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All Journal Jurnal Ekonomi Kuantitatif Terapan dCartesian: Jurnal Matematika dan Aplikasi Media Statistika Indonesian Journal of Mathematics and Natural Sciences Gadjah Mada International Journal of Business Jurnal Sains Matematika dan Statistika Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) International Journal on Emerging Mathematics Education Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) ComTech: Computer, Mathematics and Engineering Applications Jurnal Linguistik Komputasional Imajiner: Jurnal Matematika dan Pendidikan Matematika KAIBON ABHINAYA : JURNAL PENGABDIAN MASYARAKAT Jurnal Abdi Insani SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Journal of Science and Science Education Jurnal Akuntansi dan Keuangan Prosiding University Research Colloquium Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya d'Cartesian: Jurnal Matematika dan Aplikasi International Journal of Computing Science and Applied Mathematics-IJCSAM
Didit Budi Nugroho
Universitas Kristen Satya Wacana
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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MATHEMATICAL SILVER FOR ENTREPRENEURIAL MATHEMATICS Parhusip, Hanna Arini; Nugroho, Didit Budi; Purnomo, Hindriyanto Dwi; Kawuryan, Istiarsi Saptuti Sri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (472.285 KB) | DOI: 10.30598/barekengvol16iss4pp1175-1184

Abstract

This article shows the result of entrepreneur mathematics by creating mathematical objects from silver. The objects discussed here are accessories to introduce undergraduate students to integrating several aspects of learning mathematics. These are learning geometry modernly, mathematical art, popularizing mathematics for society, introducing entrepreneurial values using mathematics, teamwork for achieving targets, and considering local heritage in mathematics. These aspects are blended into activity by creating designs and producing products based on the obtained designs. The particular product for this activity is creating silver accessories. The used research method is initiated by creating designs with the help of software where the surface equations are known. After the designs are obtained, the designs are communicated to the silver craftsman to be a partner in design testing and manufacturing of accessories products using the given designs. The size and the similarity of perceptions to the appearance of the design are discussed because the actual design is a three-dimensional image but expressed in objects to be two- dimensional objects. After productions are obtained, the accessories are managed to be promoted to the marketplace and social media as a form of entrepreneurial activity with materials starting from mathematics.
Study on The Continuous-Jump Behavior of Asset Return Volatility Through The GJR Model Alfagustina, Yumita Cristin; Nugroho, Didit Budi; Tita, Faldy
Prosiding University Research Colloquium Proceeding of The 17th University Research Colloquium 2023: Bidang MIPA dan Kesehatan
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

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Abstract

Generalized Auto-Regressive Conditional Heteroskeasticity (GARCH) is a model used to predict the volatility of returns. Volatility is a statistical measure of the movement of returns for securities (financial instruments that can only be traded through markets or securities companies) or certain market indices. Then the GARCH model was further developed into an asymmetric form, namely conditional volatility and returns have a relationship, namely the GJR model which is an abbreviation of the name (Glosten- Jagannathan-Runkle). This research focuses on the GJR-X by adding high-frequency exogenous variables in volatility process and on the GARCH-CJ which is a decomposition of the exogenous variable X, namely the continuous component C (Continuous) and the jump J (Jump). TOPIX data (Tokyo Stock Price Index) is the real data used in this study. To estimate the model parameters, the ARWM (Adaptive Random Walk Metropolis) method will be used with the MCMC (Markov Chain Monte Carlo) algorithm. First, it was found that the ARWM method is good at estimating parameters. Second, the AIC value of GJR-CJ was smaller than that of GJR-X, which means that GJR-CJ had better data fitting.
PENGABDIAN MASYARAKAT UNTUK PEMBELAJARAN CODING ARTIFICIAL INTELLIGENCE KEPADA SISWA SMP KRISTEN WONOSOBO Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Kurniawan, Johanes Dian; Susanto, Bambang; Setiawan, Adi; Nugroho, Didit Budi
Jurnal Abdi Insani Vol 11 No 2 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i2.1536

