Optimasi association rule pada keranjang belanja pelanggan menggunakan apiori dan algoritma genetika

Muhammad, Ammar Rusydah (2018) Optimasi association rule pada keranjang belanja pelanggan menggunakan apiori dan algoritma genetika. Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

Transaction data that exist in a company, especially in the retail store must be reprocessed so it will not vain. Based on result from previous research apriori have a weakness at rules extraction which only use parameter minimum support that cause rules become too much at large scale dataset. In this research we proposed genetic algorithm to perform optimization and selection for rules generated by apriori. We use objective function parameter to determine rule’s strength. The object is a dataset from UCI Machine Learning Repository by Dr. Daqing Chen with subject Online Retail Data Set. Result expected to have fewer rules with more optimal value range sa it can be used as an effective result interpretation. From the experiment with only apriori performed we got 958 rules and 0,7529 range value. Meanwhile with using apriori and genetic algorithm, we got 624 rules and 0,278239 range value. Based on this result we can say that combination of apriori and genetic algorithm produce more optimal rules than apriori result. Keyword : Apriori, Association Rule, Genetic Algorithm, Optimization

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