Strategi Maximum Profit Algoritma Greedy dalam Kecerdasan Buatan Penentu Aktivitas Fisik pada Mobile Exergame - Siti Dwi Setiarini; Sofy Fitriani; Hashri Hayati (Journal of Computer System and Informatics (JoSYC), 2023)
The Covid-19 pandemic has started to subside. However, after the pandemic subsided the obesity rate in Indonesia continued to increase. The new habits formed after the pandemic are activities that can be done at home. This is an opportunity to be able to reduce obesity by doing physical activity at home. Physical activity undertaken specifically to lose weight. However, various problems arise when doing physical activity. Such as requiring expensive fees, monotony, and others. Mobile exergame is one solution to this problem. In previous research, exergames that use the brute force algorithm in determining physical activity take a long time. This problem will be solved using a greedy algorithm which also uses a maximum profit strategy. Descriptive statistics are used to compare the average time needed by the two algorithms to select the physical activity to be performed in an exergame. The results of this study indicate that in terms of time the greedy algorithm that uses the maximum profit strategy is proven to be faster than the brute force algorithm.