Abstrak

This study introduces an innovative method for fabricating glucomannan/PEO nanofibers using rotary force spinning (RFS) and enhances RFS efficiency through response surface methodology (RSM) and machine learning. Various machine learning models were tested, with the artificial neural network (ANN) model showing the highest predictive accuracy. The analysis identified polymer concentration, nozzle diameter, and spinneret angular speed as critical factors influencing fiber diameter. The integration of RSM and ANN provided a comprehensive understanding of glucomannan/PEO nanofiber synthesis. Process optimization achieved nanofibers with a diameter of 253.52 nm, advancing the understanding and optimization of glucomannan/PEO nanofiber synthesis.