Research Article
Comparative Analysis and Implementation of Time Series Models for Air Quality Prediction in North Sumatra
@INPROCEEDINGS{10.4108/eai.16-9-2025.2361163, author={Suvriadi Panggabean and Faridawaty Marpaung and Zulfahmi Indra and Lasker P. Sinaga}, title={Comparative Analysis and Implementation of Time Series Models for Air Quality Prediction in North Sumatra}, proceedings={Proceedings of the 7th International Conference on Innovation in Education, Science, and Culture, ICIESC 2025, 16 September 2025, Medan, Indonesia}, publisher={EAI}, proceedings_a={ICIESC}, year={2026}, month={3}, keywords={air quality time series prediction arima exponential smoothing north sumatra}, doi={10.4108/eai.16-9-2025.2361163} }- Suvriadi Panggabean
Faridawaty Marpaung
Zulfahmi Indra
Lasker P. Sinaga
Year: 2026
Comparative Analysis and Implementation of Time Series Models for Air Quality Prediction in North Sumatra
ICIESC
EAI
DOI: 10.4108/eai.16-9-2025.2361163
Abstract
Air quality significantly affects public health and the environment, especially in industrial regions like North Sumatra. Accurate air quality prediction is vital for early warnings and policymaking. This study compares three time series forecasting models—Single Exponential Smoothing (SES), Double Exponential Smoothing (DES), and Autoregressive Integrated Moving Average (ARIMA)—to determine the most accurate model for predicting the Air Quality Index (AQI) in North Sumatra. Historical data were collected, preprocessed, and analyzed using the three models. Model performance was evaluated with Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that the ARIMA model achieved the lowest RMSE and MAPE, indicating superior accuracy. This suggests that North Sumatra’s air quality data exhibit complex temporal patterns best captured by ARIMA, which is then applied for short-term forecasting to support a more responsive air quality monitoring system.
