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 $C[\alpha,\beta]$ space-valued functions that defined on a closed interval $[f,g]\subseteq C [\alpha, \beta] $, where $C[\alpha,\beta]$ is the collection of all real-valued continuous functions defined on a closed interval $[\alpha,\beta]\subseteq \mathbb$. 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 $ C [\alpha, \beta] $, such as some properties of $ C [\alpha, \beta] $ as a Riesz space, convergence of sequences and series, $C[\alpha,\beta]$ space-valued norms and metrics, and neighborhood of a point in $ C [\alpha, \beta] $. Furthermore, we construct calculus on $ C [\alpha, \beta] $ for constructing Henstock-Kurzweil integral in the space $ C [\alpha, \beta] $ such as limit, continuity, derivative of $C [\alpha , \beta]$ space-valued functions, and convergence of a sequence of functions. To construc the Henstock-Kurzweil integral of a $ C [\alpha, \beta] $ space-valued function that defined on a closed interval $ [f, g] \subseteq C [\alpha, \beta] $, we begin by constructing a partition on $ [f, g] $. Furthermore, we define Henstock-Kurzweil integral of a $ C [\alpha, \beta] $ space-valued function that defined on a closed interval $ [f, g]\subseteq C[\alpha,\beta] $. 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 $C [\alpha , \beta]$ 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

Registration Schedule for New Students of Mathematics Doctoral Program of Mathematics and Natural Sciences University of Gadjah Mada University Even Semester T.A. 2020/2021 wave 1 starts on 13 October 2020 until 12 November 2020, wave 2 starts 10 December 2020 until 4 January 2020.

Requirements for Prospective New Students of the Mathematics Doctoral Program in Mathematics and Natural Sciences UGM Academic Year 2020/2020:

1. S2 GPA Requirements:
a. For S2 graduates who are in the same field (Mathematics / Statistics) have S2 GPA as follows:
i),003.00 on a scale of 4 or equivalent, for applicants graduating from accredited A study programs and for prospective students with 3.00 ≤ IPK < 3.25 must have 2 publications (journals/proceedings) in mathematics/statistics. read more

Announcement of Graders and Tutors for the Actuarial Science Study Program 2019/2020
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This socialization activity was held on September 4, 2019, in Meeting Room III of the Mathematics Department of the Faculty of Mathematics and Natural Sciences UGM. This socialization event is intended for students of mathematics class of 2018 who enter the even semester 2018/2019 and class of 2019, however, students of the previous generation are allowed to attend the event. This socialization activity was attended by 17 students. In this event, the Manager of FMIPA UGM Doctoral Programme in Mathematics conveys the vision, mission, goals, and objectives of the Doctoral Programme in Mathematics, the curriculum structure of the Doctoral Programme in Mathematics, the obligations and rights of doctoral students, and the monitoring and evaluation of doctoral students which is held every semester, etc. read more

Announcement to Mathematics Ph.D. students at the Faculty of Mathematics and Natural Sciences, Gadjah Mada University, that the monitoring and evaluation (Monev) for the 1st Semester of the Academic Year 2019/2020 will be conducted in early October 2019. Click here.monev 2019

Socialization of the 2017 curriculum year 2019

Closed Examination for Mathematics Doctoral Program Students in the name of Sugiyanto which was held on Freday August 9, 2019.
Title dissertation Modeling Mathematics Carcinoma Nasopharynx at the Cellular Level with supervisor Dr. rer. nat . Lina Aryati , MS, Dr. Fajar Adi Kusumo , M.Sc. and dr. Mardiah Holy Hardianti , Ph.D., Sp.PD -KHOM.(FKKMK UGM)
Exam Closed held in the Meeting Room of the KPTU FMIPA UGM Building ( 2nd floor ) . during not enough more 2.5 hours with Chair of the Examining Team : Prof. Dr. Triyono, SU and 6 members of the Examining Team namely : Promoter Team, Prof. Dr. Widodo, MS, Prof. Dr. Salmah, M.Sc. , Dr. Nanang Susyanto , M.Sc., Dr. Irwan Indrayanto A, M.Sc. and examiners from outside Faculty Mathematics and Science UI Natural Sciences Dr. Hengki Tasman. read more

Ujian Tertutup Mahasiswa S3 Matematika atas nama Winita Sulandari yang dilaksanakan tanggal 9 Agustus 2019.
Judul disertasi Model Peramalan Berbasis Singular Spectrum Analisys pada Runtun Waktu Berpola Musiman Kompleks dengan pembimbing Prof. Drs. Subanar, Ph.D., Dr. Suhartono, M.Si. dan Dr. Herni Utami, S.Si.,
Ujian Tertutup dilaksanakan di Ruang Sidang Gedung KPTU FMIPA UGM (lantai 2) berlangsung selama kurang lebih 2,5 jam dengan Ketua Tim Penguji: Dr.Edi Suharyadi, M.Si. dan 6 anggota Tim Penguji yaitu: Tim Promotor, Prof. Dr. Sri Haryatmi, M.Sc., Drs. Danardono, M.P.H., Ph.D., Dr. Abdurakhman, M.Si., Dr. Gunardi, M.Si. dan penguji dari bidang Program Studi Ilmu Komputer Departemen IKE FMIPA UGM Dr. Agus Sihabudin, S.Si., M.Kom. read more