Aplikasi Penerapan Jaringan Syaraf Tiruan untuk Memprediksi Tingkat Pengangguran di Kota Batam dengan Menggunakan Algoritma Pembelajaran Backpropagation - Dodi Prima Resda; Jhon Hericson Purba; Miranda Miranda; Arista Sitanggang; Maidel Fani; Andy Triwinarko (JURNAL INTEGRASI, 2023)
The imbalance between labor supply and demand often leads to unemployment in a given region. The unemployment rate serves as a key indicator to assess the overall health of the economy. Utilizing Artificial Neural Networks (ANN) as a predictive tool has emerged as a reliable solution to forecast unemployment rates in Batam City, using 7 input parameters. The methodology employed in this predictive model is the Backpropagation algorithm. This involves dividing the dataset into two distinct components: training data, consisting of 4 parts, and the remaining data set aside for testing purposes. This division results in a substantial allocation of 95% for training data and a significant 79% for testing data. The accuracy achieved by this model forms the basis to evaluate its potential success in forecasting unemployment rates for the upcoming year. By harnessing the capabilities of Artificial Neural Networks and employing the Backpropagation methodology, it is possible to predict unemployment rates in Batam City. The outcomes of this analytical approach can serve as a reference to address labor imbalance issues, while also providing a pragmatic tool to enhance economic planning and policy formulation for a more sustainable future.