OPTIMALISASI METODE NAIVE BAYES DAN DECISION TREE UNTUK MENENTUKAN PROGRAM STUDI BAGI CALON MAHASISWA BARU DENGAN PENDEKATAN UNSUPERVISED DISCRETIZATION - Wildani Eko Nugroho; Heru Saputro (Universitas Islam Nahdlatul Ulama Jepara, 2022)
Higher Education is a place for providing education that aims to produce quality human resources and is able to face increasingly fierce job competition. Therefore, from the recruitment process or the admission process, prospective new students must consider various procedures that aim to be able to direct prospective new students in determining the study program that will be taken by prospective new students. The things that have been broken in the admission process for new students include the scores of national exam results, report cards, school test scores and the admission test for new students, as well as the admission process of the achievement path and aiming for missions. From these things, performance must be improved is a supporting factor so that the process of transforming educational science to students can be carried out properly. The purpose of this study is to obtain classification in determining the study program of prospective new students by optimizing the Naïve Bayes and Decision Tree methods with an Unsupervised Discretization Approach, as an effort to improve the internal quality assurance system, especially the standards for the admission process for new students in determining study programs at the Harapan Polytechnic with Tegal. Where in the process of accepting new students, planning, implementing, evaluating, and monitoring have been carried out as a form of implementing the Internal Quality Assurance System (SPMI). In this study, the data used was data on the results of the admission of prospective new students from all study programs. These data include data on the administrative completeness of the requirements of prospective new students, as well as data on the value of the results of the new student admission test. The data used is data for 1 academic year 2019/2020. From this data, training and testing will be carried out using Rapidminer 9, a classification of lecturer teaching performance will be obtained.