E jurnal Matematika

126-132

Authors: I Made Dwi Udayana Putra, G. K. Gandhiadi, Luh Putu Ida Harini

Weather information has an important role in human life in various fields, such as agriculture, marine, and aviation. The accurate weather forecasts are needed in order to improve the performance of various fields. In this study, use artificial neural network method with backpropagation learning algorithm to create a model of weather forecasting in the area of South Bali. The aim of this study is to determine the effect of the number of neurons in the hidden layer and to determine the level of accuracy of the method of artificial neural network with backpropagation learning algorithm in weather forecast models. Weather forecast models in this study use input of the factors that influence the weather, namely air temperature, dew point, wind speed, visibility, and barometric pressure.The results of testing the network with a different number of neurons in the hidden layer of artificial neural network method with backpropagation learning algorithms show that the increase in the number of neurons in the hidden layer is not directly proportional to the value of the accuracy of the weather forecasts, the increase in the number of neurons in the hidden layer does not necessarily increase or decrease value accuracy of weather forecasts we obtain the best accuracy rate of 51.6129% on a network model with three neurons in the hidden layer.

139-147

Authors: I Ketut Putra Adnyana, I Wayan Sumarjaya, I Komang Gde Sukarsa

The aim of this research is to model and forecast the number of tourist arrivals to Bali using transfer function model based on exchange rate USD to IDR from January 2009 to December 2015. Transfer function model is a multivariate time series model which can be used to identify the effect of the exchange rate to the number of tourist arrivals to Bali. The first stage in transfer function modeling is identification of ARIMA model in exchange rate USD to IDR variable. The best ARIMA model is chosen based on the smallest Akaike information criterion (AIC). The next stage are as follows identification of transfer function model, estimation of transfer function model, and diagnostic checking for transfer function model. The estimated transfer function model suggests that the number of tourist arrivals to Bali is affected by the exchange rate of the previous eight months. The mean absolute percentage error (MAPE) is equal of the forecasting model to 9,62%.

156-163

Authors: I Gusti Ayu Mita Ermia Sari, Komang Dharmawan, Tjokorda Bagus Oka

Binomial tree is a method that can be used to determine price option contracts. In this method, the stock price movement is presented in the form of a tree with each branch representing the probability of the stock price to move up or move down. The purpose of this paper was to determine the price of the options contracts with the American type on Binomial Tree method and compare the three methods that is variance matching, proportional u∙d=1, and risk neutral of determining the value of price option contracts used in Binomial Tree method with Black-Schole method. The result of this research was the value of the options contract using the variance matching more similar with the value of the Black-Scholes contract.

170-175

Authors: Wirya Sedana, Komang Dharmawan, Ni Made Asih

It has been discussed in many literatures that commodity prices tend to follow mean reversion model. This means that when there is a jump price in certain time, the price will revert to the mean price in the future. In this research, the method to determine the existence of mean-reversion of soybean price dynamics is discussed. Then, the future contract of soybeans is calculated using mean-reversion simulation and the spot-future parity theorem. Both methods are applied to the closing price of soybeans for the period of 19 September 2011 to 28 April 2016. The results show that the future contract price calculated by Model Mean-Reversion simulation under estimate the future contract price determined by the spot-future parity theorem.

183-193

Authors: Ni Putu Mirah Sri Wahyuni, I Wayan Sumarjaya, I Gusti Ayu Made Srinadi

The purpose of this research is the model of forecasting rainfall using spectral analysis method. To obtain complete information on characteristics of time series data we need to examine periodicity of the data. Examining the periodicity of time series data in the frequency domain is called spectral analysis. The results of spectral analysis show that periodogram is clearly dominated by a very large peak at frequency ω_10=0,083. This frequency corresponds to period of 12 cycle per month. Based on the results of analysis of time series data rainfall is SARIMA (0,1,1)(0,1,1)12 where the model can be written as X_t^*=X_(t-1)^*+X_(t-12)^*-X_(t-13)^*+a_t^*-0,9045a_(t-1)^*-0,8199a_(t-12)^*+0,7416a_(t-13)^*. The result indicates minimum rainfall happen in January and maximum rainfall happen in August.