COMPARISON OF AIR QUALITY DATA ACCURATION USING DECISION TREE AND NEURAL NETWORK METHOD

Fahmi Izhari (Universitas Pembangunan Pancabudi Medan)
Hanna Willa Dhany (Universitas Pembangunan Pancabudi Medan)

pendidikan, science, teknologi, dan ekonomi
Vol 14, No 2 (2020): JIT

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.