DWI NUR FITRIANTO, -
(2024)
Implementation of the Ordinal Logistic Regression Method for Air Quality Classification Based on the Air Pollution Standard Index.
International Journal of Multidisciplinary Research and Growth Evaluation.
ISSN 2582-7138
Abstract
Air pollution is a serious problem in many cities around the world, caused by human and natural activities. Jakarta, as an Indonesian economic and transportation hub, faces serious air pollution challenges. This study uses the Ordinal Logistics Regression Method to develop an optimal classification model for identifying air quality based on the air pollution index. The aim is to contribute to dealing with air contamination and improve understanding of the use of such methods in the classification of air quality. Data used from 2012 to 2021 covered parameters such as PM10, SO2, CO, O3, and NO2. Oversampling is done using SMOTE to address imbalances in the datasets used. The data used is divided into two parts: training data and validation test data, with an 80:20 ratio. The model training and testing process is carried out with a variety of scenarios, including parameter significance tests using probability tests and Wald tests, cross-validation with fold numbers 5, 10, and 15, as well as evaluation using a confusion matrix. Models are used for Mord libraries such as Ordinalridge, LogisticAT, logisticIT, and LogisticSE. There were a total of 72 model testing experiments to find the best model of the six data model outputs. The test results showed that the optimal model was obtained with a data deletion scenario of null values, data oversampling using the LogisticIT model, and K-fold = 5, with a training accuracy of 0.8628 and validation data test accuracy of 0.8599, as well as each precision value of 0.86, recall value of 0.86, and F1-score of 0.86. The model's performance was satisfactory in handling different data variations, according to the evaluation. These results show that the model is able to generalize data well and is reliable in predicting air quality accurately.
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