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.
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0.009
Persentase
Accuray of LVQ
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