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 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: Case Study of Gallbladder Mucocele and Treatment Used In Small Animals
Abstrak: Gallbladder mucocele (GM) is defined as distention of the gallbladder caused by an inappropriate accumulation or deposition of high concentrations of bile. The purpose of this case study is to study GM conditions in small animals and to report the clinical sign and characteristics of GM through the sonogram interpretation and blood chemistry test evaluation, and studies related to the therapy given and other treatments recommended. A 14-year-old Toy Poodle dog has been diagnosed with severe GM and cholecystitis. No pain signs were shown during the physical examination as asymptomatic is a common characteristic in the early stage of biliary related diseases. In this study Abdominal ultrasonography interpretation and blood chemistry evaluation was conducted in 2021-2022 and analyzed as a secondary data source. Ultrasonography results showed presence of hyperechoic, immobile deposits in gallbladder accompanied by wall thickening and enlarged in size, followed by a significant increase in the enzyme alkaline phosphatase (ALP). This study has summarized mainly the diagnosis and treatments of GM in small animals and suitable alternatives treatments were recommended to encounter the GM conditions in small animals. The effectiveness of combined therapy shows the positive impact on the patient, the condition of GM has been under control.
Keyword: ultrasound, gallbladder, biliary sludge, abdominal ultrasonography