221 limited historical climate data on a long period of time, and the need for future climate projections
certain scenarios to study the effects of climate change [7]. This study aims to classify the precipitation during the dry season with Learning Vector
Quantization LVQ. This classification produces intensity low, medium and high precipitation. This research used IOD, SOI and SST data to make the classification. Some study that used LVQ
the application of LVQ method is mostly used for classification such as a classification in training data selection iris and heart disease on the UCI machine learning [8]; classification textual
documents [9], and mass spectrometric classification [10].
2. MATERIAL AND METHODS
2.1 Data Collection
The data used for this study consist of: 1. Ten-day precipitation called as dasarian data in five areas at Indramayu district from 1971
to 2008 38 years. The Dasarian data in 1 year starts from January
, , until to December
, ,
The data sourced from the Climatology Meteorology and Geophysics Agency BMKG supported by the Center for Climate Risk and Opportunity Management in Southeast Asia
and Pacific CCROM-SEAP. 2. SOI and SST
Monthly SOI and SST data from 1970 to 2008 are used. SST consists of Nino12, Nino3, Nino4 and Nino34. SOI and SST sourced from National Oceanic and Atmospheric
Administration NOAA can be downloaded at www.noaa.gov
. 3. Monthly IOD data from 1970 to 2008 are used. IOD data sourced from Japan Agency for
Marine-Earth Science and Technology JAMSTEC can be downloaded at www.jamstec.go.jp
2.2 Preprocessing
In this research, we focused on May to August abbreviated as MJJA. The total intensity of precipitation on May to August dasarian data are Calculated. Calculating the precipitation of
Dasarian in May ,
, to August
, ,
from 1971 to 2008 in five area at Indramayu equation 1. Total precipitation intensity which would be classified, obtained from
average of the five regions in Indramayu equation 2. Normalization IOD, SOI, and SST data from 1970 to 2008 using Z-score Method equation 3: The result of preprocessing phase are
average of MJJA and normalization of IOD, SOI, SST.
Z-score = x
i
-x σ
3 = total of precipitation MJJA every area
= dasarian data = average of MJJA
= total of MJJA = total area n=5
222
= variable Nino12, Nino3, Nino4 and Nino34
̅ = average of variable = standard deviations of variable
2.3
Data Processing
The average precipitation of MJJA is divided into three classes. This class divided by using statistical method from average precipitation of MJJA, shown at Figure 1.
Figure 1: The class division
2.4 Predictor Selection
The average MJJA is a response and the variable predictor’s are IOD, SOI and SST.
Calculate correlation between average precipitation MJJA with SOI, IOD and SST using Pearson correlation method equation 4.
r= ∑ X
i
-XY
i
-Y
n i=1
∑ X
i
-X
n i=1
2
. ∑ Y
i
-Y
n i=1
2
4
2.5 Cross Validation
Leave one out cross validation LOOCV method used in this research. This method selects one of data as testing, and others as training data
2.6 Modeling
Algorithm LVQ method in [11] are used to classify the precipitation class. Step 0 : initialize reference vector, learning rate and alpha
Setp 1 : while stop condition is false, do step 2 to 6
Step 2 : For each training input vector x do step 3 to 4 Step 3 : Find J so that
is a minimum Step 4 : Update
as follows: If T = then
; If T
then Step 5 : Reduce learning rate update learning rate
Step 6 : Test stop condition
l Cass 1
Class 2 Class 3
