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

       Isnen Hadi Al Ghozali, Triardani Lestari, Gandung Triyono


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.


apriori algorithm; FP-Growth; EClaT; fiscal policy

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