah Bintan),” Jurnal Sains dan Informatika, vol. 7, no. 2, pp. 222–230, 2021, doi: 10.34128/jsi.v7i2.329.
[9] C. R. Artsitella, A. R. Apriliani, and S. Ashari, “Penerapan Association Rules - Market Basket Analysis untuk Mencari Frequent Itemset dengan Algoritma FP-Growth,” JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI, vol. 6, no. 2, p. 61, 2021, doi: 10.36722/sst.v6i2.661.
[10] S. Saharani, T. Iriyanto, and N. Anisa, “Perkembangan Perilaku Prososial Anak Usia 4-5 Tahun Di TK Mardi Putra 01 Kota Batu,” JP2KG AUD (Jurnal Pendidikan, Pengasuhan, Kesehatan dan Gizi Anak Usia Dini), vol. 2, no. 1, pp. 19–30, 2021, doi: 10.26740/jp2kgaud.2021.2.1.19-30.
[11] I. Ernawati and N. Matondang, “Product Bundling Application To Support Implementation of Association Rule Mining Using Fp-Growth,” Jurnal Teknologi Informasi dan Pendidikan, vol. 14, no. 1, pp. 60–63, 2021, doi: 10.24036/tip.v14i1.355.
[12] N. Boyko and O. Tkachyk, “Frequency Pattern Growth Algorithm (FP) for Multimodal Data Extraction,” CEUR Workshop Proc, vol. 3156, pp. 72–82, 2022.
[13] N. Boyko and O. Tkachyk, “Model for Finding Frequent Sets in FP-growth for Multimodal Data,” CEUR Workshop Proc, vol. 3137, pp. 58–67, 2022, doi: 10.32782/cmis/3137-11.
[14] H. J. Jang, Y. Yang, J. S. Park, and B. Kim, “Fp-growth algorithm for discovering region-based association rule in the iot environment,” Electronics (Switzerland), vol. 10, no. 24, 2021, doi: 10.3390/electronics10243091.
[15] M. Shawkat, M. Badawi, S. El-ghamrawy, R. Arnous, and A. El-desoky, “An optimized FP-growth algorithm for discovery of association rules,” Journal of Supercomputing, vol. 78, no. 4, pp. 5479–5506, 2022, doi: 10.1007/s11227-021-04066-y.
[16] A. Wadanur and A. A. Sari, “Implementasi Algoritma Apriori dan FP-Growth pada Penjualan Spareparts,” Edumatic: Jurnal Pendidikan Informatika, vol. 6, no. 1, pp. 107–115, 2022, doi: 10.29408/edumatic.v6i1.5470.
[17] E. - and S. P. Tamba, “Penerapan Data Mining Algoritma Apriori Dalam Menentukan Stok Bahan Baku Pada Restoran Nelayan Menggunakan Metode Association Rule,” Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA), vol. 5, no. 2, pp. 97–102, 2022, doi: 10.34012/jurnalsisteminformasidanilmukomputer.v5i2.2407.
[18] L. Lenus, “Discovering Co-Occurrence Patterns Among Blockchain Address Categories Using the FP- Growth Association Mining Algorithm,” vol. 2, no. 1, pp. 41–62, 2025, doi: 10.47738/jcrb.v2i1.24.
[19] W. Sriurai and S. Nuanmeesri, “The development of association rules for student performance analysis using FP-Growth algorithm as a guideline for multidisciplinary learning,” Journal of Applied Research on Science and Technology (JARST), vol. 23, no. 1, pp. 1–6, 2024, doi: 10.60101/jarst.2023.253807.
[20] T. Lakshika and A. Caldera, “Association Rules for Knowledge Discovery From E-News Articles: A Review of Apriori and FP-Growth Algorithms,” Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 178–192, 2022, doi: 10.25046/aj070519.
[21] S. Chopvitayakun, W. Jitsakul, and N. Aukkanit, “Analyzing Purchase Behavior Using FP Growth Technique to Find Association Rules,” ACM International Conference Proceeding Series, pp. 106–111, 2024, doi: 10.1145/3678610.3678618.
[22] M. Khanpour, K. Meesters, and D. Paulus, “The Reality of Evidence-based Decision Making in Humanitarian Programming: An Exploratory Study of WASH Programs in Uganda,” in 2020 IEEE Global Humanitarian Technology Conference (GHTC), IEEE, Oct. 2020, pp. 1–8. doi: 10.1109/GHTC46280.2020.9342885.
[23] A. Oluwafemi Joseph and D. Afolorunso Olalekan, “Exploring The Religio-Socio Ethical Dimensions of Ai-Driven Evangelism: Opportunities, Challenges, And Implications,” Journal of Business and Social Sciences, vol. 2024, no. 1, Nov. 2024, doi: 10.61453/jobss.v2024no17.
[24] C. P. Scheitle and A. Adamczyk, “Divine callings: Religious sensemaking in the organizational founding process,” in Management, Spirituality and Religion, De Gruyter, 2023, pp. 77–100. doi: 10.1515/9783111216058-004.
[25] E. C. Clark, T. Burnett, R. Blair, R. L. Traynor, L. Hagerman, and M. Dobbins, “Strategies to implement evidence-informed decision making at the organizational level: a rapid systematic review,” BMC Health Serv Res, vol. 24, no. 1, p. 405, Apr. 2024, doi: 10.1186/s12913-024-10841-3.
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