Septiana, Lauda (2014) Statistically Inspired Modification Of Partial Least Square Untuk Memprediksi Suhu Dan Kelembaban Dengan Pra-Pemrosesan Principal Component Analysis. Undergraduate thesis, Institut Teknologi Sepuluh Nopember Surabaya.
Preview |
Text
1310100079-Undergraduate_Thesis.pdf - Published Version Download (1MB) | Preview |
Abstract
Informasi tentang prakiraan cuaca yang cepat dan tepat
menjadi hal yang penting dalam aktifitas manusia. BMKG
masih menggunakan metode subjektif dalam melakukan
prakiraan cuaca jangka pendek. Salah satu informasi cuaca
jangka pendek yang diumumkan oleh BMKG yaitu suhu maksimum
(TMAKS), suhu minimum (TMIN), dan kelembaban ratarata
(RH). Sejak tahun 2004, BMKG memanfaatkan data
Numerical Weather Prediction (NWP) sebagai upaya
meramalkan cuaca secara objektif, namun data NWP masih
menghasilkan ramalan yang bias. Oleh karena itu, perlu
dilakukan pemrosesan (post processing) menggunakan metode
Model Output Statistics (MOS). MOS merupakan model yang
menghubungkan an-tara variabel respon (hasil observasi
cuaca) dengan variabel prediktor (parameter NWP) berbasis
regresi. Observasi cuaca yang digunakan sebagai variabel
respon adalah TMAX, TMIN, dan RH, sedangkan parameter NWP
yang digunakan sebanyak 18 variabel. Sebelumnya dilakukan
reduksi dimensi dalam sembilan grid pengukuran untuk
masing-masing variabel NWP menggunakan Principal
Component Analysis (PCA). Metode regresi yang digunakan
adalah Statistically Inspired Modification of Partial Least
Square (SIMPLS). Sebagian besar komponen utama yang
terbentuk dari setiap variabel NWP adalah sebanyak satu
komponen. Hasil penelitian menyim-pulkan bahwa validasi
model SIMPLS dengan kriteria Root Mean Square Error
Prediction (RMSEP) menunjukkan bahwa RMSEP untuk TMAKS
di empat stasiun berkriteria sedang. Nilai RMSEP untuk TMIN
di tiga stasiun berkriteria baik dan nilai RMSEP untuk RH
berkriteria baik di dua stasiun pengamatan. Nilai Percentage
Improval (%IM) untuk prediksi TMIN berkisar antara 20,20 -
89,75 persen, yang artinya model SIMPLS dapat mengkoreksi
bias NWP antara 20,20 - 89,75 persen
==================================================================================================
Information about weather forecasts quickly and accurate
to be important in human activities. BMKG was using
subjective methods in short-term weather forecast. One of the
short-term weather information announced by BMKG is
temperature maximum (TMAKS), temperature minimum (TMIN),
and humidity (RH). Since 2004, BMKG use Numerical Weather
Prediction (NWP) to predict the weather objectively.
Therefore, it is necessary to processing (post processing) using
Model Output Statistics (MOS). MOS is model that connects
between response variable (weather observation) and predictor
variable (NWP parameters) based regression. Weather
observation were used as the response variabel is TMAX, TMIN,
and RH, while NWP parameters are used as many as 18
variables. Previously conducted reduction dimension in nine
grid for each variable using Principal Component Analysis
(PCA). Regression method used is Statistically Inspired
Modification of Partial Least Square (SIMPLS). Most of the
principal components that formed of each variable NWP there
is one principal component. The regression method used is
Statistically Inspired Modification of Partial Least Square
(SIMPLS). The result of the study concluded that validation
model SIMPLS with criteria Root Mean Square Error
Prediction (RMSEP) indicates that RMSEP for TMAKS in four
stations have precisely criterion. RMSEP values for TMIN in
three stations have good criterion and the value of RMSEP for
RH have good criterion in two stations. The value of
Percentage Improval (%IM) to predict TMIN between 20,20%
until 89,75%, which means that model SIMPLS can correct
bias NWP between 20,20% until 89,75%
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSSt 519.535 4 Sep s |
| Uncontrolled Keywords: | MOS, NWP, Principal Component Analysis, SIMPLS, Suhu dan Kelembaban |
| Subjects: | Q Science > QA Mathematics > QA275 Theory of errors. Least squares. Including statistical inference. Error analysis (Mathematics) Q Science > QA Mathematics > QA278.5 Principal components analysis. Factor analysis. Correspondence analysis (Statistics) |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | EKO BUDI RAHARJO |
| Date Deposited: | 16 Oct 2020 06:06 |
| Last Modified: | 16 Oct 2020 06:37 |
| URI: | http://repository.its.ac.id/id/eprint/82157 |
Actions (login required)
 |
View Item |