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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 210 Documents
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The Optimal Control of Mathematical Models for Monkeypox Spread Using the Pontryagin Maximum Principle with Numerical Solution RK6 on Study in Indonesia: engglish side, syafruddin; Yusuf SAP, Andi Muh. Ridho; Musfira, Musfira; Abdy, Muhammad
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9461

Abstract

This study examines the SIQR mathematical model by applying the Pontryagin Maximum Principle (PMP) and the 6th order Runge-Kutta (RK6) numerical solution to the spread of Monkeypox in Indonesia. This study aims to analyze and simulate the dynamics of the spread of Monkeypox and identify optimal control strategies that are effective in suppressing the rate of infection. This study has a population of 283,487,843 individuals. The results of this study show the effectiveness of implementing optimal control in reducing the spread of Monkeypox in the region. This study makes a significant contribution to the development of more efficient and targeted health policies in dealing with Monkeypox outbreaks, and offers valuable insights for future infectious disease control strategies.
APPLICATION OF GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY (GARCH) MODEL IN FORECASTING THE MARKET PRICE OF NICKEL IN INDONESIA Sidjara, Sahlan; Sanusi, Wahidah; Nyulle, Rusdianto
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9798

Abstract

Indonesia is one of the largest nickel exporting countries in the world, with the increasing demand for electric vehicles making nickel a target for producers. The increase in nickel demand makes it necessary to increase the observation of nickel prices to maintain the sustainability of the mining industry and economic growth. The purpose of this study is to forecast the price of Indonesia's nickel market using the GARCH method. The GARCH method is one of the methods used in time series data modeling that identifies heteroscedatic effects. The steps taken are to analyze the training data, check the stationery, estimate the parameters, and test the diagnostic model, then the best ARIMA model is selected based on the smallest AIC value, namely ARIMA (0,1,1). The residual values of the best ARIMA models are then used to determine the GARCH model. The best GARCH model obtained is GARCH (0.1) with an AIC value of 19.04061. Furthermore, forecasting was carried out using the GARCH model (0.1) and comparing the forecast results with the testing data to obtain MAPE values. The MAPE value obtained is 17.67014 % which shows that the GARCH model (0.1) has good forecasting accuracy, so this model is quite feasible to be used in forecasting the price of Indonesia's nickel market.
The Coefficient Parallelisator Matrix: A Diagonal Similarity Operator for Symmetry Preservation in Knot Semantic Logic Ja'faruddin, Ja'faruddin; Ashari, Nur Wahidin; Baharuddin, Baharuddin; Asyari, Syahrullah; Fadiyah, Wulan Nur; Farhan, Muhammad
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.10762

Abstract

This study introduces the Coefficient Parallelisator Matrix (CPM) as a novel diagonal similarity operator designed to preserve structural and semantic symmetry within the framework of Knot Semantic Logic (KSL). The CPM formalizes the process of parallelization in linguistic and conceptual structures by transforming semantic matrices through similarity operations that maintain eigenvalues, determinants, and symmetry invariants. Each element of the CPM acts as a scaling coefficient, re- balancing semantic weights while conserving the overall interpretive equilibrium of the text. Mathematically, the transformation A′ = MAM−1 establishes a spectral equivalence between the original and parallelized structures, ensuring that both share identical eigen-spectra, determinant, and Hermitian invariants. This invariance reflects a form of semantic gauge symmetry, wherein the un- derlying topology of meaning remains stable despite local transformations in semantic intensity. Conceptually, the operator bridges linguistic theory, topology, and algebraic representation, providing a formal mechanism for analyzing reflective relations such as parallelism, chiasmus, and concentric composition. The findings extend the mathematical foundation of KSL by establishing the Coefficient Parallelisator as an analytical framework for quantifying semantic symmetry—enabling deeper integration between mathematical logic, structural linguistics, and computational semantics.
Mathematical Modelling of Dengue Fever Spread with Education-Based Prevention in South Sulawesi Pratama, Muhammad Isbar; Mariani, Mariani; Fadilah, Nur; Wahyuni, Maya Sari
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.10907

