Lihat Juga

• Relationship Model Anomaly Harvested Rice with a Weighted Rainfall Index in Buru Maluku Using Bootstrap Aggregating MARS Methods to Predict the Forecast Error Rates Harvested Area and Rice Production
  oleh: F., Kondo Lembang, et al.
  Terbitan: (2015)
• The research analyzed rainfall data from Subang and Karawang as the centers of rice production in West Java. The objectives of this research were to (1) develop monthly rainfall prediction model for predicting the next four months rainfall, (2) develop a next three months rice yield prediction model and (3) estimate the availability of rice in Subang and Karawang as a function of monthly rainfall. Both rainfall and rice yield prediction models were built by ANN technique. ANN rainfall prediction model was applied at six rainfall stations in Subang and Karawang which are Cigadung, Karawang, Rawamerta, Subang, Sindanglaya and Ciseuti. It was developed by including 7-8 variables (X) at input layer and 6-10 nodes at a single hidden layer. Variables at input layer are month code (t) as X1, monthly rainfall values at t, t+1, t+2, and t+3 as X2, X3, X4, and X5 respectively, SOI at t as X6 and SST anomalies at t and t+3 as X7 and X8. Rice yield model was built to estimate the rice production at t+3 by using four variables at input layer which are t, t+1, t +2 and t+3 as X1, X2, X3 and X4 and also included 6-8 nodes at hidden layer. The results of this research found that the ANN model could accurately predict the monthly rainfall in all stations with the R2 values ranged from 64-96%, and maximum errors of each month rainfall ranged from 0.4-3.4 mm/month. Rainfall model predicted that there were trends of Above Normal (AN) rainfall at Karawang and Rawamerta stations in dry season, while at four stations in Subang region would experience Below Normal (BN) rainfall in dry season. Based on 2009 rainfall prediction, the rice yield model predicted highest rice production to happen during February and March 2009 at values of 299.294 ton and 329.082 ton. Key words: artificial neural network, rainfall prediction, rice production
  oleh: Syarifuddin, Magfira, et al.
  Terbitan: (2012)
• ANALISIS PELUANG UNTUK MEMPREDIKSI KEJADIAN IKLIM EKSTRIM "STUDI KASUS : DISTRIBUSI CURAH HUJAN DI KABUPATEN KARAWANG, INDRAMAYU, DAN SUBANG"PROBABILITY ANALYSIS FOR PREDICTING EXTREM CLIMATE, CASE STUDY : RAINFALL DISTRIBUTION IN KABUPATEN ..
  oleh: Sutikno, S.; Jurusan Statistik, Institut Teknologi Sepuluh November, Surabaya, et al.
  Terbitan: (2003)
• The paper describes about rainfall zoning and rainfall prediction modeling and its use for rice availability and vulnerability analysis. The study used rainfall data from Station Baros (Banten region), Station Karawang and Station Kasomalang Subang (Northern Coastal of West-Java), and Station Tarogong (Garut). Fuzzy clustering methods, that was applied for rainfall zoning, used the representative data for El-Nino, La-Nina and normal means condition during 1980-2006 periods. Neural network analysis technique was applied for rainfall prediction modeling. Training set of the model based on the rainfall data of 1990-2002 periods, and validation model based on data of 2003-2006 periods. The model were used to predict the rainfall of 2007-2008 periods. The distibution of equivalence value between rainfall stations was very variative under El-Nino, La-Nina and Normal condition. On the certain of equivalence level it could be derivated some different rainfall zone under El-Nino, La-Nina and normal condition. Model training set could explain 88% of Baros rainfall variability, 89% of Karawang rainfall variability, and 72% of Kasomalang rainfall variability. At Baros, Karawang and Subang, rainfall was predicted to be increased on November 2007-February 2008 period, and to be decreased on the March-June 2008, and to be increased on July-November 2008. The rainfall decreasing on the March-June would carry a losses of rice production up to 25%. But, applying the well irrigation management and suitable growing periods could decrease and mitigate the decreasing of paddy production. Key words: rainfall prediction model, fuzzy clustering, neural network analysis, rice vulnerability
  oleh: Pramudia, Aris, et al.
  Terbitan: (2012)
• PREDICTION OF DAILY RAINFALL CHARACTERISTICS FROM MONTHLY CLIMATE INDICES(PREDIKSI KARAKTERISTIK CURAH HUJAN HARIAN DARI PARAMETER IKLIM BULANAN)
  oleh: Boer, R, et al.
  Terbitan: (2007)