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All Journal JOURNAL OF CIVIL ENGINEERING, BUILDING AND TRANSPORTATION Journal of Computer Science and Engineering (JCSE)
Sugiarto, Cahya
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Civil Engineering, Building, Construction & Architecture Computer Science & IT

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The Utilization of HuberRegressor Machine Learning Model to Predict Carbon Monoxide Concentration in Surabaya City Sugiarto, Cahya; Abigael, Febby Debora; Athallah, Yusron Faiz; Agung Hari Saputra
JOURNAL OF CIVIL ENGINEERING BUILDING AND TRANSPORTATION Vol. 8 No. 1 (2024): JCEBT MARET
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jcebt.v8i1.11262

Abstract

Carbon monoxide (CO) is one of the pollutant gases whose concentration currently continues to increase due to an increase in population and population activities, especially those that occur in the city of Surabaya, East Java. The purpose of this study is to make a prediction of CO gas concentration in Surabaya City in 2022. CO concentration air quality data was obtained from MERRA-2 Reanalysis through NASA's Giovanni platform. CO concentration data processing is carried out by Machine Learning methods using the Google Colaboratory platform with the HuberRegressor model. The results of the data processing carried out were obtained with details of MASE worth 0.6218, RMSSE worth 0.3657, MAE worth 0.0280, RMSE worth 0.0314, MAPE worth 0.0836, and SMAPE worth 0.0876. From the results of the evaluation of the model, it can be concluded that the HuberRegressor model can make a prediction of CO gas concentration in the city of Surabaya quite well.
Monte Carlo Simulation Application for Meteorological Parameter Prediction Rizqi, Muhammad Nur; Sugiarto, Cahya; Afifah, Ghaitsa
Journal of Computer Science and Engineering (JCSE) Vol 6, No 2: August (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

Rainfall in Indonesia, particularly in southern coastal regions such as Cilacap Regency, is strongly influenced by the interaction of multiple meteorological variables. This study aims to predict monthly meteorological parameters consisting of rainfall, air temperature, wind speed, humidity, and solar radiation intensity using the Monte Carlo simulation method based on historical data from 2022 to 2024 obtained from the Tunggul Wulung Cilacap Class III Meteorological Station. The simulation process involved probability distribution fitting and random number generation for 10,000 iterations for each parameter. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that air temperature and humidity achieved the highest predictive accuracy, with MAPE values of 4.04 percent and 3.18 percent. These values indicate high model consistency. Solar radiation intensity and wind speed produced moderate accuracy with MAPE values of 38.83 percent and 44.44 percent. In contrast, rainfall exhibited low predictive performance with a MAPE of 53.13 percent. This low performance is primarily caused by high temporal variability and limited data length. The findings demonstrate that Monte Carlo simulation is effective for predicting meteorological variables with stable patterns but less suitable for parameters with extreme fluctuations such as rainfall 
Co-Authors Abigael, Febby Debora Afifah, Ghaitsa Agung Hari Saputra Athallah, Yusron Faiz Rizqi, Muhammad Nur