IMPLEMENTATION OF K-MEANS ALGORITHM FOR INFORMATION TECHNOLOGY FRESHMAN CLASS DIVISION

Abstract

Almost all univerities divide their Information Technology freshman into classes randomly or based on student score, either their score during the selection test held by the university or National Examination score. Universities often find the case that a class consists of all 'snart students and a class consists of all 'lazy' students. This thesis intends to create an application to help divides their Information Technology freshman into classes based on freshman competency and experience about IT on their senior high school. The application consists of two parts: Web Survey and Python Program. The data is collected by providing survey form on the Web Survey and the data is processed using Python Program to retrieve information about freshman competency ad show the class division suggestion for universities. The experiment is conducted by collecting data IT students who are not in the first semester. The data consists of their experience about IT as well as other knowledge fields and their current GPA. The current GPA is needed to compare the relationship between their experience about IT and ohter knowledge fields in senior high school and their GPA on the campus. The result of the experiment show that from 50 data samples collected, the application correctly predicts 34 students GPA range based on respondents competency with IT and other knowledge fields during their study in senior high school.