Classification of Research Proposal Funding Using Naïve Bayes and Decision Tree Methods

Abstract

In the selection process, determining a university funding research proposal at Tunas Pembangunan University (UTP) still has not fully used information technology to support related institutions, namely the UTP Institute for Research and Community Service (LPPM). So that it has obstacles and requires a long time. So we need a system that is able to help these institutions to make it easier to determine recipients of research proposals that are worthy of funding. The application of data mining is a series of processes to explore added value in the form of knowledge that has not been known manually from a data set. This research has parameters, namely, NIDN, academic degree, track record, a proposed budget plan (RAB), and targeted outcomes. This is certainly less efficient because if a lecturer proposes a proposal, he must wait a long time to find out whether the results are accepted, accepted with improvements, or not. In addition, the assessment process has not used relevant methods so the results of the assessment of research proposal selection are not objective because the results of the assessment of the proposals obtained by the lecturer proposing the proposal are the final results in the form of a feasibility recommendation contained in a decision letter so that the application of classification with criteria in accordance with the selection needs is necessary. research proposal. By applying the data mining algorithm of the Naïve Bayes Method and the Decision Tree, it is hoped that it can simplify and accelerate the LPPM in determining recipients of research proposals that are eligible for funding at Tunas Pembangunan University.