Klasifikasi Beban menggunakan Feed - Forward Neural Network pada Gedung Bertingkat - Armanto, Ony; Aulia, Masyitah; Bahrul, Yasya (AI Society & STMIK Indonesia, 2025)
Electricity consumption continues to increase year by year, leading to inefficiencies in energy management. This issue has become a major concern in modern power systems, particularly in energy monitoring systems based on Smart Grid technology. As the use of technology becomes more accessible, energy loads also grow significantly. Therefore, the ability to identify the types of electrical loads used in an installation is crucial, necessitating the implementation of load classification systems. To support the performance of electrical load classification, a Feed-Forward Neural Network (FFNN) is utilized. The results of this study show that the classification model achieved an accuracy of 99.03% with an error rate of 6.43%, and the RSME 0.098, indicating excellent classification performance