STUDI KOMPARASI ALGORITMA APRIORI DANALGORITMA ECLAT UNTUK ANALISIS POLA PEMBELIAN KONSUMEN PADA DATA TRANSAKSI PENJUALAN

Anifah Putri Utami, NIM.: 19106050047 (2023) STUDI KOMPARASI ALGORITMA APRIORI DANALGORITMA ECLAT UNTUK ANALISIS POLA PEMBELIAN KONSUMEN PADA DATA TRANSAKSI PENJUALAN. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

Preview	Text (STUDI KOMPARASI ALGORITMA APRIORI DANALGORITMA ECLAT UNTUK ANALISIS POLA PEMBELIAN KONSUMEN PADA DATA TRANSAKSI PENJUALAN) 19106050047_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf - Published Version Download (2MB) | Preview
	Text (STUDI KOMPARASI ALGORITMA APRIORI DANALGORITMA ECLAT UNTUK ANALISIS POLA PEMBELIAN KONSUMEN PADA DATA TRANSAKSI PENJUALAN) 19106050047_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf - Published Version Restricted to Registered users only Download (4MB) | Request a copy

Abstract

Toko Wijaya is a store that has a fairly high sales intensity. From the existence of data that is increasing, it can be used to support business processes. For this reason, the use of the Apriori Algorithm and the Eclat Algorithm were chosen because they are two different types of algorithms in how they work. In addition, both algorithms have their own advantages and disadvantages which can help in knowing which algorithm is right for a particular dataset, taking into account several factors such as computational efficiency, large data handling capabilities, and ease of use and implementation. Therefore, this research will implement data mining techniques in processing 7.237 sales transaction data at Wijaya Stores in August 2022 to look for consumer purchasing patterns. The information to be obtained is the result of the application of the Apriori Algorithm and the Eclat Algorithm as well as a comparison between the two algorithms which are processed through several stages starting from data collection, data selection, data preprocessing, data transformation, pattern discovery, and interpretation. The application of the Apriori and Eclat Algorithms was successfully implemented in this study. The results of this processing can be seen that the rule with the highest support value is 0,4974%, namely (Copy Photo, Print 1 Sheet), while the highest confidence value is 53,125%, namely the rule (Tjatoet Ijo Tea 1bji, Sugar 1/2kg). From the results of the comparative analysis, it can be seen that the Apriori Algorithm generates 2 times more rules than the Eclat Algorithm. And in terms of time, the Apriori Algorithm is shorter than the Eclat Algorithm.

Share this knowledge with your friends :

Actions (login required)

View Item