Optimizing Smartphone Recommendation System through Adaptation of Genetic Algorithm and Progressive Web Application

The ubiquity of smartphone use nowadays is undeniable exponentially growing, replaced cell phones, and a host of other gadgets replaced personal computers to a certain degree. Different smartphones specifications and overwhelmed smartphone advertisements have caused broader choices for the customer. Many qualitative and quantitative criteria need to consider, and customers want to select the most suitable smartphones. They face difficulties deciding the best smartphone according to their budget and desire. Thus, a new method is needed to recommend the customer according to their preferences and budget. This study proposed a method for optimizing the recommendation system of the smartphone using the genetic algorithm (GA). Moreover, it is implemented with a progressive web application (PWA) platform to ensure the customer can use it on multiple platforms. They can choose the platform to input any specification of smartphone preferences besides the budget. Functional testing results showed the achievement of the study’s objectives, and usability testing using UEQ managed to receive feedback of 93.64%, with an overall average mean of 4.682. Therefore, according to the outcome, it can be concluded that optimizing the smartphone recommendations through GA enables the customer to ease the comparison based on the obtained optimum result.