IMPLEMENTASI METODE REGRESI DALAM
MEMPREDIKSI KELULUSAN MAHASISWA INSTITUT
TEKNOLOGI DIRGANTARA ADISUTJIPTO YOGYAKARTA
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Institusion
INSTITUT TEKNOLOGI DIRGANTARA ADISUTJIPTO
Author
SULASTRY SALADAN, SRY
Subject
Q Science (General)
Datestamp
2024-04-01 02:56:30
Abstract :
Student graduation is an important factor of a college/high school. The percentage of student passing up and down each year is an evaluation material for universities, where the ability of student to graduate quickly is an important element for accreditation. Prediction of student graduation is very important for the university, which can monitor it directly using a student prediction system because from students who are known from an early age that they cannot graduate on time, it can be handled immediately how to graduate on time. To overcome the existing problems, this study predict student graduation using the Regression Method. Training data used to predict student graduation from 2015- 2020. The training data entered are 63 data with variables X1 as credits, X2 as GPA and Y as length of study, the decision criteria are based on predictions of length of study. If the predicted length of study is 4 years, the student will graduate on time, and if the predicted length of study is > 4 years, the student will not graduate on time.
Student graduation is an important factor of a college/high school. The percentage of student passing up and down each year is an evaluation material for universities, where the ability of student to graduate quickly is an important element for accreditation. Prediction of student graduation is very important for the university, which can monitor it directly using a student prediction system because from students who are known from an early age that they cannot graduate on time, it can be handled immediately how to graduate on time. To overcome the existing problems, this study predict student graduation using the Regression Method. Training data used to predict student graduation from 2015- 2020. The training data entered are 63 data with variables X1 as credits, X2 as GPA and Y as length of study, the decision criteria are based on predictions of length of study. If the predicted length of study is 4 years, the student will graduate on time, and if the predicted length of study is > 4 years, the student will not graduate on time.
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