Modeling MATERIAL AND METHODS

223

2.7 Testing

Tests conducted thirty-eight times by using the 100 epoch and varied learning rate. Data testing was done by using Euclidean distance measurement methods.

2.8 Analyzing

This model analyze by calculating the accuracy from the classification of precipitation class. This, shows how appropriated data being classified with the actual class. Calculation accuracy is done by creating a contingency matrix. The Accuracy is calculated by dividing the correct total sample on the diagonal contingency table with the total data equation 5. Accuracy = ∑ diagonal table ∑ x 100 5

3. RESULTS AND DISCUSSIONS

3.1 Correlation

Correlation between average precipitation MJJA with predictor’s IOD, SOI, and SST using correlation analysis. Table 1 shows the correlation analysis using 5 significance level. Table 1 Correlation values r MJJA with IOD, SOI and SST MJJA Month IOD SOI Nino12 Nino3 Nino4 Nino34 average precipitation MJJA May 0.056 0.012 0.110 -0.315 0.061 -0.167 Jun 0.041 -0.414 0.113 -0.225 -0.180 -0.244 Jul -0.126 -0.221 0.235 -0.038 0.130 -0.141 Aug 0.075 0.117 -0.077 0.196 0.021 0.017 Sep 0.137 -0.019 -0.130 0.102 0.038 -0.026 Oct 0.174 -0.126 0.007 0.183 -0.082 0.052 Nov -0.122 0.113 0.168 0.151 -0.077 0.034 Dec -0.196 -0.125 0.146 0.108 -0.134 0.024 Jan 0.399 -0.478 0.414 0.387 0.326 0.305 Feb 0.366 -0.380 0.201 0.500 0.360 0.415 According to Pearson correlation rule table for the amount of 38 years data 8, Pearson value is 0.312. Hence, predictor’s for classification consists of correlation values between r ≥ 0.312 and r ≤ -0.312. This study will use six scenarios to obtain the best model which has high accuracy. On the first scenario we use all of variables. Next Scenario we uses Pearson value rule. On the third scenario we choose the highest correlation each variable, and the others we use principal component analysis method. The complete scenario’s shown at Table 2. Table 2 Scenario Scenario Predictor 1 IOD, SOI, Nino3 and Nino4 2 IOD-Jan, IOD-Feb, SOI-Jun, SOI-Jan, SOI-Feb, Nino12-Jan, Nino3-May, Nino3- Jan, Nino3-Feb, Nino4-Jan, Nino4-Feb and Nino34-Feb 3 SOI-Jun, SOI-Jan, Nino12-Jan, Nino3-Feb, Nino34-Feb 4 PC1, PC2 and PC3 5 PC1, PC2, PC3, PC4 and PC5 6 PC1, PC2, PC3, PC4, PC5, PC6 and PC7

3.2 Modeling LVQ

LVQ method produced accuracy for each scenario with small differences to classify precipitation on dry season, shown at Figure 2. The first, third, fourth and sixth scenarios produce the level of accuracy 71.05 with learning rate of 0.002, 0.004 and 0.005 are highest than second 224 and fifth scenarios. The first scenarios we modeled by using all of the global climate indices index data. The third scenarios we choose the highest correlation each variable. The other scenarios are fourth and sixth scenarios using principal component analysis with Eigen value 75 and 95. Figure 2 Accuracy of LVQ

4. CONCLUSION

Classification of precipitation on dry season which divided into three classes can be implemented by using LVQ method. The accuracy of this classification is 71.05.For the future work, we suggest to add the longer precipitation data and choose the best variety learning rate.

5. ACKNOWLEDGMENT

Thank you to Center for Climate Risk and Opportunity Management in Southeast Asia and Pacific CCROM-SEAP, BAU, for the supported data.

6. REFERENCES

[1] Yamamoto Y, Furuya J, Suzuki K, Ochi S. 2002. Estimation of rainfall distribution and its relation to rice production in Laos. http:www.gisdevelopment.netapplicationagricultureproductionma06_69b.htm [2] Naylor RL, Falcon W, Wada N, Rochberg D. 2002.Using El-Niño Southern Oscillation Climate Data to Improve Food Policy Planning in Indonesia, Bulletin of Indonesian Economic Studies, 38, 75-91. [3] Ashok K, Guan Z, Yamagata T. 2001: Impact of the Indian Ocean Dipole on the Relationship between the Indian Monsoon Rainfall and ENSO. Geophys Res. Lett., 28, 4499–4502. [4] Naylor RL, Battisti DS, Vimont DJ, Falcon WP, Burke MB. 2007. Assessing the risks of climate variability and climate change for Indonesian rice agriculture, Proc. Nat. Acad. Sci., 104, 7752- 7757. [5] DArrigo R, Wilson R. 2008. El Nino and Indian Ocean Influences on Indonesian Drought: Implications for Forecasting Rainfall and Crop Productivity. International Journal of Climatology. 28: 611–616. [6] Estiningtyas W. 2012. Pengembangan Model Asuransi Indeks Iklim Untuk Meningkatkan Ketahanan Petani dalam Menghadapi Perubahan Iklim [disertasi]. BogorID:Institut Pertanian Bogor. 10 20 30 40 50 60 70 80 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 Persentase  Accuray of LVQ Skenario 1 Skenario 2 Skenario 3 Skenario 4 Skenario 5 Skenario 6