Farmer’s exchange rate (FER) is a proxy indicator to value the farmer’s purchasing rate and shows the term of trade between agricultural products and services sold and the goods purchased by farmers in producing and consuming households. FER obtained by comparing the Farmer Received Price Index with the Farmer Paid Price Index both expressed as percentages. The purpose of this study is to predict the FER of Bali Province from May 2019 to December 2019 and to count the level of purchasing power of the farmers. The monthly data of FER from January 2010 to April 2019 were used to build a seasonal ARIMA (SARIMA). Four models i.e. SARIMA(0, 1, 3), SARIMA(3,1, 0), SARIMA(4,1,0), and SARIMA(1,1, 1) with seasonal factor (0,1, 1)12 were tested. Referring AIC value for SARIMA(0,1,3) as much as 326.94 is the lowest then we inferred this model is the best SARIMA model to predict the FER of Bali Province. Our research concludes the farmer’s income increases more than their expenditures.

The purpose of this study is to predict the demand for catfish that can efficiently and cost effectively through the application of information technology at UD Ulong. The application of information technology referred to is forecasting or forcasting using the Single Moving Average method. Through the application of this method, researchers will maximize the use of the method by taking samples of sales or demand data contained in UD Ulong. Data will be taken sales or demand data for 1 year. Based on these data, researchers will predict demand in the next month. so that the owner of the catfish culture will benefit in predicting the demand for catfish at UD Ulong. Keywords: Forecasting; Single Moving Average Method; Catfish  Abstrak: Tujuan penelitian ini adalah untuk memprediksi permintaan ikan lele yang dapat mengefesiensikan dan mengefektifkan biaya melalui penerapan teknologi informasi pada UD Ulong. Penerapan teknologi informasi yang dimaksud yaitu peramalan atau forecasting menggunakan metode Single Moving Average. Melalui penerapan metode ini, peneliti akan memaksimalkan penggunaan metode dengan mengambil sampel data penjualan atau permintaan yang terdapat pada UD Ulong. Data yang akan diambil adalah data perjualan atau permintaan selama 1 tahun. Berdasarkan data tersebut, peneliti akan memprediksi permintaan di bulan depan sehingga pemilik budidaya ikan lele akan mendapatkan manfaat dalam memprediksi permintaan ikan lele pada UD Ulong. Kata Kunci: Peramalan; Metode Single Moving Average; Ikan Lele

One of the macroeconomic indicators to see the stability of a country’s economy is inflation. This study aims to model the value of monthly inflation in Indonesia from January 2003 to December 2019 using the ARIMA-NN hybrid. The data plot shows a non-linear pattern and trends, so that the differencing process is carried out and the model is built using ARIMA model. The best ARIMA model obtained is SARIMA (1,1,0)(0,1,1)12 with a Root Mean Square Error (RMSE) of 0.01134. Furthermore, ARIMA residuals that do not satisfy white noise and normality are modeled using NN. The best structure obtained of NN model is (3×2×1) with an RMSE of 0.023984. From the ARIMA and residual NN prediction results, the ARIMA-NN hybrid model is obtained to predict the value of monthly inflation in Indonesia for the next 12 months with the Mean Absolut Percentage Error (MAPE) value is 11.40873%. It means that the model result has high prediction accuracy.