Modeling Portfolio Based on Linear Programming for Bank Business Development Project Plan

The bank’s business processes target business plans for the next year. Existing conditions, the business plan is based on the growth asset portfolio every year, so that the purchase of productive assets awaits issuers’ offers. This condition will cause a portfolio not to be measured and the inaccuracy of portfolio selection. Asset Liability Management (ALM) is the management of the structure of assets and liabilities to achieve profit. Banking books and trading books are bank portfolios to earn income. In selecting each portfolio, it contains liquidity risk, market risk and, credit risk. The level of profit is reflected in returns, while returns and risks are a trade-off so that calculations require mathematical and simulation models. Each bank needs an overview of the composition of productive assets, as short-term, medium-term and, long-term assets must be measured risk and target achievement. Linear programming method will allocate productive assets as the bank’s leading source of income, to achieve optimization of profit on the risks received. The problem with this research is that there are 830 variables as banking assets and 19 constraints as indicators of risk. In the seventh iteration of mathematical models, return 1,803 Trillyun from 11 banking book assets

Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches

The natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr) plant growth have been analyzed and synthesized in these research through modeling using Artificial Neural Network (ANN) and Lindenmayer System (L-System) methods. Research aimed to design and to visualize plant growth modeling on the soybean varieties which these could help for studying botany of plant based on fertilizer compositions on plant growth with Nitrogen (N), Phosphor (P) and Potassium (K). The soybean plant growth has been analyzed based on the treatments of plant fertilizer compositions in the experimental research to develop plant growth modeling. By using N, P, K fertilizer compositions, its capable result on the highest production 2.074 tons/hectares. Using these models, the simulation on artificial life for describing identification and visualization on the characteristic of soybean plant growth could be demonstrated and applied

Eeg Signal Identification Based on Root Mean Square and Average Power Spectrum By Using Backpropagation

The development of user interface for game technology has currently employed human centered technology researches in which EEG signal that utilizes the brain function has become one of the trends. The present research describes the identification of EEG Signal by segmenting it into 4 different classes. The segmentation of these classes is based on Root Mean Square (RMS) and Average Power Spectrum (AVG), employed in feature extraction. Both Root Mean Square (RMS) and Average Power Spectrum(AVG) are employed to extract features of EEG signal data and then used for identification, by which a BackPropagation method is employed. The experiment,done with 200 tested signal data file, demonstrates that the identification of the signal is 91% accurate

Discrete Mean Amplitude of Glycemic Excursion (MAGE) Measurement on Diabetics with Spline Interpolation Method

Diabetes Mellitus (DM) is a disease that is characterized by glycemic disorders, including sustained chronic hyperglycemia and acute glucose fluctuations. Because DM is closely related to the body metabolism, the observation of the blood vessels becomes very important to perform. The observation is done by using the Mean Amplitude of Glycemic Excursion (MAGE). Definitively, MAGE is an important variable to solve clinical DM problems that contributes in generating oxidative stress related to the macro and microvascular complications. MAGE is technically used with continuous blood glucose data which is obtained by Continuous Glucose Monitoring (CGM). Because of the CGM is expensive for personal use, it cannot be used in the daily observation. The contribution of this study is the utilization of discrete data (3 days observation) to be used in MAGE measurement. This research employs Spline Interpolation technique to convert discrete blood glucose data to continuous signal. The validation of interpolated signal is conducted by comparing the pattern of discrete data and continuous signal for both original and clustered data. The experiment showed that both scenarios depicted identical pattern. The smallest RMSE was achieved by Linear Spline with 57.66 while the highest RMSE value was obtained by Quadratic Spline with 177.00

Fractal Based on Noise for Batik Coloring using Normal Gaussian Method

Noise is an un-expected signal which exists naturally at any system. In the study of fractal batik coloring, noise as a spot is generated as the basis of batik motive coloring. Even distribution of noise spots will produce art-works which involve elements of culture and technology. The development of batik motives and colors could be harmonized with the development of technology, such as the use of fractal method in order to create the new motives of batik. Fractal is a geometric form which can be separated into pieces, where each part is the repeated small version. The coloring of batik was based on the generating noise using Gaussian method. Noise on fractal batik was spots which were generated randomly on the surface of fractal batik, meanwhile Gaussian method was a noise model which followed normal distribution standard with zero average and standard deviation 1.The generating noise as coloring basis of fractal batik patterns, which was formed in the previous study, showed the distant error of noise between 9.1 pixels and 13.7 pixels. This was because the distribution of noise on the fractal batik patterns was carried out randomly using Gaussian method for every process of fractal rewriting system

Facial Emotional Expressions Of Life-Like Character Based On Text Classifier And Fuzzy Logic

A system consists of a text classifier and Fuzzy Inference System FIS to build a life-like virtual character capable of expressing emotion from a text input is proposed. The system classifies emotional content of sentences from text input and expresses corresponding emotion by a facial expression. Text input is classified using the text classifier while facial expression of the life-like character are controlled by FIS utilizing results from the text classifier. A number of text classifier methods are employed and their performances are evaluated using Leave-One-Out Cross Validation. In real world application such as animation movie the lifelike virtual character of proposed system needs to be animated. As a demonstration examples of facial expressions with corresponding text input as results from the implementation of our system are shown. The system is able to show facial expressions with admixture blending emotions. This paper also describes animation characteristics of the system using neutral expression as center of facial expression transition from one emotion to another. Emotion transition can be viewed as gradual decrease or increase of emotion intensity from one emotion toward other emotion. Experimental results show that animation of lifelike character expressing emotion transition can be generated automatically using proposed system

Application of Interval Type-2 Fuzzy Logic System in Short Term Load Forecasting on Special Days

This paper presents the application of Interval Type-2 fuzzy logic systems (Interval Type-2 FLS) in short term load forecasting (STLF) on special days, study case in Bali Indonesia. Type-2 FLS is characterized by a concept called footprint of uncertainty (FOU) that provides the extra mathematical dimension that equips Type-2 FLS with the potential to outperform their Type-1 counterparts. While a Type-2 FLS has the capability to model more complex relationships, the output of a Type-2 fuzzy inference engine needs to be type-reduced. Type reduction is used by applying the Karnik-Mendel (KM) iterative algorithm. This type reduction maps the output of Type-2 FSs into Type-1 FSs then the defuzzification with centroid method converts that Type-1 reduced FSs into a number. The proposed method was tested with the actual load data of special days using 4 days peak load before special days and at the time of special day for the year 2002-2006. There are 20 items of special days in Bali that are used to be forecasted in the year 2005 and 2006 respectively. The test results showed an accurate forecasting with the mean average percentage error of 1.0335% and 1.5683% in the year 2005 and 2006 respectively