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: 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: Synthesis of Apatite - Chitosan Composite Using Duck Eggshell and Chitosan with Temperature and pH Control
Abstrak: Composite of apatite-chitosan was prepared by using chitosan as polymer matrix. Calcium phosphate was prepared from CaO and (NH4)2HPO4, which the ratio Ca : P had to be 1.67. Chitosan is produced by deacetilase from biopolymer chitin. Duck eggshell (95% CaCO3) as starting material was converted to be calcium oxide (CaO) through calcinations with temperature 1000oC for 5 hours. In-situ and ex-situ methods were prepared from apatite and chitosan (dissolved with acetate acid 2%). Calcium phosphate prepared for bone implantation, but it is too fragile and brittle to be implant into the body. So addition of chitosan can decrease crystalinity and increase mechanic characteristic (tougher). Composite apatite-chitosan was resulted by fast precipitation. Fast precipitation was held in a breaker glass with nitrogen atmosphere, under controlled temperature at 37oC and pH 7, in order to increase amount of hydroxyapatite. Precipitation with CaO which was dropped by (NH4)2HPO4. Hydroxyapatite and chitosan were appearing at samples with in-situ and ex-situ methods which indicating by peak matching of XRD (closed lattice parameter, crystal size), overlapping at FTIR spectra and supporting by SEM picture (homogenous and agglomerated particle). However, it still needs further research to examine biocompatibility osteoconductive degree of sample by in-vivo and in-vitro test.
Keyword: