Pemodelan Regresi Nonparametrik Menggunakan Pendekatan Polinomial Lokal pada Beban Listrik di Kota Semarang

Semarang is the provincial capital of Central Java, with infrastructure and economic's growth was high. The phenomenon of power outages that occurred in Semarang, certainly disrupted economic development in Semarang. Large electrical energy consumed by industrial-scale consumers and households in the San Francisco area, monitored or recorded automatically and presented into a historical data load power consumption. Therefore, this study modeling the load power consumption at a time when not influenced by the use of electrical load (t-1)-th. Modeling using nonparametric regression approach with Local polynomial. In this study, the kernel used is a Gaussian kernel. In local polynomial modeling, determined optimum bandwidth. One of the optimum bandwidth determination using the Generalized Cross Validation (GCV). GCV values obtained amounted to 1425.726 with a minimum bandwidth of 394. Modelling generate local polynomial of order 2 with MSE value of 1408.672

Prediction of Weekly Rainfall in Semarang City Use Support Vector Regression (SVR) with Quadratic Loss Function

Semarang city is one of the busiest city in Indonesia. Doe to its role as the capital city of Central Java, Semarang is known as having a relativity high rate economic activities. The geographic of Semarang city bordered by the Java sea, thus whenever the rainfall is high, there could be flood at certain area. Therefore, prediction of rainfall is very important. Support vector machine (SVM) is one of the most popular methods in nonlinear approach. One of the branches of this method for prediction is support vector regression (SVR). SVR can be approached by quadratic loss function. The study is focus on Semarang rainfall prediction during 2009 to 2013 using several kernel function. Kernel Function can provide optimal weight Some of kernel functions are linear, polynomial, and Radial Basis Function (RBF). Using this method, the study provide 71.61% R-square in the training data, for C parameter 2 with polynomial (p=2), and 71.46% R-square for the testing dat

Peramalan Beban Puncak Pemakaian Listrik Di Area Semarang Dengan Metode Hybrid Arima (Autoregressive Integrated Moving Average)-anfis (Adaptive Neuro Fuzzy Inference System) (Studi Kasus Di PT Pln (Persero) Distribusi Jawa Tengah Dan DIY)

Electricity become one of the basic needs in society, so that the demand level for electricity even bigger as more complex activities in society. In order to fulfill the needs of electricity in Indonesia, PT PLN have to do electrical peak load forecasting to prevent electrical crisis. In this research, we use hybrid ARIMA-ANFIS methods to forecast daily peak load of electricity in Semarang period December 2014 until January 2015. The use of hybrid ARIMA-ANFIS is to capture both linear and nonlinear patterns in the data, because sometimes time series data can contain both linear and nonlinear patterns. Since ARIMA can not deal with nonlinear patterns while ANFIS is not able to handle both linear and nonlinear patterns alone. The accuracy of the model was measured by symmetric MAPE (sMAPE) criteria, in which the best model chosen is the model with the smallest sMAPE value. The results showed that the hybrid ARIMA-ANFIS model that used to predict the daily peak load electricity in Semarang during the period of December 2014 until January 2015, comes from combination between SARIMA (0,1,1)(0,1,1)7 model and residual forecasting with ANFIS model using first lag input, Gaussian membership function in 3 clusters

Analisis Support Vector Regression (Svr) dalam Memprediksi Kurs Rupiah terhadap Dollar Amerika Serikat

In economy, the global markets have an important role as a forum for International transactions between countries in selling or purchasing goods or services on an International scale. Money as legal tender in the trading activities, but the problem is the difference between the state of the currency, the exchange rate will be established. Exchange rate is the value of a country\u27s currency is expressed in another country\u27s currency value. Fluctuations in foreign exchange rates greatly affect the Indonesian economy, so the determination of the exchange rate should be beneficial to a country can run the economy well. To predict the exchange rate of the Rupiah against the United States dollar in this study used methods of Support Vector Regression (SVR) is a technique to predict the output in the form of continuous data. SVR aims to find a hyperplane (line separator) in the form of the best regression function is used to predict the exchange rate against the United States dollar with linear kernel and polynomial functions. Criteria used in measuring the goodness of the model is the MAPE (Mean Absolute Percentage Error) and R2 (coefficient of determination). The results of this study indicate that both the kernel function gives very good accuracy in the prediction results of the exchange rate with R2 of 99.99% with MAPE 0.6131% in the kernel linear and R2 result of 99.99% with MAPE 0.6135% in the kernel polynomial

Pemodelan Variabel-variabel Pengeluaran Rumah Tangga Untuk Konsumsi Telur Atau Susu Di Kabupaten Magelang Menggunakan Regresi Tobit

Censored data is the data on a dependent variable of which most of the observations are worth less than or equal to zero while others have a certain value or more than zero. Tobit regression model is a statistical model that can overcome the problems in which many independent variables is zero or called data censored. In this research, modeling eggs or milk consumption in Magelang is analyzed using tobit regression. The data used in this research is secondary data derived from Susenas Data Magelang regency 2013. The concluding results of the final modeling shows that the educational level of householder, the amount of expenditure for food in a month, the number of children in the household and the householder's profession give significant effect on household expenditures for the consumption of eggs or milk with a coefficient determination of is 60,31%. While the remaining 39,69 % is effected by other variables is not examined in this study such as the appetite of consumers and health factors

Aplikasi Metode Momen Probabilitas Terboboti Untuk Estimasi Parameter Distribusi Pareto Terampat Pada Data Curah Hujan (Studi Kasus : Data Curah Hujan Di Kota Semarang Tahun 2004-2013)

The method used to analyze the extreme rainfall is Extreme Value Theory (EVT). One of the approaches in the EVT is Peak Over Threshold (POT) which follows the Generalized Pareto Distribution (GPD). The shape and scale parameter estimates obtained using the method of probability weighted moment. The results of this research were presumptive maximum value within a period of 1 year to the period 2004 to 2013 showed that year 2009/2010 has the possibility of extreme value compared with other years. Also obtained Mean Absolute Percentage Error values ( MAPE ) of 33.19 %. This result is a big difference because the MAPE values above 10 %, thus allowing the emergence of extreme values