PERBANDINGAN METODE ARIMA DAN EXPONENTIAL SMOOTHING TERHADAP PREDIKSI TINGKAT IMUNISASI CAMPAK PADA BAYI (Studi Kasus: DKI Jakarta) - Ananta Khayana Putra (2023)
Measles is one of the diseases that can be prevented by early measles immunization. Individuals who contract measles tend to exhibit initial symptoms such as cough with phlegm, runny nose, high fever, and red eyes. While the initial symptoms may not appear severe, the disease should still be taken seriously as it can lead to complications such as bronchitis, pneumonia, ear inflammation, and brain infection. Therefore, immunization is necessary as a preventive measure against measles. Early immunization reduces the risk of contracting measles and prevents complications. To assess the future immunization rates for measles in infants, this study utilizes the advancing technology to make predictions. In forecasting the level of measles immunization in infants, two methods are employed: the ARIMA method and Holt Winter's Triple Exponential Smoothing. The research findings indicate that the ARIMA model achieves an average RMSE value of 27.65 and an average MAPE value of 27.19, indicating an accuracy level of 72.81%. On the other hand, the Holt Winter's Triple Exponential Smoothing model obtains an average RMSE value of 21.92 and an average MAPE value of 17.56, indicating an accuracy of 82.44%. In terms of computational time, the Holt Winter's Triple Exponential Smoothing model only requires 7.89 seconds for computation, from model design to prediction results, compared to ARIMA, which takes 2 minutes and 59 seconds. It can be concluded that the frequency of measles immunization in infants will increase over time until the initial target is achieved.