In the bustling digital landscape of Indonesia’s agricultural marketplaces, a novel approach to data mining is making waves, promising to bridge the gap between vast transaction data and effective marketing strategies. Researchers have turned to the Apriori algorithm, a classic data mining technique, to uncover hidden patterns in historical transaction data, ultimately enhancing product recommendation systems.
The study, led by Handika Attha Maulana from the Faculty of Computer Science at Universitas AMIKOM Yogyakarta, focuses on transforming untapped historical data into actionable business insights. By analyzing 10 transaction histories involving 22 product items, the research team set out to identify significant association rules that could drive better marketing strategies and improve user experience.
The Apriori algorithm, known for its efficiency in mining frequent itemsets, was applied with a minimum support and confidence threshold of 50%. The analysis revealed a robust positive correlation between specific products, with the strongest rule showing a confidence value of 60.6% and a Lift Ratio of 11.1. This rule was then successfully integrated into a functional recommendation feature, demonstrating the practical applicability of the findings.
“The results were consistent and promising,” said Maulana. “The system’s recommendations matched our manual calculations perfectly, indicating the reliability of the Apriori-based approach.”
The implications for the agricultural sector are substantial. By leveraging historical transaction data, e-marketplaces can offer personalized product recommendations, enhancing customer satisfaction and driving sales. This approach not only optimizes marketing strategies but also provides a competitive edge in the rapidly growing digital agriculture sector.
As Maulana noted, “This study serves as a benchmark for developing similar technologies. The potential to improve sales and user experience is immense, and we believe this is just the beginning.”
The research, published in ‘Jurnal Sisfokom’, highlights the transformative power of data mining in agriculture. By unlocking the insights hidden within transaction data, the study paves the way for more sophisticated and effective recommendation systems, ultimately shaping the future of digital agriculture in Indonesia and beyond.