Results in Mathematical Modeling
type: channel
description: Results in Mathematical Modeling is an international peer-reviewed journal dedicated to the advancement of theoretical, computational, and applied mathematical modeling, including its development in educational contexts. The journal publishes high-quality original research articles that develop, analyze, validate, and implement mathematical models to address real-world and educational problems across science, engineering, industry, environment, defense systems, economics, and mathematics education. The journal emphasizes rigor in formulation, analytical depth, computational validation, and interdisciplinary integration. Scope The journal welcomes contributions in, but not limited to, the following areas: 1. Applied Mathematics and Modeling Theory - Ordinary and partial differential equations - Fractional and integro-differential equations - Dynamical systems and stability theory - Control systems and system identification - Linear, nonlinear, and multi-objective optimization - Variational analysis - Stochastic processes and probabilistic modeling 2. Computational and Numerical Modeling - Numerical methods for ODEs and PDEs - Finite difference, finite element, and spectral methods - Scientific computing and algorithm development - Computational simulation and validation - High-performance computing in modeling - Data-driven and hybrid mathematical models 3. Mathematical Modeling in Science and Engineering - Physical and engineering systems - Environmental and climate modeling - Energy systems - Structural and material modeling - Biological and epidemiological systems - Defense and strategic systems modeling 4. Industrial, Economic, and Operational Modeling - Operations research - Logistics and supply chain modeling - Financial mathematics - Risk and decision analysis - Game theory and strategic interaction - Policy modeling 5. Mathematical Learning Models and Mathematics Education The journal welcomes research that integrates mathematical modeling approaches within m…
publisher: Indonesian Scholar Society
language: en-US
publication name: Results in Mathematical Modeling
issn: 3163-8694
items: Stock price forecasting under post-pandemic and normal market conditions using the generalized wiener process
items: Optimization of fiber optic backbone networks using minimum spanning tree based on Kruskal and prim algorithms
items: Optimizing bank teller service efficiency using the M/M/s queueing model and server scenario simulation
items: Forecasting bitcoin prices using the ARIMA–GARCH model: A volatility-based time series approach
items: Short-term prediction of Indonesian gold prices using a weighted Markov chain approach

Stock price forecasting under post-pandemic and normal market conditions using the generalized wiener process
type: item
description: The Covid-19 pandemic significantly altered stock market behavior and increased public participation in short-term trading and investment activities. However, limited analytical knowledge among new investors may increase the risk of inaccurate trading decisions, particularly during volatile market periods. This study evaluates the forecasting performance of the generalized Wiener process in predicting stock prices under two different market regimes: the post-pandemic New Normal period in 2021 and normal market conditions in 2023. Daily adjusted closing prices of two Indonesian mining companies, PT Aneka Tambang Tbk (ANTM) and PT Timah Tbk (TINS), were used as case studies. Historical price data from January to December 2020 were used to forecast stock prices for January–February 2021, while data from January to December 2022 were used to forecast January–February 2023 prices. Two forecasting schemes were compared: forecasts based on real-time reference data and forecasts based on previous predicted values. Forecast accuracy was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the generalized Wiener process produced more accurate forecasts under normal market conditions than during the post-pandemic period. Real-time reference data consistently generated lower forecasting errors than previous-prediction reference data. For TINS, the MAPE decreased from 5.51% in the 2021 post-pandemic period to 2.73% in the 2023 normal period using real-time reference data. In contrast, using previous predicted values produced substantially higher errors, particularly in 2021, with a MAPE of 27.28%. These findings indicate that the generalized Wiener process is reliable for short-term stock price forecasting when supported by real-time reference data, even under volatile market conditions. The study contributes to financial forecasting by demonstrating the sensitivity of stochastic stock price models to market regimes and reference-data selection.
creator: Irsal Alfian
creator: Radhiah
creator: Rini Oktavia
rights: Copyright (c) 2026 Irsal Alfian, Radhiah, Rini Oktavia https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-01
publication date: 2026-06-01
volume: 1
number: 1
starting page: 1
ending page: 9
doi: 10.55749/rmm.v1i1.175

Optimization of fiber optic backbone networks using minimum spanning tree based on Kruskal and prim algorithms
type: item
description: Fiber optic cable has become the primary backbone infrastructure for modern campus communication networks due to its high transmission speed, large bandwidth capacity, and long-distance reliability. However, the deployment cost of fiber optic infrastructure is relatively expensive, particularly in star-topology network architectures that require extensive cable utilization. Therefore, network optimization is essential to reduce infrastructure costs while maintaining network connectivity and performance. This study proposes a graph-theoretical optimization approach for the fiber optic backbone network at Syiah Kuala University (USK), Indonesia, using the Minimum Spanning Tree (MST) concept implemented through Kruskal’s and Prim’s algorithms. The existing network infrastructure was modeled as a weighted graph consisting of 29 vertices representing campus buildings and weighted edges representing fiber optic cable distances. Furthermore, a reconstructed network model was developed by assuming direct connectivity between neighboring buildings to obtain a more efficient topology configuration. The results demonstrate that both Kruskal’s and Prim’s algorithms produced identical MST structures with a total cable length of 5,632 meters. Compared with the currently installed network length of 11,175 meters, the optimized MST configuration reduced cable usage by 5,543 meters, corresponding to approximately 49.6% network efficiency improvement. The resulting topology also transformed the existing star-ring hybrid architecture into a more efficient tree-based backbone network. The findings indicate that Minimum Spanning Tree modeling provides an effective mathematical framework for optimizing fiber optic infrastructure in smart campus environments.
creator: Siti Nurhaliza
creator: Nurmaulidar
creator: Bonno Andri Wibowo
rights: Copyright (c) 2026 Siti Nurhaliza, Nurmaulidar Oktavia, Bonno Andri Wibowo https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-01
publication date: 2026-06-01
volume: 1
number: 1
starting page: 10
ending page: 15
doi: 10.55749/rmm.v1i1.176

