The research entitled  “The Codifference Function of Moving Average Processes With Symmetric -stable Innovation ” was conducted by Iqbal Kharisudin under the guidance of Dr.rer.nat., Dedi Rosadi, M.Sc., Dr. Abdurakhman, M.Si., and Dr. Suhartono, M.Sc. in 2018.

The following is the abstract of this research.

ABSTRACT

Most of statistical models require the existence of a second-order moment or based on the distribution with finite variance. We can study the dependency structure of the model based on second-order moment. In the time series modeling with finite variance innovation (eg Gaussian model), the autocorrelation function (ACF) plays an important role. One of the applications of ACF on Box-Jenkins time series modeling is for model identification. In the MA() models, we found that the ACF values is zero after lag q. Such essential properties are used as a basis for identifying the MA process given by time-series data. read more

The research entitled  “N-soft Sets and Their Decision-Making Algorithms” was conducted by Fatia Fatimah under the guidance of Dr.rer.nat., Dedi Rosadi, S.Si., M.Sc., and Dr. Raden Bagus Fajriya Hakim, S.Si., M.Si. in 2018.

The following is the abstract of this research.

ABSTRACT

Decision-making uncertainty has been applied in various fields. Soft set is one of the theories that can handle the decision-making problems under uncertainty where its parameters as the benchmark of decision making. The parameters may be real numbers, words, sentences, functions and so on. Therefore, it is flexible in representing individual needs. In everyday life, data sets can be found in the form of probabilities or rankings. Unfortunately, there is no literature in soft sets for handling decision-making problems using probabilities or N ary rankings. Thus, this research discusses about decision-making approach using probabilistic soft sets and N-soft sets. The results are as follows. First, probabilistic soft sets is useful for decision making where its probability distributions based on parameters. Seven decision-making algorithms are proposed i.e., Probabilistic Soft Sets-Choice Values, Probabilistic Soft Sets-Minimax, Probabilistic Soft Sets-Opportunity Cost, Probabilistic Soft Sets-Weighted Choice Values, Probabilistic Soft Sets-Weighted Minimax, Probabilistic Soft Sets Weighted Opportunity Costs, and Probabilistic Soft Sets Positive Matrices. Second, dual probabilistic soft sets, as an adaptation of probabilistic soft sets, is necessary for handling decision-making problems where its probability distributions based on objects. Dual Probabilistic Soft Sets Positive Matrices algorithm is presented. Third, N-soft sets can handle decision making with binary or non-binary rankings. Three decision-making algorithms are introduced i.e., Extended Choice Values, Extended Weight Choice Values, and T-Extended Choice Values. They are applied to a real case study. Fourth, graded soft set, a special case of N-soft set, draws a bridge between decision making in soft set and social choice theory. Its decision-making mechanism using choice values coincides with the Borda count in voting. read more

The research entitled  “Analysis and Design of Input-Output Group Decoupling Problems for Regular Linear Descriptor System with Index One” was conducted by Arman under the guidance of Dr.rer.nat. Ari Suparwanto, M.Si. and Prof. Salmah, M.Si. in 2018.

The following is the abstract of this research.

ABSTRACT

In the control systems, every input is generally controls more than one output and every output can be controlled by more than one input. Such a system is called coupled system. In general, the coupling system is very difficult to control. It is also known that not all coupling systems can be converted into a decoupling system. Therefore it is necessary to design some compensator such that the coupling system can be converted into a decoupling system in the sense that every input controls only one output and every output is controlled by only one input. This problem is called the input-output decoupling problem. read more

The research entitled  “A Characterization of Cut Set on Semilattice-Valued Fuzzy Sets” was conducted by Harina Orpa Lefina Monim under the guidance of Prof. Dr. Sri Wahyuni, MS.  and Dr. Indah Emilia W., M.Si. in 2018.

The following is the abstract of this research.

