Implementation of the Simple Additive Weighting Algorithm for Café Recommendations in Lhokseumawe City

• Published in: International Journal of Engineering Science and Information Technology Vol. 5; no. 3; pp. 73 - 79
• Main Authors: Arkan, Raihan, Safwandi, Safwandi, Ar Razi, Ar Razi
• Format: Journal Article
• Language: English
• Published: 12.05.2025
• ISSN: 2775-2674; 2775-2674
• DOI: 10.52088/ijesty.v5i3.885

The selection of cafés that match customer preferences is a challenge, especially in the city of Lhokseumawe, which has 30 cafés with different characteristics. This research implements the Simple Additive Weighting (SAW) algorithm to provide recommendations for the best café based on six criteria, namely price (weight 0.25), menu (0.2), order duration (0.15), service (0.2), facilities (0.15), and discounts promotions (0.05). The recommendation system was developed using a combination of Laravel PHP and Python, where Laravel is used to build an interactive web interface. Python also plays a role in data processing and complex mathematical calculations. The results showed that the system was able to provide optimal recommendations, with Petrodollar Coffeeatery Roastery as the top choice based on the calculation of the highest preference values (3.28 for price, 2.48 for menu, 3.16 for order duration, 2.88 for service, 2.96 for facilities, and 2.8 for discounts promotions). TR Coffee and Platinum Coffee occupy the following positions. In addition, this study found that the weight of the criteria and the number of datasets (150 reviewers) significantly influence the quality of recommendations. The more representative the weights used and the larger the dataset analyzed, the more accurate the system will produce recommendations based on user preferences. Thus, weight optimization and dataset expansion are essential factors in improving the effectiveness of SAW-based recommendation systems.