Sukemi, Sukemi (2020) Online Retail Marketing Recommendation System Based on Generalized Sequential Pattern Algorithm and FP-Growth Algorithm. Advances in intelligent systems research,proceeding of the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019), 172. pp. 353-357. ISSN 1951-6851
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Abstract
Data mining association is a technique to find the relationship between items where the function can help sellers in determining their sales strategy. The algorithm used in this data mining techniques are Generalized Sequential Pattern Algorithm and FP-Growth Algorithm. Generalized Sequential Pattern Algorithm is an algorithm based on sequential patterns in the formation of rules, while FP-Growth Algorithm is a tree-based algorithm in the formation of rules. This research produces a comparison of the computation time of each algorithms in carrying data mining process associated with the data that has been determined. The result of computational time comparisons show that FP-Growth Algorithm is 11.97% faster than Generalized Sequential Pattern Algorithm based on 30 tests. Generalized Sequential Pattern Algorithm produces 2 rules and FP-Growth Algorithm produces 8 rules by testing 500 transaction data and minimum support value is 3. Where the rules obtained is evaluated using the lift ratio techniques to calculate the value of the rule accuracy generated from each algorithms.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. |
| Divisions: | 09-Faculty of Computer Science > 56201-Computer Systems (S1) |
| Depositing User: | Dr. Sukemi Sukemi |
| Date Deposited: | 18 Jan 2022 05:59 |
| Last Modified: | 18 Jan 2022 05:59 |
| URI: | http://repository.unsri.ac.id/id/eprint/60712 |
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