RANCANG BANGUN APLIKASI DATA MINING DENGAN ALGORITMA FP-GROWTH PADA DATA PENJUALAN SPAREPART MOBIL SUZUKI RADIO DALAM

Abstract

Suzuki Radio Dalam is an automotive company operating in the automotive sector. They have been facing a challenge where sales data of spare parts has been accumulating without being effectively utilized or managed. The company has never employed data mining techniques to extract meaningful patterns or insights from this spare parts sales data. To address these issues, the researchers adopted the data mining technique known as the FP-Growth algorithm. The FP-Growth algorithm is a form of association algorithm within data mining. Association algorithms are utilized to uncover relationships and connections between variables present in a dataset. Through the application of the FP-Growth algorithm, data can be extracted through the construction of FP-Trees, which reveals insights into patterns of items purchased by customers. This method allows for the identification of frequently co-purchased items, enabling the company to devise marketing strategies aimed at boosting spare parts sales. The proposed solution involves creating a web-based platform to facilitate the FP-Growth algorithm calculations, particularly when dealing with large volumes of data. This web-based system was developed using PHP and utilizes a MySQL database. This data is then subjected to FP-Growth algorithm calculations and subsequently analyzed to generate association rules. These association rules hold valuable information about customer purchasing patterns. The implementation of this web mining solution streamlines the FP-Growth algorithm calculations, making it more manageable and efficient when dealing with substantial datasets. The resulting association rules derived from these calculations provide actionable insights for Suzuki's marketing strategies. By offering enticing promotions to customers based on the information gleaned from these association rules, the company aims to enhance spare parts sales.