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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi CommIT (Communication & Information Technology) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jambura Journal of Biomathematics (JJBM) Journal of Fundamental Mathematics and Applications (JFMA)
Susyanto, Nanang
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Unveiling SIR Model Parameters: Empirical Parameter Approach for Explicit Estimation and Confidence Interval Construction Susyanto, Nanang; Arcede, Jayrold P.
Jambura Journal of Biomathematics (JJBM) Volume 5, Issue 1: June 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v5i1.26287

Abstract

We propose a simple parameter estimation method for the Susceptible-Infectious-Recovered (SIR) model. This method offers explicit estimates of parameters using second-order numerical derivatives to construct empirical parameters. In addition, the method constructs confidence intervals, providing a robust assessment of parameter uncertainty. To validate the accuracy of our method, we applied it to simulated data, in order to demonstrate its effectiveness in accurately estimating the true model parameters. Furthermore, we applied this method to actual COVID-19 case data from the USA, Indonesia, and the Philippines. This application enables the estimation of parameters and reproductive numbers, along with their confidence intervals, thus underscoring the efficacy of our technique. Notably, the parameter estimates obtained through our approach successfully predicted the case numbers in all three countries, confirming its predictive reliability. Our method offers significant advantages in terms of simplicity and accuracy, making it an invaluable tool for epidemiological modeling and public health planning.
Program Evaluation and Review Technique (PERT) Analysis to Predict Completion Time and Project Risk Using Discrete Event System Simulation Method Yudistira, I Gusti Agung Anom; Nariswari, Rinda; Arifin, Samsul; Abdillah, Abdul Azis; Prasetyo, Puguh Wahyu; Susyanto, Nanang
CommIT (Communication and Information Technology) Journal Vol. 18 No. 1 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i1.8495

Abstract

The prediction of project completion time, which is important in project management, is only based on an estimate of three numbers, namely the fastest, slowest, and presumably time. The common practice of applying normal distribution through Monte Carlo simulation in Program Evaluation and Review Technique (PERT) research often fails to accurately represent project activity durations, leading to potentially biased project completion prediction. Based on these problems, a different method is proposed, namely, Discrete Event Simulation (DES). The research aims to evaluate the effectiveness of the simmer package in R in conducting PERT analysis. Specifically, there are three objectives in the research: 1) develop a simulation model to predict how long a project will take and find the critical path, 2) create an R script to simulate discrete events on a PERT network, and 3) explore the simulation output using the simmer package in the form of summary statistics and estimation of project risk. Then, a library research with a descriptive and exploratory method is used for data collection. The hypothetical network is used to obtain the numerical results, which provide the predicted value of the project completion, the critical path, and the risk level. Simulation, including 100 replications, results in a predicted project completion time and a standard deviation of 20.7 and 2.2 weeks, respectively. The DES method has been proven highly effective in predicting the completion time of a project described by the PERT network. In addition, it offers increased flexibility.
A New Path to Accurate Risk Adjustment: Applying CreVaR for Better Financial Reporting under IFRS 17 Cherlyn, Cherlyn; Susyanto, Nanang
Journal of Fundamental Mathematics and Applications (JFMA) Vol 7, No 2 (2024)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jfma.v7i2.24561

Abstract

IFRS 17 is an international financial reporting standard that emphasizes the principles of consistency, transparency, and comparability. It divides reserve recording into Present Value of Future Cash Flows (PVFCF), Risk Adjustment (RA), and Contractual Service Margin (CSM). As IFRS 17 does not prescribe a specific calculation method, companies have the flexibility to define their own risk assessment approaches. Value-at-Risk (VaR) is widely used due to its simplicity and ease of application. However, its limitations in handling large datasets can lead to reduced accuracy. Moreover, variations in methods across companies can compromise the comparability of financial standards. This study proposes an enhanced VaR calculation based on credibility theory—Credible Value-at-Risk (CreVaR)—to improve accuracy and promote greater consistency across corporate entities. The Diebold-Mariano (DM) test demonstrates that CreVaR provides a more accurate estimation of RA without overestimation, making it a suitable alternative for calculating RA under IFRS 17
A Reinforcement Learning Based Decision-Support System for Mitigate Strategies During COVID-19: A Systematic Review Rifanti, Utti Marina; Aryati, Lina; Susyanto, Nanang; Susanto, Hadi
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 1: March 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i1.30513

Abstract

The past threat of the COVID-19 pandemic has challenged policymakers to develop effective decision-support systems. Reinforcement learning (RL), a branch of artificial intelligence, has emerged as a promising approach to designing such systems. This systematic review analyzes 20 selected studies published between 2020 and 2024 that apply RL as a decision-making tool for COVID-19 mitigation, focusing on environment models, algorithms, state representation, action design, reward functions, and challenges. Our findings reveal that Q-learning is the most frequently used algorithm, with most implementations relying on SEIR-based models and real-world COVID-19 epidemiological data. Policy interventions, particularly lockdowns, are commonly modeled as actions, while reward functions are health-oriented, economic, or hybrid, with an increasing trend toward multi-objective designs. Despite these advancements, key limitations persist, including data uncertainty, computational complexity, ethical concerns, and the gap between simulated performance and real-world feasibility. This review further identifies a research opportunity to integrate epidemic model formulations with explicit control inputs into RL frameworks, potentially enhancing learning efficiency and bridging the gap between simulation and practice for future pandemic response systems.
PERFORMANCE LOSS QUANTIFICATION IN KERNEL DENSITY ESTIMATION FOR ACTUARIAL AND FINANCIAL ANALYSIS Untsa, Shafira Fauzia; Susyanto, Nanang; Qoiyyimi, Danang Teguh; Ertiningsih, Dwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2029-2038

