Optimization of Fuzzy System Inference Model on Mini Batch Gradient Descent

Optimization is one of the factors in machine learning to help model training during backpropagation. This is conducted by adjusting the weights to minimize the loss function and to overcome dimensional problems. Also, the gradient descent method is a simple approach in the backpropagation model to solve minimum problems. The mini-batch gradient descent (MBGD) is one of the methods proven to be powerful for large-scale learning. The addition of several approaches to the MBGD such as AB, BN, and UR can accelerate the convergence process, hence, the algorithm becomes faster and more effective. This added method will perform an optimization process on the results of the data rule that has been processed as its objective function. The processing results showed the MBGD-ABBN-UR method has a more stable computational time in the three data sets than the other methods. For the model evaluation, this research used RMSE, MAE, and MAP

A Mathematical Model of Microplastic Spreading into Fish Digestive Based On Abiotic Factor

In this research, we observe the fish from seven different river location on Yogyakarta by evaluating its digestive weight. We investigate the microplastics spreading on fish digestive based on the abiotic factor such as river temperature, acidity, and river flow microplastics granules to be carried into the digestive tract of the fish. The rate of microplastics in the fish body can be describe mathematically using differential equation. We build a model based on the diagram flow of the relationship between each variables. Thus we have a differential system as the model. In the next step we analyze the model analytically. To show the accurancy of the model, we make a simulation using data simulation to the system and we compare it with the computing results using observation data. At the end of our research, we give a justification for the most influential abiotic factor for microplastic sreading