Predicting New Student Admissions with the SVM Regression Model in Data Mining

Pradani Ayu Widya Purnama; Nurmaliana Pohan; Adiddo Restiady

doi:10.36378/jtos.v8i2.5035

Pradani Ayu Widya Purnama Universitas Putra Indonesia YPTK Padang
Nurmaliana Pohan Universitas Putra Indonesia YPTK Padang
Adiddo Restiady Universitas Islam Sumatera Barat

DOI: https://doi.org/10.36378/jtos.v8i2.5035

Keywords: Students, Prediction, Data Mining, Linear Regression, SVM Method

Abstract

Prediction is an action to predict future conditions based on past data. One method for making predictions that can be used is the linear regression method. The linear regression method itself consists of two types, simple linear regression and multiple linear regression. One method that uses past data to make predictions is the linear regression method. Regression is a statistical calculation to test how closely the relationship between variables. The simplest and most frequently used regression analysis is simple linear regression. In regression analysis, there is one dependent variable usually written with the symbol Y and one or more independent variables usually written with the symbol X. The relationship between the two variables has a linear nature according to its name. The SVM method was chosen for data mining analysis in this study. There are two parameters used: Exam Scores and Admission Status. This research uses recapitulation data on the acceptance of new students at Play Group & Kindergarten Rahmah Abadi with a total of 50 people. Based on the analysis results, an accuracy rate of 91%.

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