Classification is a job of assessing data objects to include them in a particular class from a number of available classes. The classification method used is the k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) methods. The data used in this study is data on poverty in Papua with the category of the number of low/high level poor people. Of the 29 regencies/cities that were sampled, 15 regencies/cities represent the number of low-level poor people and 14 districts/cities are the number of high-level poor people. The results of the analysis obtained are the k-Nearest Neighbor (k-NN) method with a value of k=15 producing an accuracy of 58.62%, while the Support Vector Machine (SVM) method with Parameter cost = 1 using the RBF kernel produces an accuracy value. by 93.1%. The classification criteria to find the best method is to look at the Root Mean Square Error (RMSE) which states that the Support Vector Machine (SVM) method is better than the k-Nearest Neighbor (k-NN) method.
Structural equation modeling (SEM) consists of two parts, namely measurement model, which measures a relation between indicators with latent variables and structural model that analyzes the relationships between the latent variables. In General, the researches about SEM assume that relation among latent variables are linear. If the relation between latent variables is nonlinear than the function has to be estimated. Two exogenous and an endogenous latent variables models can be written as follows (,) = + 1 2 1 2 ξ ξ ω ω ω and (,) = 1 2 κ κ κ. The estimator of the function in the nonlinear structural model is influenced by location and some knot points 1 κ and 2 κ. 7440 Ruliana et al.
Spatial regression is a development of classical linear regression which is based on the influence of place or location. To determine the location/spatial effect, a spatial dependency test was performed using the Moran Index, and the Lagrange Multiplier (LM) test was used to determine a significant spatial regression model. In this study, spatial regression was applied to the case of food security in each district in South Sulawesi Province. The results of the analysis show that there is a negative spatial autocorrelation, meaning that the spatial effect does not affect the level of food security. The significant spatial regression model is the SEM (Spatial Error Model) model. The equation of the SEM model produces variables that have a significant effect, namely the ratio of normative consumption per capita to net availability, percentage of population living below the poverty line, percentage of households with a proportion of expenditure on food more than 65 percent of total expenditure, percentage of households without access to electricity, percentage of households without access to clean water, life expectancy at birth, ratio of population per health worker to the level of population density, the average length of schooling for women above 15 years, and the percentage of children under five with height below standard (stunting). Thus, the resulting distribution pattern is a uniform data pattern. This means that each adjacent district tends to have different characteristics.
The use of pirated software in Indonesia is quite high compared to other countries in the world. One of the efforts made to reduce the level of software piracy is to develop publicly licensed software such as R software which is open source software. The preparation of this package uses the R software and other additional packages, especially packages for regression analysis. Making this package can make it easier for users to perform regression analysis easily and legally. This package is named SLR App (Simple Linear Regression App) and MLR App (Multiple Linear Regression) which are regression analysis packages that have a user friendly interface. From the tests carried out that this package has similarities from the results of the analysis between the SLR App and MLR App.
This development research has obtained an assessment model of probability literacy in senior secondary school students. Such literacy is crucial because the statistical assessment as part of mathematics today in senior secondary schools tend to emphasize technical procedures with many mathematical formulae. Literacy of probability consists of five basic competencies, namely: (1) understanding the concept of probability, (2) insight into the application of the probability concepts, (3) skills in calculating the value of probability, (4) careful interpretation of the probability value, and (5) skills in visualization and communication of probabilities and conclusion that may be drawn from them. This study maps ability levels in these five competencies across Makassar City, identifying weaknesses in several questions about probability literacy that need to be revised. The emphasis on the concept of literacy is a major strategy to improve learning in secondary schools. These five competencies are mapped according to the type of questions that refer to the guidelines for implementing the skills of the 21st-century Curriculum 2013 in senior secondary school. The level of probability literacy achieved by high school students in Makassar is categorized as medium (47%-72%), highlighting an overall need for improved learning strategies that emphasize the five basic competencies. Additionally, morning classes are seen to correspond to a higher level of achievement than afternoon classes, for all five competencies.
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