POLA PERILAKU BELANJA CUSTOMER PADA E-MARKETPLACE DENGAN ALGORITMA HYBRID IMPROVED TABU SEARCH (TS) DAN FP-GROWTH UNTUK OPTIMASI ASSOCIATION RULE MINING

MEIDA, AYU and Rini, Dian Palupi and Sukemi, Sukemi (2019) POLA PERILAKU BELANJA CUSTOMER PADA E-MARKETPLACE DENGAN ALGORITMA HYBRID IMPROVED TABU SEARCH (TS) DAN FP-GROWTH UNTUK OPTIMASI ASSOCIATION RULE MINING. Master thesis, Sriwijaya University.

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Abstract

Pattern of customer shopping behavior can be known by analyzing market cart. This analysis is performed using Association Rule Mining (ARM) method in order to improve cross-sale. The weakness of ARM is if processed data is big data, it takes more time to process the data. To optimize the ARM, then perform merging algorithm with Improved Tabu Search (TS). Application of Improved TS algorithm as optimization algorithm for preprocessing datasets, data filtering, and sorting data closely related products on sales data, so that it can optimize the processing ARM. Method of Association Rule Mining (FP-Growth) to determine frequent K-itemset, Support value and Confidence value of data which is already sorted on TS based on patterns which often appear in the dataset so it generates rules as reference of decision making for company. To measure the level of power of rule which has been formed, performing calculation of Lift Ratio value.Based on the calculation of 97 rules produced, the lift ratio produces a value of > 1 of 82.54% and based on processing time, produces the fastest data search in 1.66 seconds. When compared with previous research that uses the hybrid method, for data retrieval based on processing time, it produces the fastest data search within 12.3406 seconds, 150 seconds and 50 seconds. Previous studies have only compared the processing time of data searching without regard to validation / accuracy of data search. So it can be concluded that the test results in this study when compared with the results of previous studies obtained more optimal results, namely in time efficiency and data mining in real time and more accurate data validation so that the resulting rule can be used as a reference in understanding shopping behavior patterns customer on the E-Marketplace.

Item Type: Thesis (Master)
Uncontrolled Keywords: Association Rule Mining, FP-Growth, E-marketplace,Tabu Search, Improved Tabu Search
Subjects: Q Science > Q Science (General) > Q1-390 Science (General) > Q223.M517 Science -- Information services. Information storage and retrieval systems --Science.
Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions: 09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User: Users 2095 not found.
Date Deposited: 26 Sep 2019 04:53
Last Modified: 26 Sep 2019 04:53
URI: http://repository.unsri.ac.id/id/eprint/8968

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