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Hanna Willa Dhany
Universitas Pembangunan Pancabudi Medan
Education Public Health

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ANALYSIS OF ACCURACY IMPROVEMENT IN RANDOM FOREST USING PRINCIPAL COMPONENT ANALYSIS (PCA) Hanna Willa Dhany; Muhammad Iqbal
Jurnal Ipteks Terapan Vol 14, No 2 (2020): JIT
Publisher : LLDIKTI Wilayah X

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (598.262 KB) | DOI: 10.22216/jit.2020.v14i2.5377

Abstract

Decision tree is used to classify a data that still does not know its class to existing classes. The data testing path is the first step that the root node goes through and finally the leaf node will predict the class for the data that has been concluded. Random Forest cannot be relied on for data types that have different categorical variables and therefore needs to be improved in the classification process, this is influenced by differences in the value of the variable. Therefore a method is needed to reduce features that are less relevant to the process of determining accuracy in the classification of the Random Forest method. In research conducted on the PCA + Random Forest classification model, using the Water Quality Status Dataset that has been simplified into 5 attributes, 4 classes and 117 instances with an accuracy rate of 91.43% with a classification error rate of 8.57%. Based on the test results from the four classification models, it can be concluded that the success of the PCA can be used as a reference to improve the accuracy performance of the Random Forest classification model
COMPARISON OF AIR QUALITY DATA ACCURATION USING DECISION TREE AND NEURAL NETWORK METHOD Fahmi Izhari; Hanna Willa Dhany
Jurnal Ipteks Terapan Vol 14, No 2 (2020): JIT
Publisher : LLDIKTI Wilayah X

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22216/jit.2020.v14i2.5389

Abstract

In research conducted on the Neural Network classification model that has been tested has an accuracy of 82.04% with a classification error rate of 17.96%. Meanwhile, the Decision Tree classification model has an accuracy rate of 99.38 %% with a classification error rate of 0.62%. Based on the test results from the two classification models, it can be concluded that the success of the Decision Tree can be used as a reference to improve the performance of the classification model's accuracy compared to the Neural Network Backpropagation model.
Co-Authors Fahmi Izhari Muhammad Iqbal