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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
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Volatilitas Return Indeks Saham Internasional Berdasarkan Model GJR-GARCH(1,1) Panjaitan, Lam P.; Nugroho, Didit Budi; Sassongko, Leopoldus Ricky
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2019: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (603.636 KB)

Abstract

Studi ini memberikan perbandingan kinerja antara model GARCH(1,1) dan model GJR-GARCH(1,1) yang mengasumsikan return error berdistribusi normal. Perbandingan tersebut berdasarkan pada data simulasi dan data riil. Data simulai merupakan data returns yang dibangkitkan berdasarkan model GJR-GARCH(1,1) sebanyak 1000 kali, sedangkan data riil yang indeks saham digunakan dalam studi ini adalah Dow Jones Industrial Average (DJIA), Standard and Poors 500 (S&P 500), dan S&P CNX Nifty untuk periode harian dari Januari 2000 sampai Desember 2017. Studi ini juga menguji kemampuan Solver Excel dalam mengestimasi kedua model. Studi ini diawali dengan mengestimasi model yang diperhatikan menggunakan metode GRG Non-Linier di Solver Excel dan menemukan bahwa Solver Excel merupakan alat estimasi yang handal meskipun pada kasus tertentu menghasilkan estimasi yang tidak sesuai dengan kendala model. Pada hasil data simulasi model GJR-GARCH(1,1) memberikan pencocokan yang lebih baik dari model GARCH(1,1) dan pada hasil data riil menunjukan bahwa model GJR-GARCH(1,1) menyediakan pencocokan lebih baik daripada model GARCH(1,1).
Pemodelan Volatilitas Menggunakan Garch(1,1) dengan Volatilitas Lag-1 Ditransformasi Box–Cox Rorimpandey, Rebecca; Nugroho, Didit Budi; Susanto, Bambang
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2019: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

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Abstract

Studi ini mengusulkan klas baru dari model GARCH dengan mengaplikasikan keluarga transformasi Box–Cox ke volatilitas lag-1. Model GARCH telah banyak digunakan untuk mendikripsikan tingkah laku volatilitas suatu runtun waktu keuangan, terutama pada kurs mata uang. Tingkah laku dari volatilitas return dipelajari berdasarkan model yang mengasumsikan distribusi normal untuk inovasi. Model diestimasi menggunakan alat bantu Solver Excel dan Matlab. Analisis empiris didasarkan pada data simulasi dan data kurs beli EUR, JPY, dan USD terhadap IDR atas periode harian dari 2010 sampai 2017. Dalam kasus data simulasi dan data riil, ditemukan bahwa Solver Excel memiliki kelemahan. Hasil empiris untuk data simulasi menunjukkan bahwa model BC(1)-GARCH(1,1) bisa dikatakan tidak lebih baik dari model GARCH(1,1). Sedangkan untuk kasus data riil dengan inovasi berdistribusi normal menunjukkan bahwa model BC(1)-GARCH(1,1) mengungguli model GARCH pada data kurs beli USD terhadap IDR.
Perbandingan Empiris antara Model Log-Garch dan Garch Kholil, Zaini; Nugroho, Didit Budi; Susanto, Bambang
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2019: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

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Abstract

Studi ini berfokus pada studi empiris tentang perbandingan antar model Log-GARCH(1,1) dan model GARCH(1,1). Kedua model diaplikasikan pada data simulasi dan data riil, data rill yang digunakan berjumlah tiga jenis data yaitu indeks harga saham Dow Jones Industrial Average (DJIA), Standard and Poor’s (S&P 500), dan S&P CNX Nifty pada periode harian dari bulan Januari tahun 2000 sampai bulan Desember tahun 2017. Model diasumsikan mempunyai inovasi return dengan berdistribusi normal. Solver Excel digunakan untuk mengestimasi model Log-GARCH(1,1) dan model GARCH(1,1) dan diselidiki kemampuannya. Secara keseluruhan, studi ini menunjukkan bahwa Solver pada Microsoft Excel mampu mengestimasi parameter-parameter model dengan cukup akurat. Dalam kasus data simulasi, hasil dari perhitungan nilai estimasi total log-likelihood mengindikasikan bahwa model Log-GARCH(1,1) berpotensi mencocokkan lebih baik dibandingkan dengn model GARCH(1,1). Sementara itu, dalam kasus data riil, hasil perhitungan nilai estimasi pada model GARCH(1,1) lebih cocok digunakan untuk ketiga data return harian indeks harga saham dibandingkan dengan model Log-GARCH(1,1).
Upaya Meningkatkan Hasil Belajar Bangun Ruang Siswa SMP melalui Model Project Based Learning dengan Metode Learning Station Rotation Pamungkas, Bintoro Ady; Nugroho, Didit Budi; Prihatnani, Erlina; Irfani, Nur
Imajiner: Jurnal Matematika dan Pendidikan Matematika Vol 7, No 1 (2025): Imajiner: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/imajiner.v7i1.20433

