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Journal of Mathematics, Computation and Statistics (JMATHCOS)
Published by Universitas Negeri Makassar
ISSN : 24769487     EISSN : 27210863     DOI : https://doi.org/10.35580/jmathcos
Core Subject : Education,
Mathematics
Fokus yang didasarkan tidak hanya untuk penelitian dan juga teori-teori pengetahuan yang tidak menerbitkan plagiarism. Ruang lingkup jurnal ini adalah teori matematika, matematika terapan, program perhitungan, perhitungan matematika, statistik, dan statistik matematika.
Arjuna Subject : Matematika - Matematika Terapan
Articles 214 Documents
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Mathematical Modeling of Typhoid Fever Control Through Non-Pharmaceutical Interventions in South Sulawesi Fausiatul Iffa; Wahidah Sanusi; Alimuddin Alimuddin
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/74nj6t57

Abstract

Typhoid fever remains one of the major public health problems in Indonesia, particularly in South Sulawesi. This article discusses mathematical modeling of the impact of non-pharmaceutical interventions on typhoid fever control. This study aims to develop a mathematical model that represents the dynamics of typhoid fever transmission and to solve the model numerically using the Variational Iteration Method (VIM). The model applied is the SICRB compartment model, which divides the population into five groups: susceptible (S), infected (I), chronic carrier (C), recovered (R), and bacterial concentration (B). The analysis results indicate that the basic reproduction number R₀ > 1, suggesting that typhoid fever has the potential to persist in the population. Numerical simulations are carried out using Maple for analytical derivation and R Studio for visualization of the dynamics. The implementation of non-pharmaceutical interventions, including health education, environmental sanitation, and herbal treatment, demonstrates a significant reduction in infection cases, an increase in recovery, and a decrease in bacterial concentration. The model shows stability toward the endemic equilibrium and consistency between mathematical analysis and numerical simulations.
Comparison of Holt’s Exponenetial Smoothing and GARCH Models In Forecasting BNI Bank Stock Isma Muthahharah; Muhammad Fahmuddin; Muhammad Nusrang
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/a76hfv58

Abstract

This study aims to compare the forecasting performance of Holt’s Exponential Smoothing and the ARIMA–ARCH/GARCH models in predicting the stock return volatility of Bank Negara Indonesia (BNI). Accurate forecasting of financial time series is essential for investors, policymakers, and market analysts, particularly in emerging markets such as Indonesia, where volatility levels tend to fluctuate due to global and domestic economic conditions. The data used in this study consist of weekly closing prices of BNI stock from January 2020 to August 2025, which were transformed into weekly stock returns. The analysis began with descriptive statistics to examine the trend and volatility behavior of the return series. Holt’s Exponential Smoothing was employed to capture the level and trend components of the data. Meanwhile, the ARIMA–ARCH/GARCH modelling approach was applied to address conditional heteroskedasticity and volatility clustering, which are typical features of financial return data. Model diagnostics, including parameter significance, stationarity tests, and white-noise assessments, were conducted to ensure the suitability of the models. The forecasting accuracy of both models was evaluated using RMSE criteria. The results indicate that the ARIMA ([4],0,0)–ARCH (2) model provides the most accurate predictions, reflected by its lower RMSE value compared to Holt’s Exponential Smoothing. This finding demonstrates that volatility-sensitive models outperform trend-based smoothing methods when applied to financial data characterized by fluctuating variance. Overall, this study highlights the importance of selecting forecasting methods that align with the statistical behavior of financial time series. The findings offer valuable insights for investors, financial analysts, and economic policymakers seeking to improve forecasting accuracy and strengthen risk management strategies in dynamic market environments.
Development of a Hybrid ARIMA–Fourier Series Model for Air Temperature Forecasting at the Gorontalo Climatology Station Suci Tilome; Isran K. Hasan; Agusyarif Rezka Nuha
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcosv9i1.11462

Abstract

Air temperature is a key climatic variable that reflects environmental conditions and influences various human activities. Recent observations indicate a persistent upward trend associated with global warming, leading to greater variability in climate patterns. These changes highlight the importance of forecasting methods that can accurately represent the characteristics of air temperature time series to support planning and decision-making. Reliable prediction is therefore essential for understanding climate dynamics and anticipating potential environmental impacts. This study proposes an air temperature forecasting approach using a hybrid Autoregressive Integrated Moving Average (ARIMA) and Fourier Series Analysis (FSA) model. The ARIMA component is applied to model trend behavior and temporal dependence, while FSA captures the remaining seasonal patterns in the ARIMA residuals. By integrating these two approaches, the hybrid model aims to improve forecasting accuracy in the presence of both stochastic and periodic components. The results show that the hybrid ARIMA–FSA model achieves good forecasting performance, with a Mean Absolute Error (MAE) of 0.56, a Root Mean Square Error (RMSE) of 0.66, and a Mean Absolute Percentage Error (MAPE) of 2.07%. These findings indicate that the proposed model effectively represents air temperature dynamics and can be considered a reliable alternative for climate forecasting applications
Adaptive ANFIS–PSO Model for Forecasting Bird’s Eye Chili Prices in Gorontalo Province Nur Siyam Djibu; Isran K. Hasan; Agusyarif Rezka Nuha
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcosv9i1.11473

Abstract

Bird’s eye chili is one of the strategic food commodities in Indonesia with high price volatility and a significant contribution to food inflation, particularly in Gorontalo Province. The dynamic and nonlinear characteristics of bird’s eye chili prices often hinder accurate forecasting when using conventional methods, thereby requiring an adaptive approach capable of capturing complex data patterns. Therefore, this study applies an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized using Adaptive Particle Swarm Optimization (PSO) to improve the accuracy of bird’s eye chili price forecasting. This study utilizes daily bird’s eye chili price data in Gorontalo Province from 1 January 2019 to 31 October 2025, obtained from the National Strategic Food Price Information Center (PIHPS). The ANFIS model is optimized using adaptive PSO to obtain optimal parameter values that address local convergence problems and parameter sensitivity commonly encountered in conventional ANFIS models. Model performance is evaluated using the Mean Absolute Percentage Error (MAPE). The results indicate that the adaptive ANFIS–PSO model achieves a MAPE value of 17.4487% on the training dataset, which decreases significantly to 5.0741% on the testing dataset. The testing MAPE value below 10% demonstrates that the proposed model has excellent generalization capability in capturing bird’s eye chili price fluctuations. These findings confirm that adaptive PSO-based parameter optimization effectively enhances ANFIS performance in modelling nonlinear and highly volatile time series data. The proposed forecasting model can serve as a reliable analytical tool to support decision-making and regional food price stabilzation policies in Gorontalo Province.

Page 22 of 22 | Total Record : 214

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