APPLICATION OF FP-GROWTH ALGORITHM TO DETERMINE VEGETABLE TRADER'S GOODS LABELING

  • Wahyu Alfafisabil Universitas Singaperbangsa Karawang
  • Budi Arif Dermawan
  • Tesa Nur Padilah
Keywords: Association Rules, FP-Growth, Data Mining

Abstract

Vegetables are a source of vitamins and protein. Every housewife needs vegetables for cooking in everyday life. Vegetables are found in the market so that it will be difficult for housewives whose homes are far from the market. Mobile vegetable traders are traders who sell various kinds of vegetables that are brought to homes to meet the needs of housewives. Mobile vegetable traders aim to make a profit, so maximizing the level of sales requires a sales strategy. Association rules are methods for finding relationships between items in a dataset. Data mining can be called one step of the KDD process. FP-Growth is an algorithm for finding the data sets that most often appear. This study analyzes transaction data to predict the placement of goods at a vegetable merchant with the aim of maximizing the level of sales using the FP-Growth algorithm and the python programming language. In the process of data mining using the FP-Growth algorithm the researcher explains the steps of FP-Growth with manual calculations. Evaluation of researchers matching the results of manual calculations with the program. After the calculation is correct, the researcher uses transaction tota data to find out the rules with a minimum support requirement of 0.01 or 1% and a minimum confidence of 0.9 or 90%. In the results there are 44 rules that meet the requirements.

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Published
2021-08-31
How to Cite
[1]
W. Alfafisabil, B. Arif Dermawan, and T. Nur Padilah, “APPLICATION OF FP-GROWTH ALGORITHM TO DETERMINE VEGETABLE TRADER’S GOODS LABELING”, JIP, vol. 7, no. 4, pp. 43-48, Aug. 2021.