ABSTRACT

Semilattice is a partially ordered set which a pair of its elements have infimum or supremum. When it is equipped by an equivalence relation defines as for any where and and are principle filters generated by respectively. Semilattice is partitioned into equivalence classes-. A collection of the classes- forms a poset under inclusion. We call the collection as a quotient set and denoted . The quotient set is a poset under inclusion. Generally, the poset of a quotient set is not a semilattice. read more

The research entitled “Developing of Mean-Variance Portfolio Modeling Using Robust Estimation and Robust Optimization Method” was conducted by Epha Diana Supandi under the guidance of Prof.Dr. rer. nat. Dedi Rosadi, S.Si., M.Sc. and Dr. Abdurakhman, S.Si., M.Si in 2018.

The following is the abstract of this research.

ABSTRACT

The person who pioneered the basic theory of portfolio selection was Markowitz (1952) who shed light on the concept of mean-variance (MV) in allocating the asset and management of active portfolio. Mean vector and variance-covariance matrix must be discovered at hand as an entry in the procedure of developing optimum portfolio of MV model requiring estimation. There are a number of estimation techniques to apply parameter estimation, all of which are not free from estimation error. As a pivotal input in the making of mean-variance portfolio model, estimation error will impinge on the output of optimum portfolio formation. Some researchers have built robust portfolio, ie the portfolio that can reduce the estimation error of the mean vector and variance-covariance matrix of the portfolio MV’s model. There are two standard approaches in the formation of the optimal robust portfolio through robust estimation and robust optimization approaches The formation of optimum portfolio through robust estimation can be carried out in two stages. The first stage, the estimation of mean vector and covariance matrix constructed by using robust estimators. After the robust estimator came to light, it is being input to the MV portfolio model to attain robust estimation portfolio of MV model. This research selects two robust estimators with high breakdown namely S estimator, Constrained-M (CM) and Fast Minimum Covariance Determinant (FMCD). Unlike robust statistic approach, the theoretical basis of robust optimization is to reduce the sensitivity of optimum portfolio due to uncertain estimation of mean vector and variance-covariance matrix. The parameter input of robust portfolio optimization is considered uncertain situated in the uncertainty set. Afterwards, the optimum solution of this model which is accomplished for the worst solution occurs at the minimum expected return and maximum risk. In the robust optimization, the uncertainty set has a pivotal role for determining parameter. Until now there is no fixed certainty as how to determine uncertainty set accurately. In this study, a new approach is carried out to construct the set of uncertainty for the mean vector and variance-covariance matrix ie using block Bootstrap percentile method. This method is appropriately used because the resampling is performed on return data, therefore the structure of dependencies between data is not lost. The determination of optimum portfolio in the value of the worst cases of the robust optimization becomes one drawback of this method. One of the potential consequences of this approach is a decision strongly influenced by the extreme observations (outlier) in the set of uncertainty. As a result, the portfolio will tend to be too pessimistic and unable to attain optimum result. To overcome these obstacles, then this research also attempts to develop MV portfolio by combining robust optimization with robust estimator. The research leads to unbiased formulation for optimum portfolio of MV model, portfolio model formulation for robust estimation, and development of portfolio model of robust optimization. The research also builds computation program to ease the end-user in utilizing the resulted theory. Afterwards, the resulted portfolio models will be applied in the registered share data as a blue chip share. The last stage of the research is the performance comparison of those portfolios by using in-samples and out-samples analysis. read more

The research entitled  “Optimalisasi Norma Jangkauan Vektor Eigen Atas Aljabar Maks-Plus Interval” was conducted by Siswanto under the guidance of Dr.rer.nat. Ari Suparwanto, M.Si. and Dr. M. Andy Rudhito, S.Pd., M.Si. in 2018.

The following is the abstract of this research.

