Retno Subekti

The application of Elman recurrent neural network model for forecasting consumer price index of education, recreation and sports in Yogyakarta

Recurrent neural network is a network which provides feedback connections. This network is believed to have a more powerful approach than the typical neural network for learning given data. The current research was aimed to apply the simplest recurrent neural network model, namely the Elman recurrent neural network (ERNN) model, to the consumer price index (CPI) of education, recreation, and sports data in Yogyakarta. The pattern of CPI data can be categorized as a function of time period, which tends to move upwards when the time period is increased, and jump at some points of the time period. This pattern was identified as similar to the pattern resulted by the function of the truncated polynomial spline regression model (TPSR). Hence, this research considered ERNN model which the inputs such as in the TPSR model were established by taking into account the location of the knot or jump points. In addition, the ERNN model with a single input, a time period was also generated. The results demonstrated that the two models have high accuracy both in training and testing data. More importantly, it was found that the first model is more appropriate than the second one in testing data.

Fuzzy bi-objective linear programming for portfolio selection problem with magnitude ranking function

Fuzzy bi-objective linear programming (FBOLP) model is bi-objective linear programming model in fuzzy number set where the coefficients of the equations are fuzzy number. This model is proposed to solve portfolio selection problem which generate an asset portfolio with the lowest risk and the highest expected return. FBOLP model with normal fuzzy numbers for risk and expected return of stocks is transformed into linear programming (LP) model using magnitude ranking function.

Portfolio selection in Indonesia stock market with fuzzy bi-objective linear programming

Selecting a portfolio which has the lowest investment risk and also the highest investment return known as a portfolio selection problem. An alternative way finding optimum solution of this bi-objective programming problem is transforming the problem into a single objective programming problem using fuzzy decision-making theory. The investment risk is expressed by mean absolute deviation of the return assets, while the investment return is expressed by the average of return assets. This fuzzy bi-objective linear programming (FBLP) is applied to construct an optimum portfolio in Indonesian stock market. The numerical result of FBLP is the same compared with weighted sum approach, but FBLP integrates better the knowledge and subjective opinion of investor where the range rate of risk which can be accepted by investor is incorporated in the model.