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Journal : Buletin GAW Bariri ( BGB)

Analisis Arah dan Kecepatan Angin Permukaan Menggunakan Digaram Wind Rose di Bandara Internasional Sultan Hasanuddin Prasetiyo, Adi
Buletin GAW Bariri (BGB) Vol 5 No 2 (2024): BULETIN GAW BARIRI
Publisher : Stasiun Pemantau Atmosfer Global Lore Lindu Bariri - Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/bgb.v5i2.133

Abstract

Sultan Hasanuddin International Airport is situated among hills and lowlands, resulting in highly variable wind patterns. In aviation operations, winds are categorized into three types: headwind, tailwind, and crosswind. The most hazardous for flight operations are tailwinds and crosswinds exceeding 10 knots. To ensure flight safety at Sultan Hasanuddin International Airport, it is essential to understand the patterns of wind direction and speed, which can be analyzed using a wind rose diagram. The data used in this study includes wind direction and speed measurements from 2014 to 2023 obtained from METAR (Meteorological Aerodrome Report) at the Sultan Hasanuddin Class I Meteorological Station. The results indicate that the dominant wind direction from 2014 to 2023 at Sultan Hasanuddin International Airport is from the east (67.5°  –  112.5°), with the highest wind speed recorded during this period being ≥ 21.58 knots. Crosswind or aligned wind conditions with runways 03 – 21 and 13 – 31 still show wind speeds exceeding 10 knots, which poses a potential hazard of crosswinds or tailwinds for aircraft.
Pemanfaatan Metode Fuzzy Logic dalam Memprakirakan Hujan (2025 – 2030) di Stasiun Meteorologi Kelas I Sultan Hasanuddin Prasetiyo, Adi; Husain, Husain; Subaer, Subaer; Arsyad, Muhammad; Palloan, Pariabti
Buletin GAW Bariri (BGB) Vol 6 No 1 (2025): BULETIN GAW BARIRI
Publisher : Stasiun Pemantau Atmosfer Global Lore Lindu Bariri - Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/bgb.v6i1.146

Abstract

Sultan Hasanuddin International Airport is an airport with a unique topography, making the process of rainfall formation at this airport very dynamic. To ensure safe flight operations at Sultan Hasanuddin International Airport, rainfall forecasting is needed using fuzzy logic methods, with input data influencing rainfall formation, such as precipitable water, relative vorticity, and divergence. In this study, the data used for applying fuzzy logic can be divided into three types: training data (for developing the fuzzy logic method), validation data (for validating the fuzzy logic method), and testing data (for testing the fuzzy logic method). Therefore, validating the fuzzy logic method to obtain results and accuracy of rainfall events, as well as testing the fuzzy logic method for rainfall event forecasting, are the goals of this research. The precipitable water, relative vorticity, divergence, and rainfall data in this study are divided into three types: training data (2010 – 2021), validation data (2022 – 2024), and testing data (2025 – 2030). The validation results for 2022 – 2024 were dominated by non – rain events, with 7.051 occurrences, while there were 948 occurrences of rain events. The accuracy of the fuzzy logic validation method was found to be 78.58% during 2022 – 2024, allowing the fuzzy logic method to be applied for forecasting rainfall events in 2025 – 2030, beginning with the creation of input data using the moving linear regression algorithm. The forecasting results for 2025 – 2030 using the fuzzy logic method were dominated by non – rain events, with 15.232 occurrences, while there were 2.296 occurrences of rain events.
Pemanfaatan Algoritma Decision Tree C4.5 dalam Memprakirakan Hujan di Stasiun Meteorologi Kelas I Sultan Hasanuddin Prasetiyo, Adi
Buletin GAW Bariri (BGB) Vol 5 No 1 (2024): BULETIN GAW BARIRI
Publisher : Stasiun Pemantau Atmosfer Global Lore Lindu Bariri - Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/bgb.v5i1.120

Abstract

Rain can have various impacts on human life, one of which is the impact on the world of aviation. To minimize the impact caused by rain in the world of aviation, rain forecasts are needed to facilitate operational activities at an airport, including Sultan Hasanuddin International Airport, which is one of the busiest airports in Indonesia. In forecasting rain, generally the data used is data that influences the formation of rain, such as data related to the amount of water vapor (precipitable water) and wind (relative vorticity and divergence). Even though the data used in forecasting rain is correlated with the formation of rain, there is the potential for poor forecast accuracy due to non – continuous supporting data for predicted rain events because atmospheric conditions are very complex and can change rapidly. To minimize rain forecasts with poor accuracy, a method is needed that can process non – continuous data regarding future rain events well, one of which is using the Decision Tree C4.5 algorithm. Decision Tree C4.5 is a machine learning algorithm that involves selecting the best features at each step so that it has the potential to produce good forecasts. In this study, the forecast results for a year were dominated by 2590 no rain events, while the total number of rain forecasts was 330 events. Accuracy of monthly forecasts was found to range from 64.92% to 100%, where if the number of correct and incorrect forecasts for each month were combined, the forecast accuracy for a year was 84%, where this accuracy could be said to be very good.