Startup Success Factors: Classifying 3H (Hustler, Hipster, Hacker) Framework using Simple Additive Weighting

Abstract

Nowadays, start-ups are heavily influenced by the character of their founders. The framework in this case is known as 3H which is an explanation of Hustler, Hipster and Hacker. In this study, a decision support system based on the Simple Additive Weighting (SAW) method was built that can determine the tendency of user characteristics to a category. This system is built in a web-based application with 25 closed questions recommended by experts. Each question has its own weight for each choice. Then this process continues to the answer normalisation stage and the total of this normalisation will be converted to a scale of 75 to determine the final category. Then the results will be validated by comparing the results done by the expert and the system. Based on testing conducted with 3 samples, the system managed to get 100% accuracy. However, there are research findings that show the Hustler character if implemented with a method like this research will only be taken if all answers are answers with minimum weight. But basically, this research shows that SAW is a fairly effective method in supporting classification decisions, it's just that improvements are needed on the expert side so that the weights can be done dynamically so that the results are more optimal.