Implementasi Algoritma Adaptive Neurofuzzy Inference System (Anfis) Dalam Memprediksi Komitmen Mahasiswa Baru Ittp
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Institusion
Institut Teknologi Telkom Purwokerto
Author
Violita, Anggraini
Subject
TA Engineering (General). Civil engineering (General)
Datestamp
2022-09-01 03:11:05
Abstract :
The development of industry in the field of education especially in admission, creates competition between them. Admission in Indonesia have increased from year to year, currently the number of admissions has reached 4482 units. One of the strategies that must be taken by the management at a university is to manage the number of students who enroll in the college. The problem in this research is admission are difficult to predict whether prospective students have a big commitment. This study aims to provide work efficiency and a reliable system as a predictive model in determining student commitment to continue their registration or not, using a prediction method called the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm. Test results, the value of average error and the average RMSE is getting smaller when the number of iterations increases from 60 to 80 which is worth 37% with value rules 216 and the target error value is 0.1. This method has the ability to predict by reading and learning from input data, where the predictions in this research produce sensitivity values of 80.01%, specificity 70.95% and accuracy 76%. Keywords: Adaptive Neuro fuzzy inference system, Prediction, Student commitment
The development of industry in the field of education especially in admission, creates competition between them. Admission in Indonesia have increased from year to year, currently the number of admissions has reached 4482 units. One of the strategies that must be taken by the management at a university is to manage the number of students who enroll in the college. The problem in this research is admission are difficult to predict whether prospective students have a big commitment. This study aims to provide work efficiency and a reliable system as a predictive model in determining student commitment to continue their registration or not, using a prediction method called the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm. Test results, the value of average error and the average RMSE is getting smaller when the number of iterations increases from 60 to 80 which is worth 37% with value rules 216 and the target error value is 0.1. This method has the ability to predict by reading and learning from input data, where the predictions in this research produce sensitivity values of 80.01%, specificity 70.95% and accuracy 76%. Keywords: Adaptive Neuro fuzzy inference system, Prediction, Student commitment
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