Estimasi Regresi Non Parametrik Dengan Metode Wavelet Shrinkage Neural Network Pada Model Rancangan Tetap

If X is a predictor variable and Y is a response variable of following model Y = g(X) +e with function g is a regression which not yet been known and e is an independent random variable with mean 0 and variant . The function of g can be estimated by parametric and nonparametric approach. In this paper, g is estimated by nonparametric approach that is named wavelet shrinkage neural network method. At this method, the smoothly function estimation is depending on shrinkage parameter's that are threshold value and level of wavelet that be used. It also depending on the number of neuron in the hidden layer and the number of epoch that be used in feed forward neural network. Therefore, it is required to be select the optimal value of threshold, level of wavelet, the number of neuron and the number of epoch to determine optimal function estimation

Pemilihan Variabel pada Model Geographically Weighted Regression

Regression analysis is a statistical analysis that aims to model the relationship between response variable with some predictor variables. Geographically Weighted Regression (GWR) is statistical method used for analyzed the spatial data in local form of regression. One of the problems in GWR is how to choose the significant variables. The number of predictor variables will allow the violation of assumptions about the absence of multicollinearity in the data. Therefore, this needs a method to reduce some of the predictor variables which not significant to the response variable. This paper will discuss how to select significant variables by stepwise method. This method is a combination of forward selection method and the backward elimination method

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

Pemetaan Penyakit Demam Berdarah Dengue dengan Analisis Pola Spasial di Kabupaten Pekalongan

The number of dengue haemorrhagic fever (DHF) incidence in Pekalongan from year to year is very volatile. In 2006, there was 352 cases, 718 cases occurred in 2007, 2008 saw 403 cases, 2009 there were 753 cases, whereas in 2010 a decline to 223 cases. This is possible due to the lack of information about the place, time and location of the incident spread of dengue in Pekalongan. Various efforts have been made to address these issues both society and government but the incidence of this disease has not been effectively suppressed. The results of data analysis showed that the incidence of dengue in Pekalongan mostly occurs during the rainy season is the period from January to June. The DHF incidence tends to be higher in Kedungwuni. Highest incidence of DHF occurred in April 2010. In addition, there are some months that indicate the spatial relationships in the incidence of dengue in Pekalongan, ie January, February, July, October and December. The sub-district that has a positive autocorrelation is Kedungwuni, Wonopringgo, and Tirto. While the sub-district has a negative autocorrelation is Karangdadap. Most of the sub-districts in Pekalongan status is still endemic for dengue

Analisis Variabel Kanonik Biplot Untuk Bank Umum Di Jawa Tengah

Bank Competition in Indonesia increase due to good economic growth and the improvement of the social middle class in Indonesia. Increased bank raises the fierce competition between banks and internal banks themselves. This makes the management of the bank should work seriously to maintain its existence. In this case the assessment of the bank become very important in the banking business to survive in today's banking industry. This study was conducted to determine the competitive commercial banks operating in Central Java with the Canonical Variate Analysis (CVA) Biplot. This analysis can be applied to find out information about the relative position, the similarity between the object characteristics and diversity of variables in the three groups of commercial banks in Central Java, namely state-owned banks, private banks and private banks Non Foreign Exchange, based on the health aspects of the bank. The results obtained are the banks in each group had different characteristics shown in the relative position of the already well-separated in the resulting biplot. Variables that tend to influence the grouping of commercial banks are Capital Adequacy Ratio (CAR). The total assets is variable with the highest level of prediction accuracy on each bank

Klasifikasi Data Berat Bayi Lahir Menggunakan Weighted Probabilistic Neural Network (WPNN) (Studi Kasus di Rumah Sakit Islam Sultan Agung Semarang)

Low Birthweight (LBW) is one of the causes of infant mortality. Birthweight is the weight of babies who weighed within one hour after birth. Low birthweight has been defined by the World Health Organization (WHO) as weight at birth of less than 2,500 grams (5.5 pounds). There are several factors that influence the BWI such as maternal age, length of gestation, body weight, height, blood pressure, hemoglobin and parity. This study uses a Weighted Probabilistic Neural Network (WPNN) to classify the birthweight in RSI Sultan Agung Semarang based on these factors. The results showed that the birthweight classification using WPNN models have a very high accuracy. This is shown by the model accuracy of 98.75% using the training data and 94.44% using the testing data

Peramalan Volatilitas Menggunakan Model Generalized Autoregressive Conditional Heteroscedasticity in Mean (Garch-m) (Studi Kasus Pada Return Harga Saham PT. Wijaya Karya)

Stock return volatility in the markets of developing countries (emerging markets) is generally much higher than the markets of developed countries. High volatility illustrates the level of high risk faced by investors due to reflect fluctuations in stock price movement. Therefore, it is probable, stock investments that are carried in Indonesia have a high risk opportunity. Important properties are often owned by time series data in the financial sector in particular to return data that the probability distribution of returns is fat tails and volatility clustering or often referred to as a case of heteroscedasticity.Time series models that can be used to model this condition are ARCH and GARCH. One form of ARCH/GARCH is Generalized Autoregressive Conditional Heteroscedasticity In Mean (GARCH-M). The purpose of this study is to predict volatility by using GARCH-M model in the return data analysis of daily stock price closing of Wijaya Karya (Persero) Tbk from October 18, 2012 until March 14, 2014 by using the active days (Monday to Friday). The best model is used for forecasting the volatility case in the stock price return of PT. Wijaya Karya is ARIMA (0,0, [35]) GARCH (1,1)-M

Prediksi Indeks Harga Saham Gabungan Menggunakan Support Vector Regression (Svr) dengan Algoritma Grid Search

The existence of capital market Indonesia is one of the important factors in the development of the national economy, proved to have many industries and companies that use these institutions as a medium to absorb investment and media to strengthen its financial position. Capital market Indonesia is an emerging market development is very vulnerable to global economic conditions and capital markets of the world. Prediction JCI (Jakarta Composite Index) is necessary to know the great value that will occur in the future so as investors can take the right policy. To predict in this study used a Support Vector Regression (SVR) method to find the hyperplane in the best regression function to predict the closing price of the JCI using a linear kernel function with output in the form of continuous data. Parameter selection cost and epsilon using a grid search algorithm combined with cross validation and obtained best cost 1 and best epsilon 0.1. While the criteria to measure the goodness of the model is MAPE (Mean Absolute Percentage Error) and R2 (Coefficient Determination). The results of this study showed that SVR with linear kernel function provides excellent accuracy in the prediction of JCI with R2 results on training data 98.4% with a MAPE 0.873% while the testing of data R2 90.9% with a MAPE 0.613%