Time Series Modeling of Disease Occurrence Patterns with SARIMA (Seasonal AutoRegressive Integrated Moving Average) - Vatresia, Arie; Utama, Ferzha (Window of Health : Jurnal Kesehatan, 2023)
Disease is a human health problem. In overcoming existing health problems, predictive analysis is needed to help overcome them early and plan to prevent and control these diseases. This study aims to determine the prediction of disease time patterns in health data modeling at Argamakmur Hospital. By knowing existing disease patterns, information can be provided based on time series patterns. The prediction of this time series pattern uses time series analysis with a seasonal pattern, which takes all possible data for existing patterns to predict and analyze time series to obtain a predictive model. This study uses time series analysis to model seasonal autoregressive integrated moving averages. The results obtained are predictions for the next six months from the best model obtained, namely: data Typhoid Fever disease ARIMA (1.1,1) increased by 3.08%, data Gastroenteritis disease ARIMA(1,0,1) increased 0.51%, data Dyspepsia data ARIMA (0,1,2) increased by 0.55%, data Acute Anemia disease ARIMA(1,0,2) decreased by 0.4%, data bronchopneumonia disease ARIMA(1,0,1) decreased by 0.58%, data for acute diarrhea disease ARIMA(1,0,1) increased 0.2%, data for vertigo ARIMA(1,0,2) decreased 0.64%, data for stroke ARIMA(1,1,1) decreased 0.28%, data tumor ARIMA(1,0,1) decreased 1%, data on asthma ARIMA(1,0,1) decreased 0.21%, data DM disease ARIMA(1,0,1) decreased by 0.47%, and data Pulmonary TB disease ARIMA(1,0,1) decreased by 0.14%. Based on these results, it is suggested that the hospital be advised to increase awareness of Typhoid Fever, gastroenteritis, and Dyspepsia.