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All Journal dCartesian: Jurnal Matematika dan Aplikasi Media Statistika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Kubik Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Ekonomi dan Studi Pembangunan (Journal of Economics and Development Studies) EMITTER International Journal of Engineering Technology Jurnal Mercumatika : Jurnal Penelitian Matematika dan Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Abdi Insani Indonesian Journal of Data and Science Jurnal Sains dan Edukasi Sains SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) Jurnal Pendidikan JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat ADPEBI International Journal of Business and Social Science Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Akuntansi dan Keuangan Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Jurnal Pendidikan Indonesia (Japendi) Jurnal Kedokteran STM (Sains dan Teknologi Medik) Multifinance Adpebi International Journal of Multidisciplinary Sciences d'Cartesian: Jurnal Matematika dan Aplikasi SJME (Supremum Journal of Mathematics Education)
Bambang Susanto
Universitas Kristen Satya Wacana
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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. 
Pembelajaran Vektor Untuk Klasifikasi Data Pada Bidang Parhusip, Hanna Arini; Susanto, Bambang; Linawati, Lilik; Trihandaru, Suryasatriya; Sardjono, Yohanes
SJME (Supremum Journal of Mathematics Education) Vol 4 No 2 (2020): Supremum Journal of Mahematics Education
Publisher : Fakultas Keguruan dan Ilmu Pendidikan Universitas Singaperbangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/sjme.v4i2.3515

Abstract

Tujuan penelitian ini adalah penyusunan hyperplane untukmemisahkan data yang mempunyai 2 kelas dan bersifat linear padabidang datar sebagai pembelajaran vektor untuk klasifikasi data.Adapun metode yang digunakan adalah pre-Support Vector Machine(SVM). Metode ini mencari garis (hyperplane) terbaik yangmemisahkan data dan memberi ruang antar 2 kelas data dimana ruangpemisah tersebut tidak boleh memuat data serta ruang tersebutmerupakan margin maksimal. Langkah awal adalah menduga garispemisah (hyperplane) awal melalui titik O. Dengan mengambil salahsatu titik data yang menjadi titik referensi, disusun vektor dari Oterhadap titik referensi dan garis melalui titik referensi sebagai bataspertama margin. Kemudian dibentuk vektor arah dari titik O yangtegak lulus terhadap garis awal (hyperplane). Selanjutnya vektorproyeksi dibentuk dari titik referensi terhadap vektor arah sehinggavektor arah dan vektor proyeksi berhimpit (searah). Penyusunanmargin diperoleh dengan menyusun garis yang pararel terhadap garisawal sebagai hyperplane serta berjarak 2 kali dengan panjang vektorproyeksi tersebut. Hyperplane terbaik diperoleh secara manual denganmengatur batas kedua dari margin yang diperoleh dengan menggambargaris melalui suatu titik data pada kelas ke-2 dengan jarak terdekat danpararel terhadap garis yang melalui titik referensi dari data kelas ke-1.
Human Capital Decision Intelligence (HCDI) architecture in microbiology laboratory based on machine learning and operations research models Trihandaru, Suryasatriya; Susetyo, Yosia Adi; Parhusip, Hanna Arini; Susanto, Bambang
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.1676

Abstract

The Human Capital Decision Intelligence (HDCI) system integrates human-computer interaction in a microbiology laboratory that uses machine learning and operational research to classify new tasks and then recommend assignments to each person. The models evaluated in building this system are Support Vector Machine, Gaussian Naive Bayes, Multinomial Logistic Regression, and Artificial Neural Network. The results of the research show that the ANN model is the most consistent and reliable across various training ratios, as indicated by the model's goodness parameters. The selected ANN model is combined with a linear programming approach to optimize workload distribution. The integrated system successfully manages new job scenarios and recommends staff based on competencies and availability. It also ensures assignments do not exceed maximum workload limits and finds alternatives when key staff are unavailable. The implementation of the HDCI system has a positive impact on various factors, including the fair distribution of tasks, enhanced staff performance monitoring, and significantly improved operational efficiency and human resource management in the microbiology laboratory. The system is designed to be easy to use and support collaboration between laboratory staff and computational models. The system is not only advanced in supporting personnel management decision-making, but it can also demonstrate how artificial intelligence and operations research systems can be combined to address the needs of the microbiology laboratory environment.
Comparison of k-Nearest Neighbor and Naive Bayes Methods for SNP Data Classification Denny Indrajaya; Adi Setiawan; Bambang Susanto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1758

