Judul: Predicting the Number of Hotspots in Kalimantan Using Long Short Term Memory Model Based on Climate Indicators
Abstrak: Forest and land fires have become a concerning phenomenon in Indonesia. As mitigation, hotspots are used as an early stage to identify fires. In an effort to predict the number of hotspots, deep learning models such as Long Short-Term Memory (LSTM) can be used. The purpose of this research is to build the best LSTM model architecture to predict the number of hotspots in Kalimantan based on climate indicators. The model is trained using the glorot uniform method as a weight initiation method, Nadam optimizer with a learning rate of 0.001, and early stopping as model regularization on training data with a proportion of 80%. The results show that the best LSTM model in this research is a model with 2 LSTM layers and 4 dense layers trained to minimize the Root Mean Squared Error (RMSE) as a loss function. The model uses a window of size 12 and a hidden layer with 64 nodes. On testing data, the model is able to predict the number of hotspots with an explained variance score of 83.4%., Kebakaran hutan dan lahan sudah menjadi fenomena yang memprihatinkan di Indonesia. Sebagai mitigasi, titik panas (hotspot) digunakan sebagai tahap awal untuk mengidentifikasi kebakaran. Dalam upaya memprediksi jumlah hotspot, model deep learning seperti Long Short-Term Memory (LSTM) dapat digunakan. Tujuan penelitian ini adalah membangun arsitektur model LSTM terbaik untuk memprediksi jumlah hotspot di Kalimantan berdasarkan indikator iklim. Model dilatih menggunakan metode glorot uniform sebagai metode inisiasi bobot, optimizer Nadam dengan learning rate 0,001, dan early stopping sebagai regularisasi model pada data training dengan proporsi sebesar 80%. Hasil penelitian menunjukkan bahwa model LSTM yang terbaik pada penelitian ini adalah model dengan 2 layer LSTM dan 4 dense layer yang dilatih untuk meminimumkan Root Mean Squared Error (RMSE) sebagai loss function. Model tersebut menggunakan window berukuran 12 dan hidden layer dengan 64 node. Pada data testing, model mampu memprediksi jumlah hotspot dengan explained variance score sebesar 83,4%.
Keyword: hotspot, layer, LSTM, Weight, dense

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: 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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