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ITS » Paper and Presentation » Statistika
Posted by fandikaaqsa@its.ac.id at 13/04/2016 11:01:37  •  891 Views


PREDICTION OF MAXIMUM TEMPERATURE MINIMUM TEMPERATURE AND AVERAGE RELATIVE HUMIDITY IN THE SHORT TERM WITH MULTIVARIATE REGRESSION THROUGH PRE-PROCESSING PRINCIPAL COMPONENT ANALYSIS

PREDIKSI SUHU MAKSIMUM SUHU MINIMUM DAN KELEMBAPAN RATA-RATA RELATIF DALAM JANGKA PENDEK DENGAN MULTIVARIATE REGRESSION MELALUI PRA-PEMROSESAN PRINCIPAL COMPONENT ANALYSIS

Author :
KUSUMAWARDANI, RIZKY ( 1310100036 )




ABSTRAK

Dalam upaya meminimalkan dampak bencana akibat cuacaiklim maka informasi prakiraan suhu dan kelembapan yang cepat dan tepat sangatlah penting mengingat suhu dan kelembapan tidak pernah lepas dari kehidupan manusia. Salah satu lembaga pemerintahan non departemen yang menangani masalah prakiraan suhu dan kelembapan adalah Badan Meteorologi Klimatologi dan Geofisika BMKG. Sebelum tahun 2004 BMKG lebih mengandalkan seorang prakirawan untuk memprakirakan suhu dan kelembapan sehingga hasilnya masih bersifat subyektif. Mulai tahun 2004 BMKG mulai mengembangkan metode baru dengan memanfaatkan luaran Numerical Weather Prediction NWP. NWP akan bias bila digunakan pada daerah yang memiliki topografi dengan vegetasi yang dominan sehingga diperlukan suatu post-proccesing dengan menggunakan Model Output Statistics MOS. MOS merupakan suatu metode berbasis analisis regresi dengan variabel respon observasi unsur cuaca di permukaan dan variabel prediktor adalah unsur cuaca NWP. Penelitian ini menganalisis data suhu maksimum suhu minimum dan kelembapan rata-rata relatif sehingga digunakan metode regresi multivariat untuk post-processingnya. Jumlah variabel prediktor yang digunakan ada sebanyak 18 sebelum dimodelkan variabel ini direduksi terlebih dahulu menggunakan Principal Componen Analysisi PCA berdasarkan grid dan variabel. Komponen utama yang dihasilkan dari proses reduksi dimensi grid sebagian besar ada sebanyak satu untuk setiap variabel sedangkan untuk reduksi variabel sebagian besar ada sebanyak 7 untuk setiap stasiun pengamatan. Hasil yang didapatkan setelah pemodelan dengan regresi multivariat adalah residual yang dihasilkan masih belum identik dan independen namun metode regresi multivariat mampu memperbaiki model NWP sebesar 8922. Jadi dapat dikatakan pemodelan MOS melalui regresi multivariat lebih akurat dibandingkan NWP untuk menduga suhu dan kelembapan hasil observasi.


ABSTRACT

In an effort to minimize the impact of disaster due to the weatherclimate forecasting information temperature and humidity quickly and accurately are important considering temperature and humidity never loses of human life. One of the non departemen government istitutions that handles forecasts of temperature and humidity is the Badan Meteorologi Klimatologi and Geofisika BMKG. Prior to 2004 BMKG only rely on a forcaster to prediction temperature and humidity so the results was subjective. Starting in 2004 BMKG began developing new methods by using Numerical Weather Prediction NWP. NWP will be bias when used in area which have topography with the dominant vegetation so it needed a post-processing using Model Output Statistics MOS. MOS is a method based regression. This research analyzed maximum temperature minimum temperature and average relative humidity so it need multivariate regression as the post-processing. The number of predictor variable that used as many as 18 before modeled this variable are reduced by Principal Compenent Analysis PCA based on grid and variable.The principal component resulting from a reduction process based on grid mostly there were one for each variablewhile reduction process based on variabel resulted mostlt 7 for each location. The result of regression multivariate is the residual still not identic and independent but this method can repairing NWP model of 8922. So be considered that MOS through multivariate regression more accurate than NWP for prediction temperature and humidity of observation results.



KeywordsModel output statistics; numerical weather prediction; PCA; regresi multivariat
 
Subject:  Analisis regresi
Contributor
  1. Dr. Sutikno, S.Si, M.Si
Date Create: 13/04/2016
Type: Text
Format: PDF
Language: Indonesian
Identifier: ITS-paper-13021160008825
Collection ID: 13021160008825
Call Number: RSSt 519.535 4 Kus p


Source
Paper and Presentations of Statistics, RSSt 519.535 4 Kus p, 2014

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Copyright @2016 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library




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  1.  ITS-paper-41099-1310100036-paper.pdf - 2545 KB
  2.  ITS-paper-41099-1310100036-presentation.pdf - 3402 KB




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