Abstract

In an accident, sometimes the identity of a person who has an accident is hard to know, so it is necessary to use biological data such as Single Nucleotide Polymorphism (SNP) data to identify the person's origin. This research aims to compare the accuracy and the F1 score of the k-Nearest Neighbor method and the Naive Bayes method in classifying SNP data from 120 people who divide into groups, namely European (CEU) and Yoruba (YRI). Determination of the best method based on the average value of accuracy and the average value of F1 score from 1000 iterations with various percentage distributions of training datasets and testing datasets. In this research, the selection of SNP locations for the classification process was carried out by correlation analysis. The average accuracy obtained for the k-Nearest Neighbor method with the value of k=31 is 98.38% where the average F1 score is 98.39% while the Naive Bayes method obtained the average accuracy of 96.74% and the average F1 score of 96.63%. In this case, the k-Nearest Neighbor method is better than the Naive Bayes method in classifying SNP data to determine the origin of a person's ancestor tends to be from CEU or YRI.
Selection Method of Modulation Index and Frequency ratio for Getting the SPWM Minimum Harmonic of Single Phase Inverter Kristi, Ant. Ardath; Susanto, Bambang; Risdiyanto, Agus; Junaedi, Agus; Muqorobin, Anwar; Rachman, Noviadi Arief; Santosa, Harjono Priyo
EMITTER International Journal of Engineering Technology Vol 9 No 1 (2021)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v9i1.587

Abstract

Harmonic content is an important parameter in relation to the power generated by inverter. In power conversion technology of inverter, sinusoidal pulse width modulation (SPWM) is the most popular used by many researchers. The advantages of SPWM inverter operation as a conversion technique compared to other inverter types can be seen from the low harmonic distortion in the output voltage of inverter. Therefore, the SPWM signal generation process becomes a determining factor for the performance of the overall system. This paper present the method for selecting the modulation index (ma) and frequency ratio (mf) using Cubic Spline Interpolation to get minimum harmonic of SPWM inverter that generated. Both parameters controlled with varied values digitally using microcontroller to generate SPWM, then the output of inverter with and without LC filter was investigated. The results show that the use of Cubic Spline Interpolation method in the selection of ma and mf precisely managed to produce SPWM inverter with minimum harmonic content. At the inverter output, the use of LC filter is not only useful for converting SPWM signals to sinusoidal waveforms but can also reduce harmonic content significantly less than 3 %.
Modeling and Forecasting Volatility through EGARCH-X and EGARCH-CJ Models Didit Budi Nugroho; Benita Dwitya Putri; Bambang Susanto
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.26532

Abstract

This study compares the performance of EGARCH-X and EGARCH-CJ models in forecasting financial market volatility using daily TOPIX data (2004-2011). Model parameters were estimated using an efficient Bayesian MCMC framework. The results indicate that the EGARCH-CJ model, which decomposes volatility into continuous and jump components, provides a superior in-sample fit. More importantly, in out-of-sample forecasting, the EGARCH-CJ model demonstrates significantly better accuracy for medium- and long-term horizons (e.g., MSE reductions up to 30% at the 5-day horizon, with significant Diebold-Mariano statistics). In contrast, the standard EGARCH model remains more effective for short-term forecasts. These findings underscore the importance of explicitly modeling jump dynamics for medium-term risk management in the Japanese stock market, offering valuable insights for financial modelers and risk managers.
One to many (new scheme for symmetric cryptography) Alz Danny Wowor; Bambang Susanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 4: August 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i4.24789

Abstract

Symmetric cryptography will always produce the same ciphertext if the plaintext and the given key are the same repeatedly. This condition will make it easier for cryptanalysts to perform cryptanalysis. This research introduces a one-to-many cryptography scheme, which can produce different ciphertexts even if the input given is the same repeatedly. The one-to-many encryption scheme can produce several ciphertexts with differences of up to 50%. The avalanche effect test obtained an average of 52.20%, better than modern cryptography Blowfish by 25.46% and 6% better than advanced encryption standard (AES). One-to-many can produce different n-ciphertexts, which will certainly make it more difficult for cryptanalysts to perform cryptanalysis and require n-times longer to break than other symmetric cryptography.
ENHANCING VOLATILITY MODELING WITH LOG-LINEAR REALIZED GARCH-CJ: EVIDENCE FROM THE TOKYO STOCK PRICE INDEX Didit Budi Nugroho; Zefania Sasongko Putri; Bambang Susanto
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
Pendampingan Olimpiade Matematika Berbasis TDS untuk Meningkatkan Kesiapan Siswa SMP Salatiga Menghadapi OSN 2026 Fika Widya Pratama; Kriswandani Kriswandani; Bambang Susanto; Helti Lygia Mampouw; Rahayu Ravita; Eka Pradita Putri Indriani; Tri Nova Hasti Yunianta
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 5 (2026): September 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i5.1576

