DOI: https://doi.org/10.14710/vol15iss2pp167-172

Increasing the Accuracy of Random Forest Algorithm Using Bagging Techniques in Cases of Stunting Toddlers

*Amir Ali  -  Medical Record and Health Information, Dr. Soetomo Hospital Foundation College Of Health Sciences, Surabaya 60286, Indonesia, Indonesia
Purwanto Purwanto  -  Department of Information Systems, School of Post Graduate Studies, Diponegoro University, Jl. Imam Bardjo S.H., No. 5, Pleburan, Semarang, Indonesia 50241, Indonesia
Mundakir Mundakir  -  Department of Health Faculty, Muhammadiyah Surabaya University, Surabaya, Indonesia 60113, Indonesia
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Abstract

Increasing the accuracy value can be increased by using other algorithms. Increasing the accuracy value of a classification algorithm, the level of success of the algorithm's prediction is more precise and appropriate in providing its label. The purpose of the research is look performance of accuracy value for prediction with bagging algorithm. This research use random forest algorithm and bagging algorithm used for optimization. 12 data whose position is far from other data. 12 data deviate from the data pattern and are outliers. With z-score process, it will be processed to eliminate outlier data. After removing the outlier data, the data clean is 137 toddler data. After removing outliers and standardizing the data, the accuracy value obtained was 71% up to 100th accuracy with random forest algorithm. Optimization of a bagging algorithm to predict stunting in a dataset of toddlers that has been acquired and assessed its performance. This can be seen from the optimization of prediction results up to the 100th iteration, where the prediction accuracy results were 80.67%. Using the Random Forest algorithm and bagging techniques, the prediction of stunting in toddlers works well. Optimization of prediction results up to the 100th iteration, where the prediction accuracy results were 80.67%.

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Keywords: Random Forest; Bagging algorithm; Outlier removal; Stunting prediction; Accuracy Improvement

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Section: Research Articles
Language : EN
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