“UGM Mathematics Ph.D. students held a Mini Tutorial and Discussion event on October 13, 2019. This event was attended by 21 students, and the presentation was given by M. Fariz Fadilah Mardiyanto, focusing on time series data with a nonparametric approach, especially Fourier. 
Closed examination of Ph.D. students in the name of Ahmad Faisol which was held on Wednesday, October 30, 2019.
Dissertation title “T [[S] Generalized Rank Series Module – Noether”. with supervisor Prof. Dr. Sri Wahyuni, S.U. and Dr. Budi. Suridjo, M.Sc.
The Closed Examination was held in the Meeting Room of the FMIPA UGM KPTU Building (2nd floor) which lasted for approximately 2.5 hours with the Head of the Examining Team: Prof. Dr. Triyono, S.U. and 7 members of the Examination Team namely Prof. Dr. Sri Wahyuni, S.U., Dr. Budi Surodjo, M.Sc., Dr.rer.nat. Indah Emilia Wijayanti, S.Sc., M.Sc., Dr.rer.nat. Yeni Susanti, S.Sc., M.Sc., Dr. Sutopo, S.Sc., M.Sc., Dr. Sumardi, M.S. and guest examiners from the Faculty of Mathematics and Natural Sciences, State University of Malang: Dra. Santi Irawati, Ph.D.
$S\alpha S$ The research entitled “The Codifference Function of Moving Average Processes With Symmetric $\alpha$-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($q$) 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.
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.
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.
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 $(S,\leq)$ 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 $p\approx_{M} q \iff \uparrow p \cap M = \uparrow q \cap M$ for any $p, q \in S$ where $M\subseteq S, M\neq \emptyset$ and $\uparrow p$ and $\uparrow q$ are principle filters generated by $p,q$ respectively. Semilattice $S$ is partitioned into equivalence classes-$\approx M$. A collection of the classes-$\approx M$ forms a poset under inclusion. We call the collection as a quotient set and denoted $S/ \approx M$. The quotient set is a poset under inclusion. Generally, the poset of a quotient set is not a semilattice.
Comprehensive Examination for M. Ivan Ariful Fathoni
Date: Wednesday, October 2, 2019
Time: 13:30 – 16:00
Location: Meeting Room 1, 1st Floor, Department of Mathematics
Examination Committee:
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.
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.
