In this article, we try to design the architecture of a smart notification system using an Android gadget for academic notification in college. Academic notification in colleges now utilizes bulletin boards and online media such as websites or social media. The problem faced is the high cost and resources required to deliver the academic notification. Another problem is whether the information delivered can be right to the students who need it. We proposed the architecture of a smart notification system that can reduce the cost, and the information delivered can be right on target to the students in need.
The exchange rate is determined by the demand and supply relationship of the currency. If the demand for a currency increases, while the supply remains or even decreases, then the exchange rate will rise vice versa. The ups and downs of exchange rates on the money market indicate the magnitude of the volatility that occurs in the currency of a State against the currencies of other countries. The volatility phenomenon indicates difficulty in analyzing the exchange rate. Increasing volatility indicates an even greater movement of currency exchange rates even if currency exchange rates experience extreme volatility resulting in economic instability both from the micro and macro sides. The high volatility seen from the pattern of price movements that occur in financial markets, and the impact that can be generated from the high volatility data is the error that will have a variance that is not constant. That is, a relatively high data variability at a time indicates the presence of heteroscedasticity. Heteroscedasticity can lead to errors in drawing a conclusion to the estimated model obtained. Therefore, we need a model that is able to solve the problem that is Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model in order to get more accurate estimation model to estimate exchange rate. From the simulation result, all data contain the volatility seen from the result of heteroscedasticity test, and obtained estimation model for all data.
Forecasting is the process of making a statement about an event where the event has not been known or observed. For pharmaceutical students, learning about forecasting techniques can help determine the treatment of various diseases, one of which is dengue fever. Dengue fever is an acute disease caused by the dengue virus. Dengue fever is still a public health issue in major cities in Indonesia, one of which is Palembang. Based on the profile done by Palembang City’s Public Health Office in 2017, dengue fever cases in the area from year to year tend to fluctuates. To get the overview of the number of dengue fever cases in the upcoming years, time series forecasting methods are used, namely the Exponential Smoothing method and the Autoregressive Integrated Moving Average (ARIMA) method. Afterward, the results of predictions from the two methods are compared. Forecasting using the ARIMA method gives the smallest MSE and MAE results of 108077.877 and 172.424, respectively, compared to the Exponential Smoothing method. This means that the ARIMA method is better at predicting the number of dengue fever cases in Palembang in the coming years.
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