Analysis of the Support Vector Machine Algorithm for Predicting New Student Admissions at MA Taruna AL Jabbar

Faustina Wardhana; Felix Felix; Elvis Ompusunggu

doi:10.47709/cnahpc.v8i3.8799

Authors

Faustina Wardhana Universitas Prima Indonesia
Felix Universitas Prima Indonesia
Elvis Ompusunggu Universitas Prima Indonesia

DOI:

https://doi.org/10.47709/cnahpc.v8i3.8799

Keywords:

Classification, Support Vector Machine (SVM), Data Mininng

Abstract

Madrasah Aliyah (MA) Taruna Teknik Al Jabar is an educational institution sponsored by the Ministry of Education and Culture that holds a new student admission program periodically every year. However, the number of students accepted tends to increase or decrease every year. This situation needs to be analyzed carefully because it affects the school's policy in achieving the school's educational goals both qualitatively and quantitatively. This problem requires systematic planning and design to develop effective solutions to improve the quality of new student admissions. The algorithm applied is Support Vector Machine which is a method commonly used for classification and regression. In classification modeling, SVM has a more mature and mathematically clear concept than other classification methods. The results achieved 100% prediction accuracy from 20 data samples, and the results of the prediction data test showed that students with registration number 25023 were declared passed, while students with registration number 25024 were declared failed.

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