Abstract

Artificial intelligence and the Internet of Things (AIOT) have been widely used by various activities, especially in the millennial generation. However, scientific technology has not been widely introduced in education. Additionally, schools experience a decline in student enrollment every year, so it is necessary to carry out innovative learning actions that can be introduced to the community through students. Innovation learning is demonstrated by providing coding lessons that students have never done before so that AIOT becomes part of the learning. Therefore, coding as a learning method is  introduced to junior students so they can get to know AIOT early. The method used is making a device called AIOT-kit with training to be able to directly monitor environmental parameters such as temperature and humidity. The Internet of Things was introduced, which uses ThinkSpeak as a dashboard for making observations. This device was made by students so that they could follow the process from making the AIOT-kit hardware and related coding to utilization. It is shown that AIOT-kit is not yet known to students, including how to code in it. AIOT is an urgent need to access developing related technology. This activity is part of the service team's efforts to make a positive contribution to the community and school environment. After carrying out this activity, there was a change in how students could make their own AIOT-kit devices while also coding. The school even received an award from the local government for the innovation activities carried out during that period.
GRG Non-Linear and ARWM Methods for Estimating the GARCH-M, GJR, and log-GARCH Models Nugroho, Didit Budi; Panjaitan, Lam Peter; Kurniawati, Dini; Kholil, Zaini; Susanto, Bambang; Sasongko, Leopoldus Ricky
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 2 (2022): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i2.7694

Abstract

Numerous variants of the basic Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have been proposed to provide good volatility estimating and forecasting. Most of the study does not work Excel’s Solver to estimate GARCH-type models. The first purpose of this study is to provide the capability analyze of the GRG non-linear method built in Excel’s Solver to estimate the GARCH models in comparison to the adaptive random walk Metropolis method in Matlab by own codes. The second contribution of this study is to evaluate some characteristics and performance of the GARCH-M(1,1), GJR(1,1), and log-GARCH(1,1) models with Normal and Student-t error distributions that fitted to financial data. Empirical analyze is based on the application of models and methods to the DJIA, S&P500, and S&P CNX Nifty stock indices. The first empirical result showed that Excel’s Solver’s Generalized Reduced Gradient (GRG) non-linear method has capability to estimate the econometric models. Second, the GJR(1,1) models provide the best fitting, followed by the GARCH-M(1,1), GARCH(1,1), and log-GARCH(1,1) models. This study concludes that Excel’s Solver’s GRG non-linear can be recommended to the practitioners that do not have enough knowledge in the programming language in order to estimate the econometrics models. It also suggests to incorporate a risk premium in the return equation and an asymmetric effect in the variance equation. 
ENHANCING VOLATILITY MODELING WITH LOG-LINEAR REALIZED GARCH-CJ: EVIDENCE FROM THE TOKYO STOCK PRICE INDEX Nugroho, Didit Budi; Putri, Zefania Sasongko; Susanto, Bambang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0881-0894

