ESSA: Exponential Smoothing and Spatial Autocorrelation, Methods for Prediction of Outbreaks Pest in Indonesia

Geographically, Indonesia is one of the countries in Asia are at risk of disease pests of rice brown planthoppers or BPH (Nilaparvata lugen Stal.) through a cycle of long-distance migration along the years following the tropical Monsoon flow. BPH attacked this country since 1930 until now. It damaged ten thousand hectares of rice and made crop failure. As anticipated in the future surveillance system is needed that is able to predict the dynamics of migration of BPH early so that the concentration of BPH endemicity. The focus of the research is to develop a procedure Exponential Smoothing and Spatial Autocorrelation (ESSA), which includes a mechanism prediction using Exponential Smoothing method and mechanism analysis of BPH spatial patterns attack using Spatial Autocorrelation method. The research was conducted through four stages which include: (1) the identification and determination of areas experiencing high attack BPH in the study area, (2) grouping of BPH data attacks that followed local cropping patterns, (3) the prediction and analysis of spatial connectivity using the Local Indicator Spatial Association (LISA), and (4) visualization and interpretation of analytical results. Prototype was built using the programming language R (http://r-cran.project). The result of the research could be developed as geographical information system (GIS) tool to predict the migration pattern of BPH which has been done by observation center for Pest Plant Diseases Laboratory Observations Region V Surakarta, Central Java, Indonesia, Ministry of Agriculture Republic of Indonesia for many times.