ABSTRACT

Let R be the set of real numbers and R epsilon: the set of real number union infinite negative. Max-plus algebra is algebraic structure formed from the set R epsilon equipped with maximum and addition operations. A matrix in the size of m times n, whose components belong to R epsilon, called matrix over max-plus algebra. The set of matrices over max-plus algebra is denoted by R epsilon power m times n. Algebraic structure formed from the set of all interval in R epsilon union [infinite negative, infinite negative] equipped with maximum and plus operations is called interval max-plus algebra. A matrix in the size of m times n, whose components belong to all interval in R epsilon union [infinite negative,infinite negative], called matrix over interval max-plus algebra. In this research discussed about optimizing range norm of eigenvector over interval max-plus algebra and optimizing range norm of eigenvector over interval max-plus algebra with prescribed components. This research based on expanssion of max-plus algebra into interval max-plus algebra, optimizing range norm of eigenvector over max-plus algebra and optimizing range norm of eigenvector over max-plus algebra with prescribed components. The discussing initiated how to find eigenvalue. eigenvector and eigenvector space over max-plus algebra the generally matrices included reducible and irreducible matrices. In the last part of this research given application examples of the problem of eigenvalue and eigenvector over interval max-plus algebra in the production system. The concepts related to the main discussion were also investigated in this research. The concepts related, namely how to indicate the existence and uniqueness of solution of linear equation system in interval max-plus algebra by the normalization of linear equation system. Included in the discussion of linear equation system are the concepts about image set of matrices over interval max-plus algebra, strongly reguler matrices and simple image set. read more

The research entitled  “Desain dan Evaluasi Performa Model Wavelet Neural Network untuk Pemodelan Time Series” was conducted by Syamsul Bahri under the guidance of Prof. Dr.rer.nat. Widodo, M.S. and Prof. Drs. Subanar, Ph.D. in 2017.

The following is the abstract of this research.

ABSTRACT

The development of time series analysis models and techniques are in line with the development of science and technology. Since the decade of the 1990, soft computing techniques that include fuzzy techniques, neural network and wavelet were started to be used as an alternative model for the analysis of time series. The application of wavelet function as the activation function on the neural network model is known as the wavelet neural network (WNN) model. Based on WNN model, in this dissertasion has developed two neural network models for time series forecasting. The first model of the WNN developed is feed forward neural network (FFNN) model which accommodates the excellence of discrete wavelet transform of Haar for the pre-processing stage of data, and the excellence of multiresolution of wavelet B-spline used as the activation function, as well as the gradient descent algorithm with momentum as the optimization algorithm in the process of updating parameters. Design of architecture of the WNN model consists of six layers with three hidden layers. The second model has developed is WNN-F. The WNN-F model is the modification of the WNN model has developed in this research through by the implementation of fuzzy method to determine one of the parameter values in the model of WNN. These parameters are treated as exogenous parameters of the model, as a result these parameters are not updated in the learning process. The Fuzzy methods is the process in which the fuzzyfication-defuzzyfication of input. The fuzzification process using Gaussian membership functions and the defu-zzification process using fuzzy inference type TSK (Takagi-Sugeno-Kang). Design of architecture of the WNN-F model is consists of seven layers with three hidden layers. Based on the simulation and case study, the performance evaluation both of the model results have be achieved. Empirical results of the simulated case shows that based on MSE value and running time indicators, WNN-F model is better than WNN model. But, the performance of WNN model based on the value of AIC indicator is better than the performance of WNN-F model. read more

The research entitled  “Integral Henstock-Kurzweil di Dalam Ruang C[Alfa,Beta]” was conducted by Firdaus Ubaidillah under the guidance of Prof. Dr. Soeparna Darmawijaya and Prof. Dr. Ch. Rini Indrati in 2017.

The following is the abstract of this research.

ABSTRACT

This dissertation is the result of research to construct of Henstock-Kurzweil type integral for space-valued functions that defined on a closed interval , where is the collection of all real-valued continuous functions defined on a closed interval . The concept of this integral follows Henstock-Kurzweil integral concept in the real space that has been known so far. We begin by introducing some fundamental concepts about the space , such as some properties of as a Riesz space, convergence of sequences and series, space-valued norms and metrics, and neighborhood of a point in . Furthermore, we construct calculus on for constructing Henstock-Kurzweil integral in the space such as limit, continuity, derivative of space-valued functions, and convergence of a sequence of functions. To construc the Henstock-Kurzweil integral of a space-valued function that defined on a closed interval , we begin by constructing a partition on . Furthermore, we define Henstock-Kurzweil integral of a space-valued function that defined on a closed interval . From this definition, we develop some properties of a Henstock-Kurzweil integrable function into theorems. From a Henstock-Kurzweil integrable function, we define a primitive of a Henstock-Kurzweil integrable function. To know some properties of a primitive of a Kurzweil-Henstock integrable function, we discuss the absolute Henstock-Kurzweil integral. In this dissertation, we give a brief discussion to Denjoy integral of space-valued functions. Further, we show that Denjoy integral is equivalent with Henstock-Kurzweil integral. To discuss this, we introduce the bounded variation function and the absolute continuous function. Finally, we discuss some convergence theorems of a sequence of Henstock-Kurzweil integrable functions. Our objective here is to prove that the monotone, uniform, controlled and dominated convergences of a sequence of Henstock-Kurzweil integral functions imply the convergence of the sequence formed by its corresponding integrals. read more

