Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal JOURNAL OF CIVIL ENGINEERING, BUILDING AND TRANSPORTATION JTIK (Jurnal Teknik Informatika Kaputama)
Abigael, Febby Debora
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PEMANFAATAN NUMERICAL WEATHER PREDICTION DAN CITRA SATELIT HIMAWARI-9 DALAM ANALISIS KONDISI ATMOSFER SAAT HUJAN LEBAT: (Studi Kasus 14 Maret 2024) Rafi, Rayhan; Syahid, Wisnu; Kaizzi Larasati, Kanaya; Aydin Umardani, Syarif Abdillah; Abigael, Febby Debora; Kristianto, Aries
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 9 No. 1 (2025): Volume 9, Nomor 1, Januari 2025
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v9i1.910

Abstract

Heavy rainfall occurred in the Special Region of Yogyakarta on March 14, 2024. This rainfall event was categorized as extreme weather, as data from the Regional Disaster Management Agency (BPBD) reported damage in 496 affected locations. Heavy rainfall can occur due to atmospheric instability caused by the growth of convective clouds (cumulonimbus). The phenomenon of heavy rainfall was monitored using remote sensing systems in the form of satellites to observe and analyze the event. Yogyakarta's topography explains the use of ECMWF ERA-5 model data to identify wind distribution patterns (streamlines) influenced by westerly winds. The Convective Cloud Overlay (CCO), red-green-blue (RGB), and High-resolution Cloud Analysis Information (HCAI) methods were applied to interpret cumulonimbus cloud development, observed from the formation phase (08:00 UTC) to the dissipation phase (18:00 UTC). Observations indicated a decrease in cloud-top temperature to -80°C at 09:00 UTC, followed by dissipation with a temperature of -20°C at 18:00 UTC. Atmospheric instability indices were analyzed using numerical weather prediction (NWP) methods to obtain quantitative values for indices contributing to heavy rainfall, such as SSI, LI, KI, TT, SWEAT, and CAPE. This study concluded that a "moderate" increase in instability index values explained why convective cloud development occurred.
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.
Co-Authors Agung Hari Saputra Aries Kristianto Athallah, Yusron Faiz Aydin Umardani, Syarif Abdillah Kaizzi Larasati, Kanaya Rafi, Rayhan Sugiarto, Cahya Syahid, Wisnu