OTOMATISASI TRANSFER DATA PENGAMATAN AUTOMATIC WEATHER STATION (AWS) SERTA PEMANFAATANNYA DALAM SATELLITE DISASTER EARLY WARNING SYSTEM (SADEWA)

Purwalaksana, Ahmad Zatnika (2015) OTOMATISASI TRANSFER DATA PENGAMATAN AUTOMATIC WEATHER STATION (AWS) SERTA PEMANFAATANNYA DALAM SATELLITE DISASTER EARLY WARNING SYSTEM (SADEWA). S1 thesis, Universitas Pendidikan Indonesia.

[img]
Preview
Text
S_FIS_1100385_Title.pdf

Download (240kB) | Preview
[img]
Preview
Text
S_FIS_1100385_Abstract.pdf

Download (212kB) | Preview
[img]
Preview
Text
S_FIS_1100385_Table_of_content.pdf

Download (394kB) | Preview
[img]
Preview
Text
S_FIS_1100385_Chapter1.pdf

Download (146kB) | Preview
[img] Text
S_FIS_1100385_Chapter2.pdf
Restricted to Staf Perpustakaan

Download (631kB)
[img]
Preview
Text
S_FIS_1100385_Chapter3.pdf

Download (334kB) | Preview
[img] Text
S_FIS_1100385_Chapter4.pdf
Restricted to Staf Perpustakaan

Download (868kB)
[img]
Preview
Text
S_FIS_1100385_Chapter5.pdf

Download (134kB) | Preview
[img]
Preview
Text
S_FIS_1100385_Bibliography.pdf

Download (327kB) | Preview
[img] Text
S_FIS_1100385_Appendix.pdf
Restricted to Staf Perpustakaan

Download (1MB)
Official URL: http://repository.upi.edu

Abstract

Informasi cuaca sangatlah bermanfaat bagi manusia karena informasi tersebut dapat digunakan untuk berbagai kepentingan kegiatan masyarakat saat ini. Informasi cuaca dapat diperoleh melalui hasil pengukuran dari salah satu alat meteorologi yaitu Automatic Weather Station (AWS). Agar data hasil pengukuran AWS dapat digunakan oleh masyarakat maka data tersebut perlu dipublikasikan, salah satunya dengan cara menampilkannya di web Satellite Disaster Early Warning System (SADEWA). Untuk melakukan hal tersebut maka diperlukan transfer data otomatis dari AWS ke SADEWA. SADEWA itu sendiri memiliki informasi cuaca yang bersifat prediksi. Informasi prediksi SADEWA didapatkan melalui simulasi model Weather Research and Forecasting (WRF). Untuk melihat sejauh mana kinerja WRF dalam memprediksi cuaca, maka dapat dilakukan validasi terhadap data pengamatan AWS yang telah ditransfer otomatis ke SADEWA. Validasi model dilakukan dengan cara menghitung koefisien korelasi dimana semakin tinggi nilai koefisien korelasinya maka hasil simulasi model semakin valid. Teori koefisien korelasi Pearson digunakan untuk melakukan validasi model. Dari hasil penelitian didapatkan nilai koefisien korelasi data SADEWA terhadap data AWS untuk suhu adalah 0,8198 dan untuk kelembaban adalah 0,7074 yang artinya bahwa data prediksi SADEWA dan data pengukuran AWS memiliki korelasi yang kuat. Sedangkan untuk curah hujan nilai koefisien korelasinya adalah 0,2522. Nilai yang rendah ini dikarenakan curah hujan merupakan parameter tersulit untuk diprediksi oleh model. Kata Kunci : Automatic Weather Station (AWS), Informasi Cuaca, Satellite Disaster Early Warning System (SADEWA), Transfer otomatis, Validasi hasil prediksi. Weather information is very useful for people because the information can be used for various purposes of their activities. Weather information can be obtained through the measurement of meteorological tool, that is Automatic Weather Station (AWS). In order that people can use the data from the result of measurement, so the data must be published by displaying it on the Satellite Disaster Early Warning System (SADEWA) web. It is needed to transfer data automatically from AWS to SADEWA for displaying the data on the web. Sadewa itself has weather information prediction. Information of SADEWA is obtained by the simulation of Weather Research and Forecasting (WRF) model. To know the performance of WRF in predicting the weather, it must be compared by observation data to validate the model. Validation of model can be obtained by calculating the correlation coefficient. Theory of Pearson correlation coefficient is used to validate the model. From the results, the correlation coefficient for temperature is 0,8198 and for relative humidity is 0,7074. It means that data of SADEWA prediction and data of AWS observation have s a strong correlation. As for precipitation correlation coefficient value is 0,2522. A low value is due to the rainfall is the most difficult parameter to be predicted by the model. Keywords : Automatic Weather Station (AWS), Weather information, Satellite Disaster Early Warning System (SADEWA), Automatic transfer, Validation of prediction’s result.

Item Type: Skripsi,Tesis,Disertasi (S1)
Additional Information: No. Panggil : S FIS PUR o-2015; Pembimbing : I. Suaydhi, II. Waslaluddin
Uncontrolled Keywords: Automatic Weather Station (AWS), Informasi Cuaca, Satellite Disaster Early Warning System (SADEWA), Transfer otomatis, Validasi hasil prediksi.
Subjects: L Education > L Education (General)
Q Science > QC Physics
Divisions: Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Jurusan Pendidikan Fisika > Program Studi Pendidikan Fisika
Depositing User: Mrs. Neni Sumarni
Date Deposited: 19 Feb 2016 02:35
Last Modified: 19 Feb 2016 02:35
URI: http://repository.upi.edu/id/eprint/19301

Actions (login required)

View Item View Item