Open AccessEnsemble Analysis of the Students Length of Study at University of Klabat Manado Indonesia
Author(s) -
Niel Ananto,
Ani Budi Astuti,
Achmad Efendi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1811/1/012079
Subject(s) - boosting (machine learning) , ensemble learning , parametric statistics , set (abstract data type) , training set , computer science , artificial intelligence , statistics , ensemble forecasting , point (geometry) , machine learning , mathematics , pattern recognition (psychology) , data mining , geometry , programming language
The purpose of this study is to classify the student’s length of study based on the status of graduating on time or not on time based on several independent variables observed, namely gender, Grade Point Average (GPA), place of residence, type of parents occupation and school origin. The statistics used in this study is non-parametric statistics with a classification analysis method. The classification analysis is to find a training set model of the training set that distinguishes records into appropriate categories or classes. The method used is classification using ensemble techniques. The basic principle of the ensemble method is to develop a set of models from training data and combine a set of models to determine the final classification. The final classification is based on the largest collection of votes from a combination of a set of models. To get the best combination of models, the ensemble method enables the use of several different classification models. The ensemble method used in this study is Bagging and Boosting.