Judul: Performance Comparison of Best Subset-Bayesian Model Averaging and Regularization Method in Predicting the Number of Hotspots in Kalimantan
Abstrak: Luas wilayah hutan di Kalimantan terus berkurang akibat terjadinya kebakaran hutan dan lahan (karhutla). Penelitian-penelitian sebelumnya menunjukkan bahwa pencegahan karhutla di Kalimantan dapat dilakukan dengan memprediksi jumlah hotspot menggunakan machine learning berdasarkan indikator iklim. Penelitian ini bertujuan untuk membangun model regresi regularisasi dan Bayesian Model Averaging (BMA) berbasis model regresi polinomial berdasarkan hasil best subset selection. Model terbaik pada penelitian ini dipilih berdasarkan metrik evaluasi RMSE dan R^2. Selain itu, pengaruh normalisasi dan standardisasi juga diukur terhadap model yang dibangun. Hasilnya diperoleh kombinasi enam variabel prediktor terbaik dan diperoleh model terbaik yaitu BMA yang merupakan gabungan dari enam model regresi polinomial. Selain itu, penerapan normalisasi dan standardisasi data mempengaruhi nilai parameter seluruh model, tetapi hanya sedikit mempengaruhi hasil pengujian model. Dengan demikian, model BMA dengan data asli lebih dipilih karena nilai koefisiennya lebih mendekati nol dengan performa yang tidak jauh berbeda., The area of forests in Kalimantan continues decreasing due to forest and land fires. Previous studies have shown that the prevention of forest and land fires in Kalimantan can be implemented by predicting the number of hotspots using machine learning based on climate indicators. This study aims to build regularized regression models and Bayesian Model Averaging (BMA) based on polynomial regression models based on best subset selection. The best model in this study is assessed by using the RMSE and R^2 evaluation metrics. In addition, the effect of normalization and standardization is also evaluated on the constructed models. The results shows that a combination of the six best predictor variables is the best and it also shows that the best model is BMA which is a combination of the six polynomial regression models. In addition, the application of data normalization and standardization affects the parameter values of all models, but slightly affects the model testing results. Therefore, the BMA model with original data is more preferable because its coefficients are much closer to zero with similar performance.
Keyword: Machine learning, hotspot, Bayesian Model Averaging, Regularisasi, Regresi Ridge, Regresi LASSO, Regresi Elastic Net

Judul: Predicting the Number of Hotspot in Kalimantan Using Gaussian Process Regression Based on Climate Indicators.
Abstrak: Hotspot merupakan suatu indikator kebakaran hutan yang sangat dipengaruhi oleh indikator iklim lokal dan global. Kebakaran hutan merupakan salah satu bencana alam yang cukup rutin terjadi di Indonesia terutama di pulau Kalimantan. Penelitian ini mengembangkan model dengan low hyperparameter complexity untuk memprediksi jumlah hotspot berdasarkan indikator iklim. Tujuan penelitian ini yaitu mengonstruksi model regresi proses Gaussian menggunakan data jumlah hotspot di Kalimantan dengan fungsi kernel ARD eksponensial kuadrat dan melakukan tuning pada hyperparameter yakni nilai varian dari noise pada fungsi kernel. Tuning hyperparameter dilakukan dengan tiga metode optimisasi yaitu Bayesian optimization, grid search, dan random search. Hasilnya, model regresi proses Gaussian terbaik diperoleh menggunakan metode Bayesian optimization dan random search dengan nilai varian dari noise masing – masing sebesar 1401.3 dan 1128.2. Metode prediksi regresi proses Gaussian yang telah dioptimisasi menggunakan dua metode tersebut menghasilkan nilai metrik akurasi pada data testing yaitu nilai RMSE, MAE, dan R-squared masing-masing sebesar 844.47, 354.29, 54.52% dan 846.58, 350.93, 54,29%., An hotspot is an indicator of forest fires heavily influenced by the local and global climate indicators. Forest fires are a natural disaster that regularly occurs in Indonesia, especially on the island of Kalimantan. This study develops a model with low hyperparameter complexity to predict the number of hotspots based on climate indicators. The purpose of this research is to construct a model using Gaussian Process Regression (GPR) with data on hotspot numbers in Kalimantan employing the ARD squared exponential kernel functions and tuning hyperparameters such as the variance value of noises in the kernel function. The hyperparameter tuning was performed using three optimizations. The methods are the Bayesian optimization, the grid search, and the random search. The results shows that the best GPR model was obtained using the Bayesian optimization and the random search, with sigma values of 1401.3 and 1128.2, respectively. The optimized GPR prediction methods using these two methods produce in accuracy metrics of RMSE, MAE, and Rsquared values of 844.47, 354.29, 54.52% and 846.58, 350.93, 54.29% respectively.
Keyword: hotspot, kernel, climate, Gaussian process regression, hyperparamater tuning

Judul: Characterizations of low alloy steel sae 4422  plated with chromic acid (Cro3)
Abstrak: Characterizations of low alloy steel SAE 4422 plated with chromic acid (CrOrO3) was use Optical Microscopy (OM), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), RD),Energy Dispersive Analysis of X-Ray (EDAX), Hardness Test and Roughness Test. Microstructures analysis exhibits cracks and grain boundaries microcracks as a characteristics of chrome metal found at plating metal. Perlite and Ferrite as grains of steel found at base metal with grain boundaries around it. Phase analysis of plating metal appropriate to chrome metal with lattice constant (a) is 2.559 Å and phase analysis of base metal appropriate to iron ferrite with lattice constant (a) is 3.318 Å. Both plating and base metal have cubic system. Chemical analysis of plating metal at surface section exhibits that cracks area have highest value of carbon element compare with grains and grains boundaries area. While chemical analysis of base metal at cross section exhibits that perlite have highest value of carbon element compare with ferrite and grain boundaries. The hardness value of plating metal at surface section and cross section are 753 and 637 VHN. While, the hardness value of base metal and interface area at cross section are 205 and 373 VHN. The roughness value at measurement points of chrome surface for as received sample are 0.445 μm. Observation used Optical Microscopy and Scanning Electron Microscopy exhibits surface irregularities combine to form surface texture of plating metal at surface section.
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