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Ensemble Learning Regression Method for Glucose Concentration Prediction System using Colorimetric Paper-based and Smartphones

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@article{IJASEIT15825,
   author = {Dian Wulan Hastuti and Adhi Harmoko Saputro and Cuk Imawan},
   title = {Ensemble Learning Regression Method for Glucose Concentration Prediction System using Colorimetric Paper-based and Smartphones},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {12},
   number = {3},
   year = {2022},
   pages = {1269--1278},
   keywords = {Ensemble; glucose; MATLAB production server; RESTful API; RPCC; urine.},
   abstract = {Prediction of glucose concentration on android smartphones and colorimetric paper-based using the Ensemble learning regression model has been successfully developed. Several successful developments in our research include automatic image segmentation, image correction using the RPCC method, and the development of a regression model for urine glucose predictions. Furthermore, the model was successfully validated for best performance in the respondent's urine susceptible to color change. We used artificial urine at a 0–2000 mg/dl concentration to create a regression model based on Ensemble learning with the boosting optimization method. In addition, we also compared the Ensemble Bagging regression model and the single learner model, Decision Tree. Server-based applications were also developed using RESTful API communication with two servers: an upload server using Node.js and a computing server using the MATLAB Production Server. The testing process results using artificial urine samples showed that the performance of R2 and RRMSE were 0.98 and 0.05 for the Decision Tree and Ensemble Bagging regression models, respectively. While for the Ensemble Boosting regression model, R2 and RRMSE at the testing process are 0.98 and 0.04. The best validation results using respondents' urine samples are shown in the Ensemble Boosting regression model with R2 and RRMSE performance values of 0.97 and 0.06, respectively. The success rate of the application was 100% on both the Samsung Galaxy A51 and Huawei Nova 5T. This research estimated the glucose concentration reasonably well for health monitoring applications.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=15825},
   doi = {10.18517/ijaseit.12.3.15825}
}

EndNote

%A Hastuti, Dian Wulan
%A Saputro, Adhi Harmoko
%A Imawan, Cuk
%D 2022
%T Ensemble Learning Regression Method for Glucose Concentration Prediction System using Colorimetric Paper-based and Smartphones
%B 2022
%9 Ensemble; glucose; MATLAB production server; RESTful API; RPCC; urine.
%! Ensemble Learning Regression Method for Glucose Concentration Prediction System using Colorimetric Paper-based and Smartphones
%K Ensemble; glucose; MATLAB production server; RESTful API; RPCC; urine.
%X Prediction of glucose concentration on android smartphones and colorimetric paper-based using the Ensemble learning regression model has been successfully developed. Several successful developments in our research include automatic image segmentation, image correction using the RPCC method, and the development of a regression model for urine glucose predictions. Furthermore, the model was successfully validated for best performance in the respondent's urine susceptible to color change. We used artificial urine at a 0–2000 mg/dl concentration to create a regression model based on Ensemble learning with the boosting optimization method. In addition, we also compared the Ensemble Bagging regression model and the single learner model, Decision Tree. Server-based applications were also developed using RESTful API communication with two servers: an upload server using Node.js and a computing server using the MATLAB Production Server. The testing process results using artificial urine samples showed that the performance of R2 and RRMSE were 0.98 and 0.05 for the Decision Tree and Ensemble Bagging regression models, respectively. While for the Ensemble Boosting regression model, R2 and RRMSE at the testing process are 0.98 and 0.04. The best validation results using respondents' urine samples are shown in the Ensemble Boosting regression model with R2 and RRMSE performance values of 0.97 and 0.06, respectively. The success rate of the application was 100% on both the Samsung Galaxy A51 and Huawei Nova 5T. This research estimated the glucose concentration reasonably well for health monitoring applications.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=15825
%R doi:10.18517/ijaseit.12.3.15825
%J International Journal on Advanced Science, Engineering and Information Technology
%V 12
%N 3
%@ 2088-5334

