Sistem Prediksi Ketersediaan Stok Vaksin dengan Metode Monte Carlo di Dinas Kesehatan Kabupaten Malaka Berbasis Website

The management of vaccine stock availability is an important factor in supporting the success of immunization programs. However, at the Malaka Regency Health Office, vaccine stock recording and planning are still conducted in a semi-manual manner, which can lead to inaccurate planning, including vaccine shortages or excess stock. This condition may disrupt immunization services and increase the risk of budget inefficiency due to vaccine expiration. This study aims to develop a website-based Vaccine Stock Availability Prediction System using the Monte Carlo method to predict vaccine requirements based on historical data. The data used consist of vaccine stock data from 2018 to 2024, with data from 2018–2020 used as training data and data from 2021–2024 used as testing data to evaluate the performance and accuracy of the prediction model. The Monte Carlo method is applied through random-based simulations to model uncertainty in vaccine demand. The system is developed using the Waterfall model, with Laravel as the framework and MySQL as the database. The results show that the system can assist the Malaka Regency Health Office in planning vaccine procurement and distribution more accurately, efficiently, and in a data-driven manner, thereby minimizing the risk of stock imbalance.