Jaringan Saraf Tiruan untuk Membangun Masa Depan Dunia Kerja di Pematangsiantar

This research examines the application of artificial neural networks (ANN) in predicting the number of workers in Pematangsiantar City as an effort to build a more adaptive and measurable future of the world of work. This research aims to investigate the effectiveness of ANN techniques in processing historical data on the labor force to produce accurate predictions of future employment numbers. The research methodology involves using a dataset that includes key economic variables that affect the labor force, such as unemployment rate, economic growth, and other relevant variables. At the analysis stage, we implemented a ANN with an optimized configuration to model the complex patterns in the data. This research uses historical data related to the labor force, local economy, and other social indicators as model inputs. The results show that this technology can generate reliable predictions to support strategic decision-making, such as job training planning, resource allocation, and workforce policy development. In addition, the application of this technology is expected to help the government and economic actors in dealing with labor market dynamics in the digital era. This research also contributes to the literature on the application of machine learning techniques in the economic domain, by highlighting the potential and limitations of using ANNs to predict complex economic phenomena such as the labor force. Thus, this research contributes to the innovative utilization of smart technology to advance the local employment sector in Indonesia.