Abstract

Accurately estimating aggregate loss distributions is critical in actuarial and financial risk assessment, as it underpins effective risk analysis and the development of mitigation strategies. However, incorrect parametric assumptions can lead to biased risk estimates and underestimated losses. Non-parametric methods, such as Kernel Density Estimation (KDE), offer a flexible alternative by generating smooth empirical probability density functions (PDFs) directly from sample data without assuming a specific distributional form. This study examines the impact of dependence structures on risk measures by applying KDE with a Gaussian kernel to estimate aggregate loss distributions. To quantify the effects of ignoring dependence, we introduce the concept of performance loss, focusing on variance, Value at Risk (VaR), and Tail Value at Risk (TVaR). The results show that performance loss increases with the correlation coefficient, indicating that higher dependency leads to greater underestimation of risk. Additionally, higher confidence levels amplify performance loss for VaR and TVaR, underscoring the sensitivity of these measures to tail behavior. These findings highlight the importance of incorporating dependence structures in risk modeling to avoid misleading evaluations. The implications are particularly relevant for disaster risk management in Central Asia, where overlooking interdependencies in seismic losses could result in inadequate financial and actuarial strategies.
Improving the Accuracy of Discrepancies in Farmers' Purchasing and Selling Index Prediction by Incorporating Weather Factors Yulianti, Silvina Rosita; Effendie, Adhitya Ronnie; Susyanto, Nanang
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22584

Abstract

One measure that can be used to see the level of farmer welfare is the farmer exchange rate (NTP), which is a comparative calculation between the price index received by farmers (IJ) and the price index paid by farmers (IB), expressed as a percentage. In reality, NTP cannot explain the actual welfare situation of farmers because the ratio value has the potential to produce biased values. Another alternative that can be used to look at farmer welfare with less potential bias is to look at the difference between the sales index and the farmer purchasing index (ID). ID data forecasting can be a reference for developing and optimizing things that need to be improved in the agricultural sector. Despite the fact that a number of external factors, such as variations in the weather throughout the year, had a significant impact on the ID value, previous research used the ARIMA model to forecast without taking exogenous factors into account. Therefore, the goal of this research is to identify the optimal ARIMAX regression model for achieving accurate forecasting results with minimal error values. This research was carried out with limitations using data from the Central Statistics Agency and the Meteorological, Climatological, and Geophysical Agency in Central Java from 2008 to 2023. The first method in this research is to prepare the data, which involved collecting secondary data such as IJ and IB along with climate data such as rainfall, duration of sunlight, air pressure, wind speed, and rice prices. Next, calculate the difference between IJ and IB to determine the ID value. Then, verify the ID data's stationarity and perform AR and MA calculations. After determining the AR and MA values, construct an ARIMAX model that incorporates external factors, search for the optimal model, and utilize the optimal model to make future predictions. The results show that the accuracy of the ARIMAX model (1,1,0) has a better value than the accuracy of the ARIMA model (1,1,0), and the results obtained in this study are better than previous studies. The authors hope that the findings of this research will serve as a benchmark for the forecasting analysis of time series data in the agricultural sector, providing the local government with a foundation for policy decisions.
Forecasting Indonesia’s Composite Stock Price Index with Semiparametric Cubic and Local Gaussian Polynomials Dewi, Mita Kornilia; Susyanto, Nanang
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.36180

Abstract

The Composite Stock Price Index (CSPI) serves as a crucial indicator for assessing the performance of the Indonesian capital market, reflecting both economic conditions and investor confidence. Its movements are influenced by macroeconomic factors such as exchange rates, inflation, interest rates, and commodity prices, including oil and gold. Parametric models often fail to capture nonlinear patterns, whereas nonparametric approaches lack efficiency and interpretability. To address this gap, this study develops a semiparametric regression model that integrates a cubic polynomial for parametric effects with local polynomial estimators using Gaussian kernels for nonparametric effects. The results show that the semiparametric model is effective, yielding an MSE of 0.569747, a MAPE of 8.60\%, and an $R^2$ of 85\%. This confirms its ability to capture nonlinear dynamics in the stock market. Moreover, the model provides accurate forecasting and practical insights for investors in portfolio strategies as well as for policymakers in managing financial market stability.
Co-Authors Abdillah, Abdul Azis Adhitya Ronnie Effendie, Adhitya Ronnie Arcede, Jayrold P. Cherlyn, Cherlyn Dewi, Mita Kornilia Dwi Ertiningsih Hadi Susanto I Gusti Agung Anom Yudistira Lina Aryati Puguh Wahyu Prasetyo, Puguh Qoiyyimi, Danang Teguh Rinda Nariswari Samsul Arifin Untsa, Shafira Fauzia Utti Marina Rifanti Yulianti, Silvina Rosita