Judul: The Construction of the Artificial Neural Networks for Modelling the Number of Hotspots Based on the Climate Indicators, The Construction of the Artificial Neural Networks for Modelling the Number of Hotspots Based on the Climate Indicators
Abstrak: Hotspot merupakan salah satu indikator terjadinya kebakaran hutan yang dipengaruhi oleh indikator iklim lokal dan global di suatu wilayah. Beberapa penelitian sebelumnya menunjukkan keterkaitan antara iklim dengan hotspot sehingga dimungkinkan untuk melakukan pemodelan jumlah hotspot berdasarkan indikator iklim. Penelitian ini mengonstruksi Artificial Neural Network (ANN) untuk memodelkan jumlah hotspot di Kalimantan. Proses konstruksi dilakukan dengan menguji kombinasi hyperparameter model ANN dan menganalisis performanya. Melalui model dengan performa terbaik, tiap indikator iklim diukur tingkat pengaruhnya terhadap jumlah hotspot menggunakan metode permutation feature importance. Hasilnya, model ANN terbaik diperoleh dengan kombinasi satu hidden layer, lima node pada hidden layer, serta dilatih dengan metode pelatihan Nadam, laju pembelajaran 0,01, dan loss function mean absolute error. Indikator iklim hari tanpa hujan yang diturunkan dari curah hujan memberikan efek paling berpengaruh terhadap jumlah hotspot karena indikator tersebut merupakan fitur paling penting bagi model terbaik.
Keyword: artificial neural network, hotspot, iklim, tingkat kepentingan fitur

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: 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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