Abstract

OSN Matematika menuntut kemampuan pemecahan masalah, penalaran, dan berpikir tingkat tinggi, sementara banyak siswa SMP memiliki keterbatasan pengalaman dan pembinaan terstruktur. Kegiatan pengabdian kepada masyarakat dalam bentuk pendampingan berbasis Theory of Didactic Situations (TDS) selama 12 pertemuan (masing-masing pertemuan 2 JP) untuk siswa SMP di Kota Salatiga (27 siswa) bertujuan meningkatkan kesiapan siswa SMP dalam menghadapi OSN Matematika Tahun 2026. Metode yang digunakan meliputi pemberian materi, penggunaan lembar kerja siswa berbasis TDS, diskusi kelompok, latihan soal olimpiade, serta evaluasi melalui pretest dan posttest. Hasil kegiatan menunjukkan bahwa rata-rata skor peserta meningkat dari 20,83 menjadi 26,76 atau mengalami peningkatan sebesar 28,47%. Hasil uji Wilcoxon Signed-Rank Test menunjukkan nilai signifikansi sebesar 0,013 (< 0,05), yang mengindikasikan adanya perbedaan yang signifikan antara kemampuan peserta sebelum dan sesudah pendampingan. Hal ini menunjukkan bahwa pendampingan berbasis TDS efektif dalam meningkatkan kemampuan pemecahan masalah matematika dan kesiapan siswa menghadapi OSN serta berpotensi untuk dikembangkan sebagai model pembinaan berkelanjutan. 
Co-Authors Adi Setiawan Adrianus Herry Heriadi Agus Priyono Agus Risdiyanto Alfagustina, Yumita Cristin Alfida Tegar Nurani Alicia Anggelia Lumbantoruan Alz Danny Wowor Anwar Muqorobin Asido Saragih Awik Hidayati Benita Dwitya Putri Carolina Febe Ronicha Putri Denny Indrajaya Didit B Nugroho Didit Budi Nugroho Didit Budi Nugroho Dini Kurniawati Dudi Rudianto Dwi Tristianto Egidius Saut Poltak Situmorang Eka Pradita Putri Indriani Elsa Septyana Eva Rachmawati Faldy Tita Fika Widya Pratama Haay, Happy Alyzhya Hanna Arini Parhusip Helti Lygia Mampouw Herdiyanti, Gita Johanes Dian Kurniawan Junaedi, Agus Kezia Natalia Putri Prasetia Khalingga, M Ariq Khalingga, Muhammad Ariq Kholil, Zaini Kristi, Ant. Ardath Kriswandani Laurentia Nindya Sari Prameswara Lenox Larwuy Leopoldus Ricky Sasongko Lestari, Dina Aulia Lilik Linawati Maria Anita masipupu, Frangky Aristiadi Mince M. M. Rosely Modjo, Marchella Ellena Noviadi Arief Rachman Nuryani, Dwi Panjaitan, Lam Peter Petrus Priyo Santosa Prasetia, Kezia Natalia Putri PURWANTI PURWANTI Rahayu Ravita Ratnawati, Aryanti Rebecca Rorimpandey Rima Dwijayanty Rinadi, Geraldus Anggoro Rorimpandey, Rebecca Saepudin Saepudin Santosa, Harjono Priyo Saragah R Pratama SARI, EMMA NOVITA Setiawan, Audita Sukadwilinda Suroto, Agung Suryasatriya Trihandaru Susetyo, Yosia Adi Syafi’i, Syafi’i Tanujaya, Lukhia Britanthia Christina Tri Nova Hasti Yunianta Tri Pujadi Susilo Tundjung Mahatma Vania Beatrice Liwandouw Visher Laja Jaja wahyu ngestisari Wulandari, Nadya Putri Yohanes Sardjono Yohanes Sardjono, Yohanes Zefania Sasongko Putri