Abstract

Penelitian ini bertujuan guna meningkatkan hasil belajar matematika siswa kelas IX-D SMP Negeri 1 Salatiga materi bangun ruang dengan mengimplementasikan model Project-Based Learning (PjBL) diintegrasikan dengan metode Learning Station Rotation. Metode penelitian yang digunakan adalah Penelitian Tindakan Kelas (PTK) dengan pelaksanaan dua siklus. Hasil observasi awal menunjukkan rendahnya prestasi belajar siswa dengan nilai rerata asesmen diagnostik sebesar 59,1. Pada siklus pertama, penerapan PjBL dengan metode demonstrasi menghasilkan rata-rata nilai belajar sebesar 71,58, namun belum mencapai kriteria ketuntasan minimal (KKM) 75. Dalam siklus kedua, dengan penerapan Learning Station Rotation, rata-rata evaluasi belajar siswa meningkat signifikan menjadi 82,89. Temuan ini menunjukkan bahwa integrasi PjBL dan Learning Station Rotation efektif guna meningkatkan hasil belajar siswa, serta memberikan kontribusi bagi pengembangan metode pembelajaran yang inovatif dan kreatif. Penelitian ini diharapkan mampu menjadi acuan untuk pendidik dalam merancang strategi pembelajaran yang efektif di kelas.
Using an LSTM Neural Network to Improve Symmetric and Asymmetric GARCH Volatility Forecast Rahmawanto, Setya Budi; Nugroho, Didit Budi; Trihandaru, Suryasatriya
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24614

Abstract

Volatility forecasting is crucial for financial risk management, yet traditional models like GARCH struggle with nonlinearities and asymmetric effects. This study leverages Long Short-Term Memory (LSTM) neural networks to enhance symmetric and asymmetric GARCH models, addressing these limitations. By integrating LSTM with GARCH, GARCH-X, and Realized GARCH frameworks, we propose hybrid models (Baseline and Extended versions) to improve forecasting accuracy. Using daily data from FTSE 100, Nikkei 225, and S&P 500 indices (2000–2020), we compared hybrid models against traditional models. Results show that the Extended LSTM hybrid model outperforms both traditional GARCH-type models and the Baseline LSTM, capturing complex volatility patterns more effectively. The Extended model’s architecture, featuring ReLU, GRU, and dropout layers, mitigates over-smoothing and enhances responsiveness to market fluctuations. This research demonstrates LSTM’s potential to refine volatility forecasting, offering valuable insights for investors and risk managers.
Learning Algorithms of SVR, DTR, RFR, and XGBoost (Case Study: Predictive Maintenance of Fuel Consumption) Parhusip, Hanna Arini; Lea, Lea; Trihandaru, Suryasatriya; Nugroho, Didit Budi; Santosa, Petrus Priyo; Hariadi, Adrianus Herry
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.85657

Abstract

The most complex aspect of predictive maintenance (PdM) for heavy vehicles is accurately forecasting fuel consumption as it is both critical and challenging to achieve optimal efficiency while minimizing expenses. Overfitting and failure to capture the existing data's linear relationships seem to remain the most persistent issues with traditional methods. In order to achieve this, the following techniques were analyzed to choose the best fuel consumption forecaster: Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFT), and XGBoost. The models were implemented and their performance measured using Mean Squared Error (MSE). The analysis revealed that SVR surpassed the others with a linear kernel (C=10) achieving the lowest MSE rates of 0.26, while DTR, RFR, and XGBoost earned significantly higher 3.375, 2.857, and 3.857 (MSEs). The other models lagged behind SVR because SVR was more effective in capturing linear relations and managing overfitting, a dominating issue with decision-tree based models. This points out another important aspect of predictive maintenance (PdM) : the appropriate machine learning technique plays a very important role in accurately predicting fuel consumption of heavy trucks, which improves precision and fuel efficiency.
Modern Ethnomathematics Mainstreaming through Mathematics Entrepreneurship Using Mathematical Ornaments Parhusip, Hanna Arini; Purnomo, Hindriyanto Dwi; Nugroho, Didit Budi; Sri Kawuryan, Istiarsi Saptuti Sri
International Journal on Emerging Mathematics Education IJEME, Vol. 5 No. 2, September 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijeme.v5i2.15118

