COMPARISON OF RANDOM FOREST AND NAÏVE BAYES METHODS FOR CLASSIFYING AND FORECASTING SOIL TEXTURE IN THE AREA AROUND DAS KALIKONTO, EAST JAVA

Soil texture is used to determine airflow, heat, instability, water holding capacity, and the shape and structure of the soil structure. Soil texture as an important attribute that determines the direction of soil management must be modeled accurately. However, soil texture is a soil attribute that is quite difficult to model. It is a compositional data set that describes the particle size of the soil mineral fraction (sand, silt, and clay). The methods used to classification and predict soil texture with machine learning algorithms are Random Forest (RF) and Naïve Bayes (NB). The purpose of this study was to classify the distribution of soil texture using the Random Forest and Naïve Bayes methods to obtain the most accurate grouping results. This research was conducted in the area around Kalikonto River Basin, East Java Province. The performance-based tests show that the RF algorithm provides higher accuracy in predicting soil texture based on the Digital Elevation Model (DEM). The results of RF’s performance testing on training data and testing data gave an accuracy value of 92.55% and 87.5%. Classification using the Naïve Bayes method produces an accuracy value of 89.98% on testing data and 80.65% accuracy on training data

Analysis of Optimal Policy Option for Sustainable Palm Oil Plantation Development

West Kotawaringin Regency, Central Kalimantan Province, Indonesia as a development center of palm oil plantations requires policies that support sustainability. Thus, the aim of this research is to determine the optimal policy option that supports business management of sustainable palm oil plantations. This research uses Mixed Multiplier analysis that derived from Social Accounting Matrix (SAM) model which is combined with Analytic Hierarchy Process (AHP). This result indicates that optimal option for sustainable palm oil plantation management is to prioritize the environmental management program, and social responsibility that is supported by fund allocation of 1% tax addition on palm oil plantation sub-sector. Keywords: mixed multiplier, social accounting matrix, analytic hierarchy process, policy optio

CLUSTER FAST DOUBLE BOOTSTRAP APPROACH WITH RANDOM EFFECT SPATIAL MODELING

Panel data is a combination of cross-sectional and time series data. Spatial panel analysis is an analysis to obtain information based on observations affected by the space or location effects. The effect of location effects on spatial analysis is presented in the form of weighting. The use of panel data in spatial regression provides a number of advantages, however, the spatial dependence test and parameter estimators generated in the spatial regression of data panel will be inaccurate when applied to areas with a small number of spatial units. One method to overcome the problem of small spatial unit size is the bootstrap method. This study used the fast double bootstrap (FDB) method by modeling the poverty rate in the Flores islands. The data used in the study was sourced from the BPS NTT Province website. The results of Hausman test show that the right model is Random effect. The spatial dependence test concludes that there is a spatial dependence and the poverty modeling in the Flores islands tends to use the SAR model. SAR random effect model R2 shows the value of 77.38 percent and it does not meet the assumption of normality. Spatial Autoregressive Random effect model with the Fast Double Bootstrap approach is able to explain the diversity of poverty rate in the Flores Island by 99.83 percent and fulfilling the assumption of residual normality. The results of the analysis using the FDB approach on the spatial panel show better results than the common spatial panel

Spatial quantile autoregressive model : case study of income inequality in Indonesia

Substantial economic development in Indonesia has dramatically increased inequality in the last decade. This issue will hinder the country’s long-term economic development as well as creating socioeconomic instability and violence. This study analysed the effects of macroeconomic factors such as gross regional domestic product, investment, unemployment rate, and labour-force participation, on Indonesian provinces’ inequality. Since the economic development in Indonesia is mostly concentrated on Java Island, a spatial based analysis was appropriate. In addition, we also considered a method that enabled a specific level of inequality modelling, since previous studies used a mean-based analysis. Therefore, we proposed a spatial quantile autoregressive (SQAR) technique. The results showed that the Gini index of Indonesian provinces had a significant positive spatial autocorrelation (SA). Regions with similar Gini index values tended to cluster together. In addition, local analysis of the SA showed Java Island as a region that was characterized by high inequality, while Sumatra and Kalimantan Island were not. By contrast, the SQAR model suggested that there were various effects of macroeconomic factors on inequality at different levels of quantile. As a consequence, distinct approaches to handling inequality should be taken for provinces with low, medium, and high Gini index values

Spatial quantile autoregressive model: case study of income inequality in Indonesia

Substantial economic development in Indonesia has dramatically increased inequality in the last decade. This issue will hinder the country’s long-term economic development as well as creating socioeconomic instability and violence. This study analysed the effects of macroeconomic factors such as gross regional domestic product, investment, unemployment rate, and labour-force participation, on Indonesian provinces’ inequality. Since the economic development in Indonesia is mostly concentrated on Java Island, a spatial based analysis was appropriate. In addition, we also considered a method that enabled a specific level of inequality modelling, since previous studies used a mean-based analysis. Therefore, we proposed a spatial quantile autoregressive (SQAR) technique. The results showed that the Gini index of Indonesian provinces had a significant positive spatial autocorrelation (SA). Regions with similar Gini index values tended to cluster together. In addition, local analysis of the SA showed Java Island as a region that was characterized by high inequality, while Sumatra and Kalimantan Island were not. By contrast, the SQAR model suggested that there were various effects of macroeconomic factors on inequality at different levels of quantile. As a consequence, distinct approaches to handling inequality should be taken for provinces with low, medium, and high Gini index values

DETERMINATION OF ENVIRONMENTAL FACTORS AFFECTING DENGUE INCIDENCE IN SLEMAN DISTRICT, YOGYAKARTA, INDONESIA

Background: Dengue is a disease related to the environment that spreads rapidly. Prevention movement is considered ineffective; therefore, a more efficient early warning system is required. It is required strongly correlated variables to as predictor in early warning system. This study aims to identify the environmental conditions associated with dengue. Materials and methods: This ecological study was conducted on five sub-districts selected based on the trend of the incidence. Data land cover and elevation obtained using GIS. Climate data were obtained from Meteorology and Climatology and Geophysics Agency of Yogyakarta. Results: There were 1.150 dengue cases from 2008-2013 obtained from District Health Office. The spatial pattern is clustered in all sub-districts (Z-score 0.05). There is no effect of climate parameters in sporadic dengue areas (p > 0.05). Conclusion: It is concluded that dengue in Sleman is clustered and associated with the environment parameter, even though it does not have close correlation. High elevated and small building area is consistent with the lower dengue cases. Humidity and rainfall affect dengue, but temperature does not affect dengue