Open Journal Systems

Comparative Study of Association Rule Algorithms to Find an Effective Fiscal Policy Mix

       Isnen Hadi Al Ghozali, Triardani Lestari, Gandung Triyono

Abstract


In 2021, the government of Indonesia changed the way it spends money four times. The dynamics of the socio-economic environment, which changes so quickly, have implications for the complexity of policy making, which is demanded immediately. The policies taken tend to be influenced by the instincts of the leadership based on non-holistic data, so inappropriate policies are very likely to occur. This study aims to compare a priori, FP-Growth, and EClaT to find the best algorithm for formulating an effective fiscal policy mix based on historical data. This study uses the 2021 dataset, which includes 19,166 police records organized into Work Units. This study uses the Knowledge Discovery in Databases (KDD) approach. Our conclusion is the FP-Growth algorithm is the best way to find an effective policy mix. FP-Growth algorithm with minlen 7 reaches the best budget performance scores at 96.77. Meanwhile, the apriori algorithm can be relied upon to formulate a fiscal policy that is aligned between top management and middle management. The EClaT algorithm has the advantage of identifying the majority of fiscal policies that will be taken by middle management.


  http://dx.doi.org/10.31544/jtera.v8.i2.2023.227-236

Keywords


apriori algorithm; FP-Growth; EClaT; fiscal policy

Full Text:

  PDF

References


M. R. Ridzuan and N. A. S. Abd Rahman, “The Deployment of Fiscal Policy in Several ASEAN Countries in Dampening the Impact of COVID-19,” J. Emerg. Econ. Islam. Res., vol. 9, no. 1, p. 16, Jan. 2021.

I. SYAHRINI, R. MASBAR, A. ALIASUDDIN, S. MUNZIR, and Y. HAZMI, “The Application of Optimal Control Through Fiscal Policy on Indonesian Economy,” J. Asian Finance Econ. Bus., vol. 8, no. 3, pp. 741–750, Mar. 2021.

C. Utama, “The Effect of Twin-shock on Monetary and Fiscal Policies in Indonesia,” J. Ekon. Malays., vol. 54, no. 2, pp. 137–147, 2020.

C. Alina Stefania, E. Iulia Alexandra, H. Andrei, V. Daniela, and B. Adrian, “Measuring the Impact of Fiscal Policy Shocks in Romania,” Econ. Comput. Econ. Cybern. Stud. Res., vol. 56, no. 3/2022, pp. 119–134, Sep. 2022.

C.-H. Chee, J. Jaafar, I. A. Aziz, M. H. Hasan, and W. Yeoh, “Algorithms for frequent itemset mining: a literature review,” Artif. Intell. Rev., vol. 52, no. 4, pp. 2603–2621, Dec. 2019.

K. K. Widiartha and D. P. D. K. Dewi, “Shopping Cart Analysis System in Product Layout Management with Apriori Algorithm,” ACSIE Int. J. Appl. Comput. Sci. Inform. Eng., vol. 1, no. 2, pp. 53–64, Nov. 2019.

A. E. Lubis and P. M. Hasugian, “Implementation Of Data Mining On Suzuki Motorcycle Sales In Gemilang Motor Prosperous With Apriori Algorithm Method,” J. Comput. Netw. Archit. High Perform. Comput., vol. 2, no. 1, pp. 23–29.

J. Yang et al., “Brief introduction of medical database and data mining technology in big data era,” J. Evid.-Based Med., vol. 13, no. 1, pp. 57–69, Feb. 2020.

M. Lu, R. Wang, and P. Li, “Comparative analysis of online fresh food shopping behavior during normal and COVID-19 crisis periods,” Br. Food J., vol. 124, no. 3, pp. 968–986, Feb. 2022.

A. H. Nasyuha et al., “Frequent pattern growth algorithm for maximizing display items,” TELKOMNIKA Telecommun. Comput. Electron. Control, vol. 19, no. 2, p. 390, Aug. 2020.

H. M. Wang, “Research On Data Mining Algorithm In Power Marketing Analysis,” J. Phys. Conf. Ser., vol. 1635, no. 1, p. 012002, Nov. 2020.

B. He, Zhang, and H. Zhang, “FP-Growth Association Rule Mining Algorithm based on Big Data,” Int. J. Sci., vol. 6, no. 12, pp. 310–318, 2019.

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, vol. 10, no. 24, p. 3091, Dec. 2021.

