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All Journal Jurnal Sains & Teknologi Lingkungan JOURNAL OF CIVIL ENGINEERING, BUILDING AND TRANSPORTATION
Athallah, Yusron Faiz
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Education

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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.
Analisis Performa Indeks Stabilitas Termodinamik Radiosonde dalam Memprediksi Kejadian Thunderstorm di Bandar Udara Juanda: Indonesia Rafi, Rayhan; Athallah, Yusron Faiz; Haryanto, Yosafat Donni
Jurnal Sains & Teknologi Lingkungan Vol. 18 No. 2 (2026): SAINS & TEKNOLOGI LINGKUNGAN
Publisher : Teknik Lingkungan Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/jstl.vol18.iss2.art1

Abstract

Operational flight safety at Juanda International Airport relies heavily on the accuracy of short-term weather predictions (nowcasting), particularly in anticipating significant convective weather phenomena. This study aims to evaluate the performance of six thermodynamic instability indices (CAPE, K-Index, Lifted Index, Total-Totals, Showalter Index, and SWEAT) derived from 00.00 UTC radiosonde data in predicting Thunderstorm events occurring between 00.00–06.00 UTC. Using observational data from September 2024 to August 2025, verification was conducted through visual distribution analysis (Box Plot) and statistical contingency table scores (POD, FAR, CSI). Seasonal analysis shows that instability indices exhibit higher sensitivity during the Rainy Season (DJF) compared to the Dry Season (JJA), consistent with the greater frequency of convective events in that period. Quantitatively, the Lifted Index (LI) demonstrates relatively superior validation performance, indicated by the highest Critical Success Index (CSI) among all indices. This suggests that stability parameters based on parcel temperature differences are more representative of local atmospheric conditions than purely energy-based parameters such as CAPE. However, the generally low CSI values (< 0.2) indicate that these indices still have limited sensitivity in capturing local atmospheric dynamics at Juanda, as reflected by the relatively high occurrences of Misses and False Alarms. This study recommends prioritizing the use of LI, while further investigation on local threshold adjustments and extending the temporal scale of analysis is necessary to improve predictive performance.
Co-Authors Abigael, Febby Debora Agung Hari Saputra Haryanto, Yosafat Donni Rafi, Rayhan Sugiarto, Cahya