PENERAPAN JARINGAN SYARAF TIRUAN UNTUK PREDIKSI JUMLAH PENUMPANG KERETA API MENGGUNAKAN ALGORITMA BACKPROPAGATION

Abstract

The number of rail passengers on the Kisaran–Medan route varies over time due to a number of variables, including local economic situations, national holidays, peak travel seasons, and current transportation regulations. The administration of Kisaran Station finds it challenging to manage train fleets, organize travel timetables, and enhance service quality due to this unpredictability. Using historical data from 2019 to 2025, this study intends to apply an Artificial Neural Network (ANN) with the Backpropagation technique to forecast the number of train passengers at Kisaran Station. With a learning rate of 0.25 and a sigmoid binary activation function, the system was constructed using a 7-5-1 network architecture with seven input neurons (yearly data from 2019–2025), five hidden layer neurons, and one output neuron. The data was split into 70% training data (January–July) and 30% testing data (August–December) after being adjusted to the interval 0.1–0.9 using the Min-Max technique. At epoch 822, the training process reached a convergence. For most months, including March (predicted: 647,271; actual: 642,870 passengers) and April (predicted: 999,728; actual: 996,320 passengers), the model was able to produce predictions that were close to actual values, according to testing results. However, there were differences in some months with seasonal spikes. It is anticipated that PT Kereta Api Indonesia and the regional government would use the created prediction system as a decision-support tool to plan transportation services and enhance the general quality of railway services.