Prediction of Feasibility of Entrepreneurial Proposals in Student Creativity Program

Abstract

Student Creativity Program is a program organized by the Directorate of Learning and Student Affairs, Directorate General of Higher Education, Research, and Technology, Ministry of Education, Culture, Research and Technology as a national level student creativity event as an effort to grow, accommodate, and realize students' creative and innovative ideas. Based on 2017-2021 data, each year an average of 63,337 proposals are received, administrative and substance evaluations involve complex assessment components and are carried out manually so that it takes a relatively long time in the calculation process. Then a special method is needed that speeds up the processing of assessment data. This research was conducted on the substance of the Entrepreneurship Sector to predict the feasibility of a proposal to get funding applying data mining with the Naive Bayes Classifier (NBC) and K-Nearest Neighbor (K-NN) algorithms with a comparison between Euclidean Distance and Manhattan Distance. From the results, it is known that NBC produces 96.49% accuracy and 0.912 Kappa. K-NN with the largest Euclidean Distance calculation in K-5, K-7 and K9 with an accuracy of 99.04% and Kappa 0.975, K-NN Manhattan Distance calculation produces the greatest accuracy of all the methods used by researchers, namely 100% and Kappa 1,00 categorized as Excellent. So the conclution is that the K-NN method with K-5 which produces the greatest accuracy and Kappa can be recommended to PKM stakeholders in funding feasibility algorithms.