PENINGKATAN AKURASI SISTEM REKOMENDASI PRODUK MENGGUNAKAN ALGORITMA ANT COLONY OPTIMIZATION

Zaenuddin; Wildan Jazuli; Rini Fath Marsya; Devi Wulandari

Penulis

Zaenuddin Universitas Mitra Bangsa Penulis
Wildan Jazuli Universitas Mitra Bangsa Penerjemah
Rini Fath Marsya Penerjemah
Devi Wulandari Universitas Mitra Bangsa Penerjemah

Kata Kunci:

Recommendation system, Ant Colony Optimization, Related Product, Accuracy

Abstrak

Product recommendation systems play a crucial role in helping users discover products that align with their preferences, particularly on e-commerce platforms. However, the main challenge lies in improving recommendation accuracy to ensure that the suggested items are truly relevant. This study proposes the application of the Ant Colony Optimization (ACO) algorithm to enhance the accuracy of recommendation systems. ACO is a metaheuristic algorithm inspired by the behavior of ants in finding the shortest path to a food source, which is adapted here to search for optimal product combinations based on users’ interaction history. Experimental results show that integrating ACO with a collaborative filtering-based approach improves recommendation accuracy by up to 34% compared to conventional methods. These findings contribute to the development of more intelligent and adaptive recommendation systems.

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