Development of Work Program Submission System Using Predictive Data Analytics Based on Neural Network Algorithms at PT Pos Lhokseumawe

Hasbul Hadi (Program Studi Magister Teknologi Informasi, Fakultas Teknik, Universitas Malikussaleh)
Nurdin Nurdin (Program Studi Magister Teknologi Informasi, Fakultas Teknik, Universitas Malikussaleh)

Jurnas Nasional Teknologi dan Sistem Informasi
Vol 12 No 2 (2026): Agustus 2026

The digital transformation within PT. Pos Indonesia KC Lhokseumawe demands a more efficient, secure, and adaptive procurement system. This study aims to develop a web-based work program submission system that integrates predictive analytics and neural network algorithms to enhance procurement efficiency and the accuracy of budget forecasting. The system is built using a React.js-based frontend architecture and a FastAPI-based backend, with MongoDB as the database. The N-BEATS model is implemented for time series-based budget forecasting, while Neural Collaborative Filtering is employed to recommend vendors based on interaction history. Evaluation results demonstrate strong performance, with an R-squared value of 0.9965 for the forecasting model and an F1-score of 73.71% for the recommendation model. This integrated system provides procurement management features, budget forecasting, and tender recommendations, and is expected to improve business process efficiency at PT significantly.