Implementasi Autoregressive Integrated Moving Average untuk Prediksi Pasien Rumah Sakit Berbasis Dashboard Interaktif

Amelia amel Contesa

doi:10.62866/jurikti.v4i2.304

Amelia amel Contesa * Universitas Lancang Kuning, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.62866/jurikti.v4i2.304

Keywords: ARIMA; Interactive Dashboard; Patient Visit Forecasting; Time Series; Google Apps Script

Abstract

A major challenge in healthcare service management is the uncertainty of patient arrivals during each service period. Fluctuations in patient visits can affect the effectiveness of human resource planning, healthcare facility allocation, pharmaceutical inventory management, and the overall quality of services provided to the community. Therefore, an approach capable of accurately predicting the number of patients is required to support more effective decision-making processes. This study aims to implement the Autoregressive Integrated Moving Average (ARIMA) method to predict hospital patient visits and integrate the forecasting results into a web-based interactive dashboard. The dataset used in this study was obtained from a Kaggle repository containing daily patient visit records from healthcare facilities in India. The daily data were transformed into monthly data through a temporal aggregation process to generate a more stable time series suitable for forecasting modeling. The forecasting model employed was ARIMA (2,1,0), while the system was developed using Google Apps Script as the development platform and Chart.js as the data visualization component. The results indicate that the total number of patient visits during the observation period reached 11,934 patients, with an average of 442 patients per month. The ARIMA model predicted 727 patient visits for the subsequent period, indicating an increasing trend. The main contribution of this study lies in the integration of a forecasting model and an interactive dashboard within a single system capable of presenting statistical information, trend visualizations, and forecasting results in real time to support decision-making in healthcare management. The findings demonstrate that the proposed approach can serve as an effective data-driven tool for hospital operational planning and resource management.

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