Sistem Rekomendasi Menu Kantin Menggunakan Lifespan-Aware Association Rule Mining Dengan Hybrid Apriori Dan FP-Growth

Abstract

This study addresses the problem of how to systematically increase cross-selling in a small canteen, where additional items such as drinks and snacks are usually offered only based on the cashier’s memory and intuition. The proposed solution is a point-of-sale (POS) recommendation system that suggests complementary menu items in real time, based on patterns learned from historical transaction data. The system uses a lifespan-aware association rule mining approach with a hybrid of Apriori and FP-Growth, where both algorithms are applied to one-hot encoded POS data and their outputs are combined and validated before being deployed as recommendation rules. The research objectives are to extract stable co-purchase patterns from canteen transactions, compare the computational performance of Apriori and FP-Growth in this real-world setting, and evaluate the practical effectiveness of the resulting recommendation system. The method benchmarks Apriori and FP-Growth across several minimum support values in terms of frequent itemsets count, computation time, and peak memory usage, and then integrates the validated rules into a POS application for real-time inference. The system’s effectiveness is measured using a session-level recommendation acceptance rate, defined as the proportion of transactions that display the recommendation modal and result in at least one recommended item being accepted and paid. The results show that Apriori and FP-Growth consistently produce identical sets of frequent itemsets, but with markedly different computational characteristics: Apriori is significantly faster, while FP-Growth exhibits more stable memory usage. In the deployed setting, the recommendation system achieves a session-level acceptance rate of 15.52% in 3,588 transactions, indicating that roughly one in seven sessions with recommendations leads to an additional item being purchased. Compared to many existing works that focus only on algorithmic performance on benchmark datasets, this research contributes a lifespan-aware, empirically benchmarked hybrid ARM approach that is fully integrated into a working POS system and evaluated using real-world acceptance behavior.