Graduate school of Bogor Agricultural University (SPs-IPB) stated that not all students of IPB master program successfully complete their studies. This becomes an evaluation for IPB to be more selective in choosing students in the future. This study aims to model the success classification of IPB master students in 2011 to 2015. The classification method used is rotation forest. The percentage of students who graduated is very large compared to those who did not pass, this can cause the evaluation value different. SMOTE (Synthetic Minority Oversampling Technique) is one of method to handle such unbalanced data by generating artificial data. The ROC (Receiver Operating Characteristic) curve is built to see the optimum cut off value. There are two classification models, they are rotation forest models before and after handled by SMOTE. The comparison results show that the rotation forest model after SMOTE with cut off value 0.6 is the best model. This model can increase the sensitivity value more than 50% although the accuracy and specificity value decreased compared to the modeling before SMOTE.
Semiparametric model in statistical downscaling (SD) consists of parametric and nonparametric functional relationship between a local scale variable as the response and global scale variables as the predictors. The local variable is rainfall intensity and the global variables are the precipitation of Global Circulation Model (GCM) output. Because of the multicollinearity problem in GCM output, principal component analysis is used to reduce dimension of the predictors to a number of orthogonal components. Usually, SD uses principal component regression (PCR) as a parametric model with the components as predictors. However, the rainfall prediction using the PCR is not quite accurate. In this research the rainfall prediction is improved using a semiparametric model. This model treats some components as parametric and the others as non parametric predictors because of the fact that not all of the components used in the model are linearly related to rainfall. The semiparametric model development uses mixed model approach where the nonparametric relationship is represented using spline with truncated power basis. The model is developed without and with dummy variables. The dummy variables are based on heterogeneous residual variance resulted from partial least square regression. The model performance is evaluated based on the values of RMSEP (Root Mean Square Error of Prediction) and R 2 (Coefficient of Determination). The result shows that the model with dummy 4372 Aji Hamim Wigena, Anik Djuraidah, and Akbar Rizki variables (RMSEP=32.58 and R 2 =97.08%) is better than that without dummy variables (RMSEP=68.88 and R 2 =79.94%).
The Postgraduate School of IPB has academic standards as well as high competitiveness of graduates who have spread both at home and abroad. In this study Binary Logistic Regression method was used to determine the factors that influence the success of the study of Postgraduate students of Bogor Agricultural University (Graduate School-IPB). The data used are data from IPB Graduate School students who graduated from 2011 to 2015. The response variable used is the success status of student studies namely graduating and not passing and using 9 explanatory variables namely gender, marital status, admission status when entering S2, college status S1 level, the source of S2 education costs, group of agencies working, S2 study program groups, age when entering S2 and S1 GPA. The data obtained is not balanced with the percentage of students who graduate is greater than those who did not pass, so the imbalance of data is handled with SMOTE if it is not handled it will cause a misclassification. Comparison of classification results seen in testing data. The results in the model before SMOTE have an area under the curve or AUC of 0.6760, an accuracy value of 88.77%, a sensitivity value of 99.09% and a specificity of 4.63%. The model after 600% oversampling SMOTE has an AUC value of 0.6642, an accuracy value of 78.36%, a sensitivity value of 83.65%, and a specificity value of 35.18%. Although the accuracy of the model and sensitivity value before SMOTE was higher than the model after SMOTE, the specificity in the model after SMOTE was higher, which meant that the model after SMOTE was better at predicting minority classes (not graduating).
IPB University (IPB) is one of the best universities in Indonesia, based on the Ministry of Education and Culture (Kemendikbud) clustering in 2020. As the best university, IPB requires efforts to improve the quality of its education. One of these efforts is to improve student achievement. This study aims to identify the factors that influence the competition and non-competition achievements of undergraduate students at IPB. The data used are achievement data (academic year 2016/2017 to 2020/2021) from the Directorate of Student Affairs and Career Development (Ditmawa) of IPB and demographic data of undergraduate level IPB students (entry year 2016/2017 to 2019/2020) from the Directorate of Administration and Education (Dit-Ap) IPB. The analytical method used in this study is the Chi-square Automatic Interaction Detection (CHAID) classification method. There was an imbalance of data on the Student Achievement response variable. Therefore, in this study, unbalanced data handling was also carried out by resampling in the form of oversampling, undersampling, and over-undersampling methods. The results showed that the classification using CHAID analysis with resampling in the form of oversampling with a balance accuracy of 73.7% resulted in the best classification performance. The factors that influence student achievement are 11 variables, and the 3 most influential variables are variables of year of admission, department, and last GPA.
Stability of a measuring tool is very important. It is necessary to get measurement results that have high precision. This research was conducted to see the stability of Non-Invasive Blood Glucose Measurement Tool. Data of this research is data research from "Pengembangan dan Uji Klinis Purwarupa Alat Pemantauan Kadar Glukosa Darah Non-Invasif Institut Pertanian Bogor". This research used analysis of statistical process control using Individual Moving Range control chart. The results obtained indicate stability process on each respondent. However, among respondents who are one with other respondents has an unstable process despite glucose levels the blood of the respondents is the same.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.