Implementation of Agile Method and Apriori Algorithm for Recommendation System in Outdoor Equipment Rental Services

Raditya Prama  Elfreda; Muhammad Lulu Latif Usman

doi:10.52436/1.jutif.2026.7.2.5567

Authors

Raditya Prama Elfreda Department Of Informatics Engineering, Telkom University Purwokerto, Indonesia
Muhammad Lulu Latif Usman Department Of Informatics Engineering, Telkom University Purwokerto, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.2.5567

Keywords:

Agile Method, Apriori Algorithm, Outdoor Equipment Rental System, Recommendation System

Abstract

Drop Outdoor Purwokerto faces inefficiencies in its outdoor equipment rental process, where customers are required to visit the store directly to check item availability, often resulting in miscommunication and suboptimal transaction management. To address this issue, this study aims to design and develop a web-based outdoor equipment rental information system that enables real-time availability checking and efficient online booking. The system is developed using the Agile methodology to accommodate dynamic user requirements and iterative system improvements. In addition, the Apriori algorithm is implemented to analyze historical rental transaction data and generate item recommendations based on association rule mining. The analysis results indicate that several outdoor equipment items exhibit strong association patterns, with the highest lift value exceeding 1, signifying meaningful relationships beyond random co-occurrence. These patterns are utilized as the basis for the recommendation feature within the system. Functional testing using Black Box Testing shows that all system features operate as expected, achieving a 100% success rate across tested scenarios, including transaction processing, cart management, and recommendation display. The findings demonstrate that integrating the Agile development approach with Apriori-based data mining can effectively support data-driven decision-making in outdoor equipment rental services. This study contributes to the development of recommendation systems for small and medium-sized rental businesses by highlighting the practical application of association rule mining on rental transaction data, which exhibits characteristics distinct from conventional retail datasets.

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