Judul: A Study of Generalized Mixed-Effects Trees Model for Classification of Household Poverty in West Java
Abstrak: Combining machine learning and statistical modeling approaches allows for handling of both random and fixed effects. This thesis looks at how to represent fixed and random effects using a statistical machine-learning technique. Generalized mixed-effects trees (GMET) is a mixed-effects model based on trees that is used to deal with response variables from the exponential family of distributions. The GMET model is a generalization of generalized linear mixed models (GLMM) where in the fixed effect part the GMET model does not require to be linear. In performing classifications involving fixed effects and random effects, both models can be used. The GMET and GLMM technique were applied in this research to identify suitable data circumstances for using this methodology, using both simulation and actual/empirical data. Different generations of response variables (mixed-effects tree structure, linear fixed effect function, and non-linear function fixed effects) were used to generate the simulation data. Additionally, different values of the variance of the random effect (small and large) and fixed effect coefficients (small and large) were used. The GMET and GLMM algorithms are also used to analyze actual/empirical data in order to identify the variables or factors that can distinguish between households with low and high levels of poverty (non-poor and poor), as well as the impact of regional typology on the classification of household poverty status in West Java Province in 2019. Based on a simulation study, analysis of variance was carried out for each performance evaluation value of the predicted value of mean absolute deviation (PMAD) and predictive classification error rate (PMCR). In the analysis of variance for the PMAD value, it was found that there is an interaction effect of the four factors (scenarios) that has p-value less than 0.05 (significant). It means that there is a relationship of dependence on a certain method, a certain generation, certain fixed effects, and certain random effects that cannot be separated. Therefore, the GMET and GLMM algorithms have their respective performance depending on the generation of a response variable, a fixed effect, and a certain random effect. In the analysis of variance for PMCR values, it was found that interaction effects of three factors is significant at alpha 5%, namely generation scenarios, fixed effect scenarios, and random effect scenarios. It means that the performance of the GMET and GLMM models could not be distinguished. This is in line with classifying household poverty status in actual/empirical study, where there is no visible difference in performance between the two models. Performance examination of the GMET and GLMM models in identifying household poverty status shows that all performance evaluations (PMCR, AUC, sensitivity, specificity, precision, recall, F1-score, accuracy, and MCC) have absolute values from the difference between the two models which tend to be small. This is consistent with simulated study on the evaluation of PMCR values, which reveal that the performance of the GMET and GLMM models cannot be separated. Thus, in this classification of household poverty status, the GMET model delivers performance that is similar to the GLMM model. The results show that the GMET performs comparably for different response variable generation
Keyword: fixed effects, generalized linear mixed-model, household per capita expenditure, random effects, supervised learning

Judul: Modified local getis statistic on AMOEBA weights matrix for spatial panel model and its performance
Abstrak: Classical regression model assumes that no interaction between objects. However, in some cases these conditions are not hold so we have to find an alternative solution that can accommodate the interaction effect. Spatial model is a model that can accommodate the interaction effect by adding a spatial component in the right hand side that represented by a spatial weights matrix. Generally, spatial weighted matrix uses closeness concept among the units, without include the proximity of interest variables. Another way to construct a spatial weights matrix is to use AMOEBA procedure introduced by Aldstadt and Getis. In this procedure, each element of the matrix, beside is determined by the closeness relationship, also determined by proximity among variables using local Getis statistics. Getis and Ord have claimed that the statistic is normally distributed. However, empirically, normal curve test shows that normality of local Getis statistic is influenced by variable of interest (original variable). Due to this reason, we propose a modification of local Getis statistic, namely Gnew, which gives a robust result. A modification of local Getis statistics by transforming to ( ) gives a robust result, that is, it has normal distribution for any distribution of Xj. To assess local Getis and modified local Getis statistics, they are applied on AMOEBA procedure to create weights matrix, namely WG and WGnew. We compare them with contiguity matrix (WC). The model that used for comparison is spatial dynamic panel model with respect to Cizek. By using simulated data and root mean square error relative criteria, it is found that WGnew gives the best performance. In the next stage, we evaluate spatial matrix performance in model to real data, whereas in this case, we take poverty issue in Center Java Province. Poverty is one of the issues of concern at both the central and local government. Central Java was the province with the second largest number of poor people in Indonesia. There are many factors that influence poverty, some of them are GDP per capita, population, education, share of agriculture labor, share of industry labor, share of trading labor and share of services labor (four dominant sectors). Focus of this research is to study influence of labor of junior high school graduates, population, GDP per capita and the share of labor in four dominant sectors to the number of poor people using spatial panel model. Sources of data for modeling obtained from central BPS and Center Java BPS from 2008 to 2012. The research results show that an increase in population, labor of junior high school graduates and share of agricultural labor can increase the number of poor people. Meanwhile the increase in labor share of industry, trade and service sectors and GDP per capita can decrease the number of poor people. Based on these findings, there are several ways to reduce the number of poor people, such as to control population by family planning program (KB program), improving quality of human resources, expand some business, especially in sectors of industry, trading and services.
Keyword: poverty, PDB per capita, random permutation, local Getis statistic, modified local Getis statistic

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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