STUDI KOMPARASI ALGORITMA APRIORI DAN FREQUENT PATTERN GROWTH PADA DATA MINING MARKET BASKET ANALYSIS

Dea Rahmadani, NIM.: 19106050048 (2023) STUDI KOMPARASI ALGORITMA APRIORI DAN FREQUENT PATTERN GROWTH PADA DATA MINING MARKET BASKET ANALYSIS. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Competition in the business world is currently getting tougher and experiencing significant developments in various fields, especially the business of buying and selling self-service businesses. One way that can be done to face competition is by utilizing technology. By utilizing data mining market basket analysis techniques so that the pattern of purchasing comments and products purchased can be obtained simultaneously. The case study taken in this study is Supermarket Adha using historical sales transaction data for the period of September to November 2022. The data were processed and analyzed using the algorithm apriori and FP-Growth. Both algorithms were chosen because they are popular in the data mining process to find out consumer purchasing patterns. By using the algorithm, it is expected to produce a pattern of association rules and get information from the calculation results to find out where the differences between the two algorithms lie. The application of data mining techniques with apriori algorithms and FP-Growth using python and RapidMiner tools was successfully implemented in this study. The same results were obtained from the two algorithms with the strongest rule Sampoerna A Mild 16 Pcs → Gudang Garam Surya with a support value of 0.035 and a confidence value of 78% meaning that if consumers buy Sampoerna A Mild 16 Pcs it has a 78% chance of also buying Gudang Garam Surya. When viewed from the calculation of the length of execution time, the FP-Growth algorithm is better because it takes a shorter time while the Apriori algorithm is more sluggisht. From these results, it can be concluded that the comparison location of the two algorithms is in the length of program execution time

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