H. K. Patel and K. P. Yadav, “Association Rule Mining Using Retail Market Basket Dataset by Apriori and FPGrowth Algorithms,” J. Algebr. Stat., vol. 13, no. 2, pp. 798–803, 2022.

L. N. Rani, S. Defit, and L. J. Muhammad, “Determination of Student Subjects in Higher Education Using Hybrid Data Mining Method with the K-Means Algorithm and FP Growth,” Int. J. Artif. Intell. Res., vol. 5, no. 1, Dec. 2021.

M. Man, N. A. Ruslan, J. A. Jusoh, and W. A. W. Abu Bakar, “Conceptual Model of Incremental R-Eclat Algorithm for Infrequent Itemset Mining,” Int. J. Eng. Trends Technol., pp. 129–133, Oct. 2020.

Lisnawita and M. Devega, “Implementation of ECLAT Algorithm Technology: Determining Books Borrowing Pattern in University library,” IOP Conf. Ser. Earth Environ. Sci., vol. 469, no. 1, p. 012036, Apr. 2020.

P. Thanathamathee and S. Sawangarreerak, “Discovering Future Earnings Patterns through FP-Growth and ECLAT Algorithms with Optimized Discretization,” Emerg. Sci. J., vol. 6, no. 6, pp. 1328–1345, Sep. 2022.

I. Fauziah, M. Rizki, M. Hartati, N. Nazaruddin, F. S. Lubis, and F. Lestari, “Market Basket Analysis with Equivalence Class Transformation Algorithm (ECLAT) For Inventory Management Using Economic Order Quantity (EOQ),” in Proceedings of the 3rd South American International Industrial Engineering and Operations Management Conference, Asuncion, Paraguay: IEOM Society International, Jul. 2022.

M. Liu, Y. Ye, J. Jiang, and K. Yang, “MANIEA: a microbial association network inference method based on improved Eclat association rule mining algorithm,” Bioinformatics, vol. 37, no. 20, pp. 3569–3578, Oct. 2021.

L. Jia, L. Xiang, and X. Liu, “An Improved Eclat Algorithm Based on Tissue-Like P System with Active Membranes,” Processes, vol. 7, no. 9, p. 555, Aug. 2019.

W. A. Wan Abu Bakar, M. Man, M. Man, and Z. Abdullah, “i-Eclat: performance enhancement of eclat via incremental approach in frequent itemset mining,” TELKOMNIKA Telecommun. Comput. Electron. Control, vol. 18, no. 1, p. 562, Feb. 2020.

H. KamelEddine, H. Kadrii, and A. Zabi, “Whale Optimization Algorithm For Solving Association Rule Mining Issue,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, pp. 333–342, Feb. 2021.

V. Geetha and A. Priadharshini, “An FBWM Algorithm For Webmining Inaccessing The Spatial Data,” Int. J. Anal. Exp. Modal Anal., vol. XI, no. VIII, pp. 154–170, 2019.

R. Muliono, Muhathir, N. Khairina, and M. K. Harahap, “Analysis of Frequent Itemsets Mining Algorithm Againts Models of Different Datasets,” J. Phys. Conf. Ser., vol. 1361, no. 1, p. 012036, Nov. 2019.

M. Nair and F. Kayaalp, “Performance Comparison of Association Rule Algorithms with SPMF on Automotive Industry Data,” Düzce Univ. J. Sci. Technol., vol. 7, no. 3, pp. 1985–2000, 2019.

M. Tünel and S. Gülcü, “Social Campus Application with Machine Learning for Mobile Devices,” in ICENTE’20), Konya, Turkey, 21/11 2021, pp. 63–68.

M. Man and M. A. Jalil, “Frequent itemset mining: technique to improve eclat based algorithm,” Int. J. Electr. Comput. Eng. IJECE, vol. 9, no. 6, p. 5471, Dec. 2019.




DOI: http://dx.doi.org/10.31544/jtera.v8.i2.2023.227-236
Abstract 96 View    PDF viewed = 40 View

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 JTERA (Jurnal Teknologi Rekayasa)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright @2016-2023 JTERA (Jurnal Teknologi Rekayasa) p-ISSN 2548-737X e-ISSN 2548-8678.

  Lisensi Creative Commons

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

JTERA Editorial Office:
Politeknik Sukabumi
Jl. Babakan Sirna 25, Sukabumi 43132, West Java, Indonesia
Phone/Fax: +62 266215417
Whatsapp: +62 81809214709
Website: https://jtera.polteksmi.ac.id
E-mail: jtera@polteksmi.ac.id