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Classification the Amount of Rice Production in Indonesia using the Geographically and Temporally Weighted Regression Method

Aan Kardiana; I Made Sumertajaya; Anik Djuraidah; Anwar Fitrianto; Wardiyono
Indonesia must strive to intensively increase the amount of rice production, both developing and implementing agricultural innovations. The key to success in increasing rice production includes optimization of agricultural resources, application of advanced and specific technologies based on location, support for production and capital facilities, a guarantee of grain prices that provide production incentives, and agricultural extension support and assistance. The strategy for realizing success is by increasing productivity, expanding planting areas, securing production, and empowering agricultural institutions and supporting business finance. Geographically Weighted Regression (GWR) is the development of global regression modeling through the addition of weighting in the form of distances between observation locations to overcome spatial diversity. The regression coefficient on the GWR model is assumed to vary spatially so that for each location point studied the interpretation is different. GWR modeling only accommodates the effect of location without including the influence of observation time. In order to obtain a more accurate parameter estimation, the time element in the GWR model was added to become the Geographically and Temporally Weighted Regression (GTWR) method so that it can handle the diversity of a spatial and temporal data simultaneously. Estimation of regression coefficients is done using the weighted least squares method. The weighting matrix is a combination of spatial and temporal information in identifying spatial and temporal diversity. This method produces models that are local to each location and time and are more representative. This study aims to group the amount of rice production in Indonesia using the GTWR method. The data used are data on the amount of rice production (tons) in 33 provinces of Indonesia from 2001 to 2016 as the response variable, with explanatory variables of harvest area (in ha), rainfall (in mm), and population (in thousand / souls). The results obtained were the GTWR method in modeling the amount of rice production having RMSE = 74455.15, MAE = 39671.47, MAD = 39671.47, and R2 = 99.93. This result is better than OLS method which has RMSE value = 328644.2, MAE = 242155.90, MAD = 216255.79 and R2 = 98.70. The modeling produces 528 models so that it is more representative for each province. Variable harvest area and population are considered local parameters. The grouping of locations studied based on the influential variables produces six groups, namely groups which total production of the rice is: (1) not influenced by harvest area, rainfall, and population, (2) only influenced by harvest area, (3) only influenced by rainfall, (4) only influenced by population, (5) influenced by harvest area and population and (6) influenced by harvest area, rainfall, and total population.
Select Volume / Issues:
Year:
2019
Type of Publication:
Article
Keywords:
Classification; Regression; Weighted; Series; Spatial; Temporal; Production; Rice
Journal:
IJECCE
Volume:
10
Number:
5
Pages:
209-223
Month:
September
ISSN:
2249-071X
Hits: 274

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