Coronavirus atau COVID-19 menjadi pandemi yang sedang terjadi dalam waktu dekat ini. Pandemi COVID-19 telah meningkatkan stres, mengganggu rutinitas, kondisi keuangan yang tertekan dan kurangnya interaksi sosial, sehingga menciptakan epidemi masalah tidur di masa pandemi COVID-19 atau yang dikenal sebagai coronasomnia. Dampak yang dihasilkan dapat berupa penurunan produktivitas, peningkatan resiko hipertensi, depresi, dan dampak kesehatan lain. Selain itu, coronasomnia dapat meningkatkan stress karena kurangnya aktivitas dan interaksi sosial, sehingga penelitian ini dilakukan untuk mengetahui seberapa besar potensi masyarakat Kota Semarang mengalami coronasomnia. Penelitian ini menggunakan analisis statistik deskriptif untuk melakukan ringkasan yang secara kuantitatif menggambarkan atau meringkas sampel. Statistik deskriptif digunakan untuk menggambarkan data frekuensi (Azri et al. 2016). Berdasarkan hasil dan pembahasan, ditemukan bahwa tidak ada responden yang tidak mengalami insomnia selama pandemi COVID-19, di mana sebagian besar mengalami insomnia sedang, sedangkan sisanya mengalami insomnia ringan dan insomnia berat sehingga dapat disimpulkan bahwa masyarakat Kota Semarang sangat berpotensi besar mengalami coronasomnia.

Coronavirus is a disease caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Coronavirus spreads so fast which is caused depression, stress, and anxiety that triggers coronasomnia. Coronasomnia is a sleep problems because of COVID-19 pandemic. This research aims to determine factors that influence potential coronasomnia for people in Semarang City. The response variables that used in this case are two or more ordinal scale categories, so the analytical method is ordinal logistic regression. Based on the results and discussion, the equation obtained is Logit g2 (X) = 1,502 – 0,902X1(1) + 0,456X5 – 0,436X8 + 0,717X9 and Logit g3 (X) = 5,169 – 0,902X1(1) + 0,456X5 – 0,436X8 + 0,717X9. It can be concluded that there are four variables that affect potential coronasomnia for people in Semarang City, i.e gender, feelings of depression, worries about the future, and anxiety.

Economic success will provide benefits for improving people’s welfare. An important indicator to determine economic success can be seen through inflation by calculating the Consumer Price Index (CPI). CPI is a time series data that is influenced by elements between locations. The GeneralizedSpace-Time Autoregressive (GSTAR) method is a suitable method to be applied to CPI data because it involves elements of time and location (spatiotemporal). The problem is that the GSTAR model cannot detect any correlated residuals. The GSTAR model was developed into the GSTAR-SUR model to estimate parameters with correlated residuals so produce more efficient estimates. The purpose of this study was to determine the best GSTAR-SUR model to predict the CPI of six cities in Central Java, namely Cilacap, Purwokerto, Kudus, Surakarta, Semarang, and Tegal. The data that used is secondary data sourced from BPS Central Java Province. Based on the results of the analysis, the best model formed is the GSTAR-SUR (11)-I(1) model with an RMSE value of 6.213. Forecasting results show that the CPI value for the next 6 months will increase every month for each city