Smart Camping Management Asset using Frequent Pattern-Growth Algorithm

Managing camping tools is crucial to ensure the safety of campers and extend the life of the tools. Frequent Pattern-Growth (FP-Growth) Association Rule Algorithms have emerged as a powerful tool for managing assets effectively. This algorithm is used to identify frequent patterns in large datasets, making it an efficient method for discovering association rules to help organizations make informed decisions about their assets. In the context of asset management, FP-Growth Association Rule Algorithms can be used to identify patterns in asset usage, maintenance, and replacement. By analyzing historical data, the algorithm can identify which assets are most frequently used, which require the most maintenance, and which are most likely to fail. This paper proposes managing the assets of camping tools using FP-growth and radio frequency identification (RFID) cards for asset tags. The proposed management asset tools are built on an Android operating system smartphone. This research using 30%, 60%, and 80% minimum support. The experiment results show that the minimum support affects the time needed to build an association. A small minimum support requires a longer time, while a large minimum support requires a faster time. The confidence value is used to determine the relevance of the FP-Growth output results. The results of the tests show that the difference in minimum support affects the speed of building associations, and the confidence value is proportional to the minimum support used. This research can be used for other case studies, using equipment data other than equipment for outdoor activities.