PURCHASE GOODS OPTIMIZATION RECOMMENDATIONS USING GENETIC ALGORITHM METHOD - Ezra Pratama, Reynara
Industrial Revolution 4.0 makes technology fully utilized, giving rise to many new digital-based businesses. The use of digital-based businesses has advantages such as easy product marketing because it can reach a wider location. This change makes many choices that can be taken in buying an item. This increasing number of choices can certainly encounter problems because every human being has limited time and costs. Therefore, in this final project, a recommendation for purchasing goods flow will be built using the Genetic Algorithm method. The Genetic Algorithm method works on a population basis and applies the principle of natural selection. In this final project, the author determines the purchase flow of goods, namely computer components and accessories which are divided into three cases by considering several factors such as the price of goods, the price of shipping costs, transportation costs, and the rating of each computer component and accessories. The first case is to make a purchase recommendation by considering one factor the total price is obtained from the sum of the total price of goods, the total price of shipping costs, and the total transportation costs. The second case is to make a purchase recommendation by considering two factors, namely the total price and the rating of each computer component and accessories. The third case is to make a purchase recommendation by considering four factors, namely the total price of goods, the total price of shipping costs, the total transportation costs, and the rating of each computer component and accessories. The simulation formation in this final project uses the python programming language. Based on the simulation results, the Genetic Algorithm method succeeded in solving the problem to get recommendations for purchasing goods in the form of computer components and accessories. The use of a larger population and generation sizes will make the recommendations produced have more optimum results.