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Pattern of E-marketplace Customer Shopping Behavior using Tabu Search and FP-Growth Algorithm Ayu Meida; Dian Palupi Rini; Sukemi Sukemi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 7, No 4: December 2019
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v7i4.1362


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, we perform merging algorithm with Improved Tabu Search (TS). The application of Improved TS algorithm as optimization algorithm for preprocessing datasets, data filtering, and sorting data closely related products on sales data can optimize the ARM processing. The 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 is 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, the Lift Ratio value was calculated. Based on the calculation of 97 rules produced, the lift ratio produces values > 1 of 82.54% and based on processing time, it 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. The test results in this study obtained more optimal results than when compared with the results of previous studies, namely in time efficiency and data mining in real time and more accurate data validation.  As a conclusion, the resulting rule can be used as a reference in understanding shopping behavior patterns customer on the E-Marketplace.
Rancangan Perilaku Belanja Customer pada E-marketplace dengan algoritma Hybrid Improved Tabu Search untuk optimasi Association Rule Mining (FP-Growth) Ayu Meida; Willy Willy; Dwi Lydia Zuharah Astuti
Annual Research Seminar (ARS) Vol 4, No 1 (2018): ARS 2018
Publisher : Annual Research Seminar (ARS)

Show Abstract | Download Original | Original Source | Check in Google Scholar


Kebiasaan seseorang dalam melakukan transaksi belanja pada suatu e-marketplace mempunyai suatu pola kesamaan memilih produk yang sama. Association Rule Mining merupakan salah satu teknik  menambang data informasi yang berguna dalam mengidentifikasi kebiasaan tersebut. Kegiatan transaksi dirancang untuk mempermudah pelanggan dalam menentukan produk belanja sesuai kebutuhan  yang diinginkan dengan tujuan kenyamanan berbelanja, terutama kemudahan dalam memberi masukan relasi produk sebagai rekomendasi berdasarkan konsumen lain pada umumnya. Penelitian ini menentukan pola perilaku belanja pelanggan pada E-marketplace dengan bantuan  Hybrid Improved Particle Swarm Optimization integrasi dengan Tabu Search dan Association Rule Mining dengan algoritma FP Growth dalam menentukan kebiasaan berbelanja pelanggan.