Market Basket Analysis Using Apriori Algorithm to Find Effective Fiscal Policy Mix with R Programming

Isnen Hadi Al Ghozali; Arief Wibowo

doi:10.51454/decode.v3i2.180

Authors

Isnen Hadi Al Ghozali Ilmu Komputer Universitas Budi Luhur
Arief Wibowo Sistem Informasi Universitas Budi Luhur

DOI:

https://doi.org/10.51454/decode.v3i2.180

Keywords:

apriori algorithm, association rules, fiscal policy, market basket analysis

Abstract

Fiscal policy drives a country's economy and is the most effectivepolicy to restore a country's economy. When a recession occurs, the fiscal policy helps a country increase aggregate demand in the market for goods and services. This study proposes a fiscal policy mix that can be implemented based on historical data. So this research focuses on using association rules to assist decision-makers (regulators in adopting appropriate fiscal policies in the global VUCA (Volatility, Uncertainty, Complexity, Ambiguity) era. Therefore, an experimental research approach was used in this study to produce the best association rules. The research was carried out in six stages to obtain conclusions, namely problem identification in the research sample and literature review related to apriori algorithms, data collection, data pre-processing, parameter determination, research findings building a priori algorithms, and knowledge extraction formed from a priori algorithms. Based on the experimental results using the a priori algorithm, 657 rules were obtained with a minimum variation of two to six itemsets. Rule 6 produces an average value of the budget performance of 90.65. Rule 6 also says that the performance value of the budget can be increased by spreading out funding sources and ensuring that operational spending is as efficient as possible.

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