Time Series Analysis of Visitor Trends at Pratama Mitra Sehat Clinic, Kabupaten Sukoharjo, Using LSTM
DOI:
https://doi.org/10.26821/IJSHRE.12.5.2024.120505Keywords:
SARIMA, Time series analysisAbstract
This study investigates the application of Long Short-Term Memory (LSTM) neural networks in forecasting visitor traffic at Pratama Mitra Sehat Clinic located in Kab Sukoharjo. The ability to accurately predict visitor influx is critical for healthcare facilities to efficiently manage resources, optimize staffing levels, and enhance patient satisfaction. LSTM models are particularly well-suited for time series forecasting tasks due to their capacity to capture complex temporal dependencies inherent in sequential data. By analyzing historical visitor data spanning various time intervals, LSTM models can identify underlying patterns and trends, enabling them to generate forecasts of future patient arrivals. To evaluate the predictive performance of the LSTM models, commonly used metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) are employed. These metrics provide quantitative measures of the models' accuracy in predicting visitor traffic patterns. The findings of the evaluation reveal promising results, indicating that the LSTM models can effectively capture the dynamic nature of visitor influx at the clinic. However, there remains room for improvement through fine-tuning model parameters and exploring additional features that may enhance predictive accuracy. The implications of accurate visitor traffic forecasting extend beyond operational efficiency to include broader implications for clinic management and patient care. By leveraging predictive insights provided by LSTM models, clinic administrators can make informed decisions regarding resource allocation, staff scheduling, and service planning. Proactive management strategies based on reliable forecasts enable clinics to better meet patient demand, minimize wait times, and improve overall patient experiences. In conclusion, this study demonstrates the potential of LSTM neural networks in forecasting visitor traffic at healthcare facilities.
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