Muhammad Ammar, Rusydah and Rima Dias, Ramadhani and Andika, Amalia (2018) Optimasi Association Rule pada Keranjang Belanja Pelanggan Menggunakan Apiori dan Algoritma Genetika. In: Proceedings on Conference on Electrical Engineering, Telematics, Industrial Technology, and Creative Media, 11 Agustus 2018, 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 indicator 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 indicator 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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Industrial Engineering and Informatics > Informatics Engineering |
| Depositing User: | Rima Dias Ramadhani |
| Date Deposited: | 05 Dec 2018 02:15 |
| Last Modified: | 16 May 2019 09:17 |
| URI: | http://repository.ittelkom-pwt.ac.id/id/eprint/5129 |
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