Abstract

This study compares the Log-linear Realized GARCH (LRG) and its extension with Continuous and Jump components (LRG-CJ) in modeling the volatility of financial assets, using daily data from the Tokyo Stock Price Index (TOPIX) over 2004–2011. The urgency arises from the need for more accurate volatility models during turbulent periods such as the 2008 Global Financial Crisis and the 2011 Great East Japan Earthquake, where markets exhibit both smooth fluctuations and abrupt jumps. Methodologically, the LRG-CJ framework introduces a novel integration of continuous and jump decomposition into the LRG structure, offering an applied innovation to high-frequency volatility modeling. Realized Volatility (RV) was calculated from 1-, 5-, and 10-minute intraday data and decomposed into continuous and jump components. Parameter estimation employed the Adaptive Random Walk Metropolis (ARWM) within a Markov Chain Monte Carlo algorithm, while model performance was assessed using multiple information criteria and out-of-sample forecast evaluations. The empirical results reveal that incorporating continuous and jump components improves volatility modeling accuracy, forecasting, and Value-at-Risk estimation. However, these benefits are frequency-dependent: the LRG-CJ model shows superior in-sample fit for 1-minute RV but provides the strongest out-of-sample forecasting and risk prediction at lower frequencies (5- and 10-minute intervals). This highlights that while jumps are best identified at ultra-high frequencies, their predictive value is most effectively captured in slightly aggregated data. The originality of this study lies in being the first empirical application of LRG-CJ, demonstrating how continuous–jump decomposition interacts with the dual-equation structure of LRG, which has not been examined in TGARCH or APARCH contexts. Limitations include sensitivity to microstructure noise in very high-frequency data and computational challenges in parameter convergence. Overall, the findings underscore the novelty and practical importance of the LRG-CJ framework for risk management, offering actionable guidance for aligning volatility models with data frequency
Evaluating the Fitting Performance of AGARCH(1,1), NAGARCH(1,1), and VGARCH(1,1) Models Didit Budi Nugroho; Veny M. Ningtyas; Hanna A. Parhusip
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 9 No. 2 (2023)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

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Abstract

This study compares the performance of the GARCH(1,1), AGARCH(1,1), NAGARCH(1,1), and VGARCH(1,1) models fitted to real data. The observed real data are the USD exchange rate against IDR in the daily period from January 2010 to December 2017. To identify the superiority and evaluate the performance of those models in capturing the heavy-tailed and skewed character in exchange rate distribution, the return error is assumed to be the Normal, Skew Normal (SN), Skew Curved Normal (SCN), and Student-t distributions. The model's parameters are estimated using the GRG Non-Linear method in Excel Solver and the ARWM method in the MCMC scheme implemented in the Scilab program. Estimation results using Excel's Solver have similar values to the estimates obtained using MCMC, concluding that Excel's Solver has a good ability in estimating the model's parameters. Based on AIC values, this study concludes that the NAGARCH(1,1) model under Student-t distribution performs the best.
Co-Authors A. Setiawan A.A. Ketut Agung Cahyawan W Adi Setiawan Agus Priyono Alfagustina, Yumita Cristin ALOYSIUS JOAKIM FERNANDEZ Anggita M Kusumawati Arisandi, Nia Lestari Atyanta Nika Rukmasari Bambang Susanto Beni Utomo Daivi Wardani, Daivi Daniel Eliazar Latumaerissa Dini Kurniawati E. D. Saputri, E. D. Erlina Prihatnani Faldy Tita Hanafi, Fika Maula Hanna A. Parhusip Hanna Arini Parhusip Hariadi, Adrianus Herry Hariyanto, H Hindriyanto Dwi Purnomo Irfani, Nur Istiarsi Saptuti Sri Kawuryan Istiarsih Saputri Sri Kawuryan Kezia Natalia Putri Prasetia Kholil, Zaini Kurniawan, Johanes Dian Lea, Lea Lennox Larwuy Leopoldus Ricky Sasongko Leopoldus Ricky Sasongko Lydia Soepriyani Fallo Mince M. M. Rosely Nia Lestari Arisandi Obed Christian Dimitrio Pamungkas, Bintoro Ady Panjaitan, Lam P. Panjaitan, Lam Peter Prasetia, Kezia Natalia Putri Puspitasari, Agnes Dhika Putri, Zefania Sasongko Rahmawanto, Setya Budi Rebecca Rorimpandey Rorimpandey, Rebecca Rudhito, Andy Santosa, Petrus Priyo Sassongko, Leopoldus Ricky Sri Kawuryan, Istiarsi Saptuti Sri Suryasatriya Trihandaru Tundjung Mahatma Veny M Ningtyas Veny M. Ningtyas Wijaya, Melina Tito Wulandari, Nadya Putri Yulius Pratomo