Prediction of the Number of Motor Vehicle Inspections using the Long Short-Term Memory (LSTM) Method as a Decision-Support Tool at the Transportation Agency of Langkat Regency

Nisa Khairun; Novriyenni Novriyenni; Kristina Annatasia  Br Sitepu

doi:10.59934/jaiea.v5i3.2549

Authors

Nisa Khairun STMIK KAPUTAMA
Novriyenni STMIK Kaputama
Kristina Annatasia Br Sitepu STMIK Kaputama

DOI:

https://doi.org/10.59934/jaiea.v5i3.2549

Keywords:

Deep_learning, Long_short_term_memory, Decision_making, Motor_vehicle_testing, Prediction

Abstract

Sustained population growth increases the demand for public services, including motor vehicle inspection or periodic testing (KIR). The Transportation Agency of Langkat Regency faces difficulty in accurately predicting the number of vehicles to be tested in a given period, which causes queue build-ups and inefficiency in resource allocation. This study aims to design and implement a prediction model for the number of motor vehicle inspections using the Long Short-Term Memory (LSTM) method as a decision-support tool. The data used are monthly historical data for the period from January 2023 to December 2025, comprising 36 data points. The data were normalized using Min-Max Scaling, formed into sequential samples with a timestep of three, and then divided into 80% training data and 20% testing data. The model was evaluated using the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) metrics. The evaluation obtained an MAE of 3, an MSE of 9, and an RMSE of 3 vehicles per month, indicating a high level of accuracy. The model projects a total of 916 vehicles in 2026 and 986 vehicles in 2027, with the testing peak occurring in July. These results can be used as a basis for resource planning and for improving the quality of public services.

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