Comparative Analysis of Apriori Algorithm and Hash-Based Algorithm in Market Basket Analysis
DOI:
https://doi.org/10.30865/jurikom.v8i6.3574Keywords:
Data Mining, Association Rules, Market Basket Analysis, Apriori Algorithm, Hash-Based Algorithm, Rapid MinerAbstract
Grocery stores are now experiencing competition in the business world that is getting tighter, making businesses have to think hard in developing strategies to face competition. In developing strategies that benefit companies can take advantage of information technology. Information technology can help business companies in conducting their business. In this case, business companies can utilize the data generated by information systems to assist in decision making if processed correctly; such data can produce valuable information. Data Mining is the process of using artificial intelligence mathematical statistics techniques and Machine Learning to extract and identify useful information and related knowledge from various large databases/ Data Warehouse (Kennedi Tampubolon, 2013).  In this study, researchers used a priori algorithm and Hash-Based Algorithm to determine consumer spending patterns or consumer shopping cart data used as much as 1023 transaction data with a minimum value of 0.03 and Confidence of 0.5. This study resulted in an Apriori algorithm producing seven rules and forming a combination of 2 items with a rule strength of 13.14% and accuracy of 92.80%. Hash-Based Algorithm 7 Rule developed as many as two itemsets with a rule strength of 14.35%and formed an accuracy of 107.76%. From the results of the algorithm, comparison can be concluded that Hash-Based Algorithm is better  than Apriori algorithm
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