Comparative Analysis of Apriori, FP-Growth, and Eclat Algorithms in Determining Tourist Visit Patterns in Simalungun Regency

Andy Paul Harianja; Amsal Tampubolon

doi:10.59934/jaiea.v5i2.2207

Authors

Andy Paul Harianja Universitas Katolik Santo Thomas
Amsal Tampubolon Universitas Katolik Santo Thomas

DOI:

https://doi.org/10.59934/jaiea.v5i2.2207

Keywords:

Apriori, Association Rule Mining, Data Mining, Eclat, FP-Growth, Flask, Tourism Pattern

Abstract

Simalungun Regency possesses diverse tourism potential, yet its utilization remains suboptimal due to limited understanding of tourist visit patterns. This study aims to analyze visit patterns to tourist attractions in Simalungun Regency using three Association Rule Mining algorithms—Apriori, FP-Growth, and Eclat—implemented in a web-based system. The web system was designed using the Flask framework with a MySQL database to manage tourist visit data in real-time. The interface allows users to input visit data and perform interactive comparative analysis of the three algorithms with adjustable parameters. Results demonstrate that all three algorithms successfully identified strong visitation patterns, such as Lake Toba → Sipiso-piso Waterfall (confidence 82%, lift 1.85). In terms of performance, FP-Growth exhibited the fastest execution time (2.3 seconds at 10% support), followed by Eclat (4.8 seconds) and Apriori (12.5 seconds). Eclat proved most efficient in memory usage (85 MB). The developed web system facilitates the Tourism Office and stakeholders in analyzing tourist visit patterns and generating data-based tour package recommendations automatically and accessibly.

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