Abstract

Modern ethnomathematics is proposed in this article by introducing curves and surfaces to objects based on commonly used mathematics. There are 2 types of objects, batik and ornament. The object is known as Batima, which means a mathematical motif made in a batik stamp. The same design can be used to design ornaments, souvenirs, accessories or other household items such as glasses, t-shirts and other materials. The formation of ethnomathematics is driven by entrepreneurial activities. The method starts with the expansion of the circular and spherical equations based on the variation of the power form which was originally 2 in the equation to be valued at random (say p). The other used equations are parametric equations, especially the hypocycloid which is extended to both curves and surfaces with spherical coordinates. In addition, derivative operators can be applied. Product manufacturing is carried out by at least 10 household businesses around Salatiga and Jogjakarta and its surroundings. In order to sustain the mainstreaming of modern ethnomathematics, entrepreneurial activities are carried out with existing materials through exhibitions and competitions that are followed. Likewise, the use of social media and marketplaces are explored to mainstream the modern ethnomathematics into society.
Learning geometry through surface creation from the hypocycloid curves expansion with derivative operators for ornaments Parhusip, Hanna Arini; Purnomo, Hindriyanto Dwi; Nugroho, Didit Budi; Kawuryan, Istiarsi Saptuti Sri
Desimal: Jurnal Matematika Vol. 4 No. 1 (2021): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v4i1.7385

Abstract

Geometry is one of the particular problems for students. Therefore, several methods have been developed to attract students to learn geometry. For undergraduate students, learning geometry through surface visualization is introduced. One topic is studying parametric curves called the hypocycloid curve. This paper presents the generalization of the hypocycloid curve. The curve is known in calculus and usually is not studied further. Therefore, the research's novelty is introducing the spherical coordinate to the equation to obtain new surfaces. Initially, two parameters are indicating the radius of 2 circles governing the curves in the hypocycloid equations. The generalization idea here means that the physical meaning of parameters is not considered allowing any real numbers, including negative values. Hence, many new curves are observed infinitely. After implementing the spherical coordinates to the equations and varying the parameters, various surfaces had been obtained. Additionally, the differential operator was also implemented to have several other new curves and surfaces. The obtained surfaces are useful for learning by creating ornaments. Some examples of ornaments are presented in this paper.
Improvement of Real-GJR Model using Jump Variables on High Frequency Data Nugroho, Didit Budi; Wulandari, Nadya Putri; Alfagustina, Yumita Cristin; Parhusip, Hanna Arini; Tita, Faldy; Susanto, Bambang
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.24294

Abstract

Volatility is a key indicator in assessing risk when making investment decisions. In the world of financial markets, volatility reflects the degree to which the value of a financial asset fluctuates over a given period. The most common way to measure the future loss potential of an investment is through volatility. Focusing on the Realized GJR (RealGJR) volatility model, which consists of return, conditional volatility, and measurement equations, this study proposes the RealGJR-CJ model developed by decomposing the exogenous variable in the volatility equation of RealGJR into continuous C and discontinuous (jump) J variables. The decomposition of exogenous variables makes the RealGJR-CJ model follow realistic financial markets, where the asset volatility is a continuous process with some jump components. As an empirical illustration, the models are applied to an index in the Japanese stock market, namely Tokyo Stock Price Index, covering from January 2004 to December 2011. The observed exogenous variable in the volatility equation of RealGJR models is Realized Volatility (RV), which is calculated using intraday data with time intervals of 1 and 5 minutes. Adaptive Random Walk Metropolis method was employed in Markov Chain Monte Carlo algorithm to estimate the model parameters by updating the parameters during sampling based on previous samples from the chain. From the results of running the MCMC algorithm 20 times, the mean of the information criteria of competing models is significantly different based on standard deviation and the result suggests that the model with continuous and jump variables can improve the model without jump. The best fit model is provided by RealGJR-CJ with the adoption of 1-minute RV data. 
WORKSHOP PERSIAPAN PEMBELAJARAN MATEMATIKA DENGAN BANTUAN PAKET PROGRAM KOMPUTER (GEOGEBRA/R) UNTUK MGMP MATEMATIKA SMA KABUPATEN SEMARANG JAWA TENGAH Setiawan, Adi; Parhusip, Hanna Arini; Nugroho, Didit Budi; Sasongko, Leopoldus Ricky; Rudhito, Andy; Utomo, Beni; Fernandez, Aloysius Joakim
Jurnal Abdi Insani Vol 12 No 2 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i2.2046

Abstract

The world is heading towards the era of Society 5.0, which requires learning Mathematics as much as possible to be easy to understand and interesting for students. Technology-based visualization, such as the use of Geogebra, can help students understand Mathematics formulas better. Therefore, teachers who are members of the High School Mathematics MGMP need to have insight into the importance of technology-based learning as a strategy to improve teaching quality. This activity aims to open teachers' insights into visualization and technology-based Mathematics learning, and help them develop creative learning modules and tools. The activity method includes four meetings consisting of webinars and workshops. The first webinar contains an introduction to the importance of visualization-based learning; the second webinar provides training in using Geogebra/R onsite or hybrid; the third webinar trains the creation of learning modules; and the fourth webinar presents the theory of writing scientific papers and using Mendeley. The results show that teachers are able to make creative and interesting lesson plans and learning modules using Geogebra/R, and motivate students to learn Mathematics independently or in groups. Some of the modules produced have been tested at school, although none of the participants have succeeded in making papers ready for publication. This activity succeeded in improving the ability of teachers to utilize technology for learning Mathematics.
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