PRINCIPAL COMPONENT ANALYSIS PADA KARAKTERISTIK DATA CURAH HUJAN RENTANG PANJANG INDONESIA; PRINCIPAL COMPONENT ANALYSIS ON CHARACTERISTIC OF INDONESIA LONG RANGE RAINFALL DATA

TITUS CHRISTIAWAN, Fajar Adi Kusumo

2015 | Skripsi | FMIPA

Meteorology is the interdisciplinary science that learns concerning the weather activity in Earth’s atmosphere, and single among the purposes of learning this is for weather forecast purposes. This thesis discusses the use of Principal Component Analysis (PCA)’s method to determine the highest rainfall of Indonesia Precipitation data based on temporal and spatial observation. This method enables to reduce the magnitude of the observed dimensions of the data without losing significant information illustrating the entire data. PCA is also called Empirical Orthogonal Function (EOF) or Karhunen-Loeve Transformation or Singular Value Decomposition (SVD) on matrix. PCA’s method is used to discover dominant structures from a set of data. This method has been applied really a lot in several realms of science, such as in biology and law. In the meteorology, this method is used to analyze the precipitation data. This thesis learns concerning theoritical analysis serving as a base for the entire steps in PCA method, that is using linear algebra’s concepts combined with several basic statistical theories. Otherwise, this thesis provides the interpretation from each step in PCA method. The data required for analysis using PCA method is Indonesia monthly precipitation data for 110 years that arrives from Global Precipitation Climatology Center and monthly precipitation data in the Regency of Gunungkidul, D. I. Yogyakarta Province, for 11 years that arrives from Central of Badan Meteorologi, Klimatologi, dan Geofisika (Meteorology, Climatology, and Geophysics Council).

Kata Kunci : Empirical Orthogonal Function; Principal Component Analysis, Singular Value Decomposition; rainfall.