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: Implementation of Random Forest  Regression and Gradient Boosting Regression on the Number of Hotspot Data in  Kalimantan
Abstrak: Hotspot merupakan suatu indikator yang dapat digunakan sebagai pendeteksi awal terjadinya kebakaran hutan dan lahan (karhutla). Karhutla merupakan bencana alam yang rutin terjadi di Indonesia salah satunya di pulau Kalimantan. Tujuan penelitian ini yaitu mengonstruksi model regresi machine learning dengan metode Random Forest (RF) dan Gradient Boosting (GB) menggunakan data jumlah hotspot di beberapa titik wilayah Kalimantan dengan melakukan tuning pada hyperparameter model yakni n-estimators (banyaknya pohon yang dibangun), max-depth (maksimum kedalaman di tiap pohon), fungsi criterion (mengukur kualitas pemisahan suatu atribut) untuk RF, dan fungsi kerugian (metrik yang mengukur selisih nilai prediksi dengan data aktual selama proses training) untuk GB. Tuning pada metode random forest menggunakan fungsi criterion poisson menghasilkan nilai n-estimators sebesar 13 dan max-depth 28. Sementara itu, tuning pada metode gradient boosting dengan fungsi kerugian huber menghasilkan nilai n-estimators sebesar 53 dan max-depth 6. Perbedaan jumlah hotspot dari hasil keluaran model dengan data aktual diukur dengan nilai measurement Root Mean Square Error (RMSE). Metode random forest menghasilkan nilai RMSE sebesar 691.18, sedangkan gradient boosting menghasilkan RMSE sebesar 719.91., Hotspot is an indicator that can be used as early detector of forest and land fires (karhutla). Karhutla is natural disaster that occur regularly in Indonesia, especially in Kalimantan. This study aims to construct regression models using machine learning algorithms with random forest (RF) and gradient boosting (GB) methods by tuning the hyperparameters of model namely n-estimators (number of the tree to be built), max-depth (maximum depth in each tree), criterion function (measure the quality of separation of an attribute) for RF, and loss function (metric that measures the difference between the predicted and the actual value during the training process) for GB, by applying the number of hotspot data at several points in Kalimantan. Tuning with RF using poisson criterion resulted in n-estimators value of 13 and max-depth of 28. Meanwhile, tuning with GB using huber loss function resulted in n-estimators value of 53 and max-depth of 6. The differences of the number of hotspots between the output of the model and the actual data are measured using the Root Mean Square Error (RMSE). The RMSE value of the RF method is 691.18, while the RMSE value of GB method is 719.91.
Keyword: forest and land fires, GB, hotspot, hyperparameter tuning, RF

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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