Optimizing bank teller service efficiency using the M/M/s queueing model and server scenario simulation
type: item
description: This study analyzes the optimal teller service system at Bank X using a multi-server queueing model. Customer waiting time and teller utilization are important indicators of service quality in banking operations, particularly when customer arrivals exceed service capacity. The objective of this study is to determine the appropriate queueing model and evaluate the optimal number of service servers based on arrival and service-time distributions. Secondary data consisting of customer arrivals and service times over 30 observation days were analyzed using the Kolmogorov–Smirnov test. The results show that customer arrivals follow a Poisson distribution, while service times follow an exponential distribution. Therefore, the queueing system was modeled as an (M/M/5):(FCFS/∞/∞) system, representing five servers, first-come-first-served discipline, unlimited queue capacity, and infinite customer population. The average arrival rate was 0.3246 customers per minute, while the average service rate was 0.2839 customers per minute. The average steady-state value was ρ=0.2348<1, indicating that the existing system is stable and does not require additional servers. The average probability of idle tellers was 34.08%, the average number of customers in the queue was 0.0163, the average waiting time in the queue was 0.0310 minutes, and the average time spent in the system was 3.6520 minutes. Server scenario simulation showed that the system remains optimal with a minimum of three servers, resulting in an (M/M/3):(FCFS/∞/∞) model. These findings indicate that queueing theory can support service efficiency improvement and resource allocation in banking operations.
creator: Fitri Marhamah
creator: Vera Halfiani
creator: Vina Apriliani
rights: Copyright (c) 2026 Fitri Marhamah, Vera Halfiani, Vina Apriliani https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-01
publication date: 2026-06-01
volume: 1
number: 1
starting page: 16
ending page: 21
doi: 10.55749/rmm.v1i1.177

Forecasting bitcoin prices using the ARIMA–GARCH model: A volatility-based time series approach
type: item
description: Bitcoin is one of the most actively traded digital assets and is characterized by high price volatility, making accurate forecasting essential for investment decision-making. This study aims to determine the best ARIMA–GARCH model for forecasting Bitcoin prices and to analyze future price movements based on historical weekly data. The dataset consists of 157 weekly Bitcoin closing prices collected from August 12, 2018, to August 8, 2021, obtained from Investing.com. The forecasting process was conducted for the subsequent five-week period from August 15, 2021, to September 12, 2021. The analysis began with stationarity testing using the Augmented Dickey–Fuller (ADF) test and Box–Cox transformation. Since the original series was non-stationary in the mean, first-order differencing was applied. Several ARIMA models were identified using the ACF and PACF plots, followed by diagnostic checking and ARCH-LM testing to detect heteroskedasticity effects. The presence of volatility clustering justified the implementation of the GARCH model. Model selection was based on parameter significance and the minimum Akaike Information Criterion (AIC) value. The results indicate that the ARIMA(0,1,2)-GARCH(1,3) model is the best model for forecasting Bitcoin prices. Forecasting results show a gradual decline in Bitcoin prices over the next five periods. The model achieved a Mean Absolute Percentage Error (MAPE) value of 3%, indicating excellent forecasting performance. These findings demonstrate that the ARIMA–GARCH approach is effective for modeling and forecasting highly volatile cryptocurrency price movements.
creator: Hizbul Watan
creator: Rini Oktavia
creator: Fitria Lestari
rights: Copyright (c) 2026 Hizbul Watan, Rini Oktavia, Fitria Lestari https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-01
publication date: 2026-06-01
volume: 1
number: 1
starting page: 22
ending page: 28
doi: 10.55749/rmm.v1i1.178

Short-term prediction of Indonesian gold prices using a weighted Markov chain approach
type: item
description: Gold is one of the most widely used investment assets because of its high economic value and relatively stable role during economic uncertainty. However, fluctuations in gold prices may affect investors’ potential gains and losses, making price prediction important as a consideration in investment decision-making. This study applies the weighted Markov chain method to predict Indonesian gold prices using weekly gold price data from January 2019 to December 2020. The data were grouped into two state classifications, namely six states and ten states, to evaluate the effect of state formation on prediction accuracy. Transition probability matrices were constructed for each classification, while the weights were determined based on autocorrelation coefficients. Prediction accuracy was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the predicted gold prices tend to remain in the same state over the prediction period. In the six-state classification, the predicted price was in state 5 with an estimated value of Rp833,701 and a MAPE of 0.66%. In the ten-state classification, the predicted price was in state 9 with an estimated value of Rp847,580 and a MAPE of 1.67%. These results indicate that the weighted Markov chain method provides very good short-term prediction accuracy. However, for longer prediction periods, the predicted values tend to become constant and are less able to capture volatile movements in actual gold prices. Therefore, the weighted Markov chain method is more suitable for short-term gold price prediction than for long-term forecasting.
creator: Mirnawati Oktavia
creator: T. Murdani Saputra
creator: Ikhsan Maulidi
creator: Fajri Farid
rights: Copyright (c) 2026 Mirnawati Oktavia, T. Murdani Saputra, Ikhsan Maulidi, Fajri Farid https://creativecommons.org/licenses/by-sa/4.0
license: https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-01
publication date: 2026-06-01
volume: 1
number: 1
starting page: 29
ending page: 35
doi: 10.55749/rmm.v1i1.179