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: 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: 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.
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