SEASONAL SPACE-TIME AUTOREGRESSIVE INTEGRATED MOVING AVERAGE UNTUK PREDIKSI PENYAKIT MENULAR BERBASIS SPASIAL TEMPORAL STUDI KASUS: INFEKSI SALURAN PERNAFASAN AKUT KOTA BANDUNG - Iqdam Musayyad Rabbani, - (2020)
Penyakit infeksi saluran pernafasan akut (ISPA) merupakan penyakit menular paling berbahaya di dunia. Penelitian melaporkan penyakit ISPA lebih sering terjadi pada musim dingin dan peralihan. Penelitian lain melaporkan kejadian ISPA memiliki korelasi spasial. Menurut Pfeifer dan Deutsh (1980), korelasi spasial dapat dipertimbangkan ketika membangun model dan melakukan prediksi sehingga didapatkan hasil yang lebih baik. Maka dari itu, penelitian ini bertujuan untuk membangun model Seasonal Space-Time Autoregressive Integrated Moving Average (STARIMA) untuk mengetahui korelasi spasial kejadian ISPA di Kota Bandung dan memprediksi jumlah penderita ISPA di masa yang akan datang. Langkah pemodelan Seasonal STARIMA yaitu melakukan sentralisasi dan transposisi data matriks, kemudian membuat matriks bobot spasial sebanyak empat ordo berdasarkan struktur neighborhood wilayah keseluruhan, menguji stasioneritas data, mengidentifikasi ordo model, mengestimasi parameter, lalu mendiagnosis model. Setelah itu, performa model Seasonal STARIMA dibandingkan dengan performa model SARIMA. Terakhir, dilakukan prediksi jumlah penderita ISPA di Kota Bandung untuk tahun 2020. Ditemukan bahwa kecamatan pada lag spasial ke-0 dan ke-2 memiliki korelasi spasial positif, sementara kecamatan pada lag spasial ke-1 dan ke-3 memiliki korelasi spasial negatif. Performa training model Seasonal STARIMA lebih baik daripada model SARIMA di semua kecamatan, sementara performa testing¬-nya lebih baik di enam kecamatan. Data yang digunakan adalah data jumlah penderita ISPA pada tahun 2010-2019 di delapan kecamatan di Kota Bandung. Acute respiratory infection (ARI) is the most dangerous infectious disease in the world. ARI disease is more common in winter and transition. The incidence of ARI in one site has a correlation with the other, one of the factors that influence it is the distance. Therefore, it can be concluded that the incidence of ARI has a spatial correlation. According to Pfeifer and Deutsh (1980), spatial correlation can be considered when building models and making predictions so that better results are obtained. Therefore, this study aims to determine the spatial correlation of ARI incidence in eight districts in Bandung and to build a Seasonal Space-Time Autoregressive Integrated Moving Average (STARIMA) model and make predictions for the future. The steps are centralizing and transposing the data matrix, creating a spatial weight matrix of four orders based on the overall neighborhood structure, testing data stationarity, identifying model orders, estimating parameters, and then diagnosing the model. After that, the performance of the Seasonal STARIMA model is compared to the SARIMA model. Finally, a prediction of the number of ARI patients in Bandung is made for 2020. It shows that the districts in spatial lag 0 and 2 had positive spatial correlation, while districts in spatial lag 1 and 3 had negative spatial correlation. Furthermore, the results show that the performance of Seasonal STARIMA training phase is better than the SARIMA in all districts, while the testing phase is better in six districts. The data used is the number of ARI sufferers in 2010-2019 in eight districts in Bandung.