The research entitled  “Recurrent Neural Network untuk Peramalan Runtun Waktu dengan Pola Long Memory” was conducted by Walid, S.Pd., M.Si. under the guidance of Prof.Drs. Subanar, Ph.D.,  Prof.Dr.rer.Nat. Dedi Rosadi, M.Si., dan Dr. Suhartono, M.Si. in 2017.

The following is the abstract of this research.

ABSTRACT

In daily practice, modeling of time series was often not only involve the lag or order autoregressive (AR) but also involves a lag or order moving average (MA). This condition occurs in both the linear model which known as the model of autoregressive moving average (ARMA) and the nonlinear models, which is one of its forms is a model of recurrent neural networks (RNN). Feedforward neural networks (FFNN) is one of nonlinear models that can be viewed as a group of highly flexible model that can be used for various applications. Recurrent Neural Network as one of the hybrid models are often used to predict and estimate the issues related to electricity, can be used to describe the cause of the swelling of electrical load which experienced by PLN. In this research will be developed RNN forecasting procedures at the time series with long memory patterns. Considering the application is national electrical load which of course has a different trend with the condition of the electrical load in any country. This research produce the algorithm of time series forecasting which has long memory pattern using FFNN hereinafter referred to the algorithm of fractional integrated feedforward neural networks (FIFFNN). In addition, this research also produce the algorithm of time series forecasting which has long memory pattern using RNN in this case using E-RNN hereinafter referred to the algorithm of integrated fractional recurrent neural networks (FIRNN). The forecasting results of long memory time series using the model of Fractional Integrated Feedforward Neural Network (FIFFNN) showed that the model with the selection of data difference in the range of [-1,1] and the model of Fractional Integrated Feedforward Neural Network (FIFFNN) (24,7,1) provides the smallest MSE value, which is 0.00170185. The forecasting results of long memory time series using models Fractional Integrated Recurrent Neural Network (FIRNN) showed that the model with the selection of data difference in the range of [-1,1] and the model of Fractional Integrated Recurrent Neural Network (FIRNN) (24,6,1) provides the smallest MSE value, which is 0.00149684. read more

The research entitled  “wavelet Neural Network Modeling With Genetic Algorithm” was conducted by Budi Warsito under the guidance of Prof. Drs. Subanar, Ph.D.,  and Dr. Abdurakhman, M.Si. in 2017.

The following is the abstract of this research.

ABSTRACT

Neural Network (NN) is one of the non-linear model which has been developed a lot in statistical modeling, especially in time series analysis. Many researches have shown the advantages of this model compared to the others. However, there are still some problems in NN modeling for time series, including the handling of the input preprocessing, inconsistency of the estimation results and the selection of optimal architecture. The handling of the input preprocessing problem is carried out through the separation of data based on the original components and the noise using wavelet decomposition. Wavelet coefficients resulting from the decomposition are then become the inputs of NN model.Wavelet transformation which is regarded more appropriate for time series data is Maximal Overlap Discrete Wavelet Transform (MODWT), because each level of the decomposition contained wavelet coefficients and scaling coefficients as many as the length of the data. This advantage reduces the weaknesses of filtering by Discrete Wavelet Transform (DWT) which can not be performed on any sample size. The next problems arising is how to determine the level of decomposition and the number of coefficients in each level. In this study, the Maximal Relevance Minimum Redundancy (mRMR) criteria is applied in MODWT decomposition to obtain the optimal input. The criteria based on the Mutual Information (MI) value has been selected due to consider the redundancy between the input variable itself, as well as its value is based on the relevance of input variables with the target. read more