Implementation Of A Perfume Recommendation System Using Ahp And Topsis At Ivan Parfume - Winda Nurdiana Putri; Dewi Maharani; Abdul Karim Syahputra (Brilliance: Research of Artificial Intelligence, 2026)
The rapid growth of the refill perfume industry requires business owners to enhance service quality, particularly in assisting customers in selecting suitable fragrance products. At Ivan Parfume, the large variety of available scents often causes confusion among customers, while the current recommendation process remains manual and subjective. This study aims to develop a web-based Decision Support System (DSS) using a combination of the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to provide objective and accurate perfume recommendations The AHP method is employed to determine the priority weights of decision criteria, including price, longevity, packaging design, volume, and scent, while the TOPSIS method is used to rank perfume alternatives based on their closeness to the ideal solution. The system processes ten perfume alternatives and generates a ranked list of recommendations based on multi-criteria evaluation. The results indicate that the system is capable of producing structured and consistent recommendations aligned with user preferences. Furthermore, the system demonstrates good performance in handling multiple criteria simultaneously and provides transparent calculation results that can be easily interpreted by users. The implementation of the AHP-TOPSIS model improves decision-making efficiency by reducing subjectivity and processing time compared to conventional methods. This study demonstrates that the proposed system can effectively support retail businesses in delivering data-driven recommendations and enhancing customer satisfaction.