SISTEM NAVIGASI ADAPTIF ROBOT PEMBERSIH PERPUSTAKAANMENGGUNAKAN MLP DENGAN INFERENSI FUZZY MAMDANI DAN BEST FIRSTSEARCH (Kohesi: Jurnal Sains dan Teknologi, 2025)
The advancement of robotics technology has enabled automation in varioustasks, including cleaning in public facilities such as libraries. However, thecomplex library environment with numerous fixed obstacles and dynamicuser activities demands an adaptive navigation system. This studyproposes an adaptive navigation system for library cleaning robots basedon the integration of the Best First Search (BFS) algorithm, fuzzyinference, and Multilayer Perceptron (MLP). BFS is used to determine theoptimal path based on priorities, while MLP predicts floor dirt levels basedon coordinates and floor types. The MLP predictions are then converted into linguistic priority categories (high, medium, low) using fuzzy logic.Simulations were conducted on a 5×5 grid map representing a library areawith varying start and goal points. The results demonstrate that the systemcan avoid obstacles, select paths based on dirt levels, and allocate cleaningtime efficiently. The integration of these three methods significantlyenhances the adaptability and efficiency of robot navigation in dynamicenvironments. This research contributes significantly to the developmentof intelligent robotic systems for automated cleaning in public spaces.