IEEE

Dian Wulan Hastuti,Adhi Harmoko Saputro and Cuk Imawan,"Ensemble Learning Regression Method for Glucose Concentration Prediction System using Colorimetric Paper-based and Smartphones," International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 3, pp. 1269-1278, 2022. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.12.3.15825.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Hastuti, Dian Wulan
AU  - Saputro, Adhi Harmoko
AU  - Imawan, Cuk
PY  - 2022
TI  - Ensemble Learning Regression Method for Glucose Concentration Prediction System using Colorimetric Paper-based and Smartphones
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 12 (2022) No. 3
Y2  - 2022
SP  - 1269
EP  - 1278
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Ensemble; glucose; MATLAB production server; RESTful API; RPCC; urine.
N2  - Prediction of glucose concentration on android smartphones and colorimetric paper-based using the Ensemble learning regression model has been successfully developed. Several successful developments in our research include automatic image segmentation, image correction using the RPCC method, and the development of a regression model for urine glucose predictions. Furthermore, the model was successfully validated for best performance in the respondent's urine susceptible to color change. We used artificial urine at a 0–2000 mg/dl concentration to create a regression model based on Ensemble learning with the boosting optimization method. In addition, we also compared the Ensemble Bagging regression model and the single learner model, Decision Tree. Server-based applications were also developed using RESTful API communication with two servers: an upload server using Node.js and a computing server using the MATLAB Production Server. The testing process results using artificial urine samples showed that the performance of R2 and RRMSE were 0.98 and 0.05 for the Decision Tree and Ensemble Bagging regression models, respectively. While for the Ensemble Boosting regression model, R2 and RRMSE at the testing process are 0.98 and 0.04. The best validation results using respondents' urine samples are shown in the Ensemble Boosting regression model with R2 and RRMSE performance values of 0.97 and 0.06, respectively. The success rate of the application was 100% on both the Samsung Galaxy A51 and Huawei Nova 5T. This research estimated the glucose concentration reasonably well for health monitoring applications.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=15825
DO  - 10.18517/ijaseit.12.3.15825

RefWorks

RT Journal Article
ID 15825
A1 Hastuti, Dian Wulan
A1 Saputro, Adhi Harmoko
A1 Imawan, Cuk
T1 Ensemble Learning Regression Method for Glucose Concentration Prediction System using Colorimetric Paper-based and Smartphones
JF International Journal on Advanced Science, Engineering and Information Technology
VO 12
IS 3
YR 2022
SP 1269
OP 1278
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Ensemble; glucose; MATLAB production server; RESTful API; RPCC; urine.
AB Prediction of glucose concentration on android smartphones and colorimetric paper-based using the Ensemble learning regression model has been successfully developed. Several successful developments in our research include automatic image segmentation, image correction using the RPCC method, and the development of a regression model for urine glucose predictions. Furthermore, the model was successfully validated for best performance in the respondent's urine susceptible to color change. We used artificial urine at a 0–2000 mg/dl concentration to create a regression model based on Ensemble learning with the boosting optimization method. In addition, we also compared the Ensemble Bagging regression model and the single learner model, Decision Tree. Server-based applications were also developed using RESTful API communication with two servers: an upload server using Node.js and a computing server using the MATLAB Production Server. The testing process results using artificial urine samples showed that the performance of R2 and RRMSE were 0.98 and 0.05 for the Decision Tree and Ensemble Bagging regression models, respectively. While for the Ensemble Boosting regression model, R2 and RRMSE at the testing process are 0.98 and 0.04. The best validation results using respondents' urine samples are shown in the Ensemble Boosting regression model with R2 and RRMSE performance values of 0.97 and 0.06, respectively. The success rate of the application was 100% on both the Samsung Galaxy A51 and Huawei Nova 5T. This research estimated the glucose concentration reasonably well for health monitoring applications.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=15825
DO  - 10.18517/ijaseit.12.3.15825