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: Performance Comparison of Random Forest and Double Random Forest for Poverty Status Classification at District/City Level
Abstrak: Prediksi berdasarkan sebuah model baik untuk dilakukan karena dapat menghasilkan prediksi yang akurat. Salah satu model yang sudah banyak digunakan dalam memprediksi adalah model klasifikasi Random Forest. Namun, ukuran minimum nodesize pada random forest mungkin kurang optimal untuk menghasilkan kinerja model yang terbaik. Double Random Forest mengatasi hal tersebut dengan membentuk pohon yang lebih besar. Penelitian ini ingin membandingkan kinerja model klasifikasi random forest dan double random forest dalam memprediksi status kemiskinan rumah tangga di Jawa Barat pada tahun 2019. Jumlah penduduk miskin yang bernilai sangat kecil dibandingkan jumlah seluruh penduduk di suatu wilayah menyebabkan adanya ketidakseimbangan pada data. Salah satu langkah penanganan yang dapat dilakukan adalah dengan menggunakan teknik SMOTE. Kinerja metode random forest dan double random forest tidak menunjukkan hasil yang berbeda dalam memprediksi status kemiskinan rumah tangga di Jawa Barat. Hal ini dikarenakan ukuran pohon yang dihasilkan random forest sudah dinilai cukup untuk memberikan kinerja model terbaik sehingga tidak diperlukan adanya perbaikan dengan double random forest. Berdasarkan model-model yang terbentuk, jumlah anggota rumah tangga (ART) merupakan peubah paling penting saat memprediksi rumah tangga miskin di Jawa Barat., Predictive modeling leads to more accurate prediction therefore good to be used. One of model that has been widely used in prediction is Random Forest classification model. However minimum nodesize in random forest is not optimal enough to produce the best model performance, so Double Random Forest overcome this issue by forming bigger trees. This study aims to compare the performance of random forest and double random forest classification models in predicting poverty status of households in West Java at 2019. The number of people living in poverty is very small compared to total population in certain area. This causes an imbalance class in the data. One way that can be done to overcome imbalance class is by using SMOTE technique. Performance of random forest and double random forest methods did not show different result in predicting poverty status of households in West Java. This is due to size of the trees produced by random forest that considered good enough to provide best model, so there is no room for improvement with double random forest. Based on the models, age of the householder showing as the most important variable in classifying household poverty status in West Java.
Keyword: classification, double random forest, poverty, random forest, SMOTE

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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