Abstract

Dengue Fever (DF) remains a major public health challenge in many tropical regions, including South Sulawesi, Indonesia, where increasing case numbers highlight the urgent need for more effective disease control strategies. Traditional approaches that rely solely on medical treatment and vector suppression have shown limited long-term success, thus necessitating complementary preventive interventions such as health education. This study develops a deterministic SIRS host–vector mathematical model to analyse the epidemiological dynamics of DF transmission and to quantify the impact of educational intervention on reducing disease spread. The model incorporates human susceptibility, infection, temporary immunity, mosquito–human transmission mechanisms, and an education parameter that represents the rate at which susceptible individuals become effectively protected. Stability analysis is conducted to determine the conditions for disease persistence or elimination, and the basic reproduction number is derived using the next-generation matrix method. Numerical simulations are performed using biologically realistic parameter values for South Sulawesi. The results show that when , both human and vector infections converge to endemic equilibrium levels, consistent with the theoretical analysis. However, increasing the education-related protection parameter significantly reduces infection prevalence and can bring below unity, leading to disease eradication. The findings demonstrate that educational interventions play a critical role in reducing transmission intensity and complementing vector control measures. This study provides a mathematical foundation for evaluating community-based education as a sustainable component of DF prevention, offering valuable insights for public health policy in dengue-endemic regions.
Implementation of Acceptable Quality Level (AQL) in the Incoming Quality Assurance (IQA) Inspection Process Using ANSI/ASQ Z1.4 Islamiyah, Deni; Kusumasari, Vita; Azizah, Azizah
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/v2466r81

Abstract

PT XYZ operates in the manufacturing industry with a focus on producing wind musical instruments. The Incoming Quality Assurance (IQA) inspection process at PT XYZ faces challenges related to the effectiveness and efficiency of quality control, as indicated by the high number of material defects recorded in April 2025, totaling 8,583 defective items. The incomplete implementation of the Acceptable Quality Level (AQL) method at PT XYZ has resulted in some inspection processes still being conducted through 100% inspection, leading to inefficiencies in inspection time, particularly given the limited number of available inspectors. This study aims to evaluate the application of the AQL method based on the ANSI/ASQ Z1.4 standard and to analyze sample performance as an effort to improve inspection efficiency. The methods applied in this study include the use of the Seven Tools approach to identify types of defects, the application of the AQL method in the sampling process and lot acceptance decision-making, and the measurement of sample performance. The Seven Tools analysis identified scratch defects as the most dominant type, accounting for 35% of the total defects. The implementation of the AQL method resulted in an AQL value of 2.5 under the normal inspection category, with a sample size of 20 units and acceptance and rejection numbers of 1 and 2, respectively. The sample performance evaluation showed an Operating Characteristic (OC) curve value of 0.9198, an Average Outgoing Quality (AOQ) of 1.916%, and an Average Total Inspection (ATI) of 29.7 units. These findings demonstrate that the application of the AQL method is effective in reducing defect rates while improving the efficiency of the IQA process without the need for 100% inspection.
Estimating the Relative Risk of Dengue Hemorrhagic Fever in Makassar City Using a Bayesian Spatial Localised Conditional Autoregressive Model Rahmawati; Aswi, Aswi; Hidayat , Rahmat; Palarungi Taufik, Andi Gagah
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/z0mxxw06

Abstract

Dengue Hemorrhagic Fever (DHF) remains a significant public health challenge in Indonesia, including in Makassar City, which reported an increase of 291 cases in 2024. This study aimed to estimate the relative risk of DHF across 15 districts of Makassar by incorporating covariates such as population density, distance to the city center, and the number of hospitals, using a Bayesian Conditional Autoregressive (CAR) Localised approach. The data were obtained from the publication Makassar City in Figures 2025, issued by the Central Statistics Agency. Spatial autocorrelation analysis with Moran’s I indicated significant clustering of DHF cases. Model selection was conducted using the Deviance Information Criterion (DIC), Watanabe–Akaike Information Criterion (WAIC), and group-level area coverage. The results showed that the best-fitting model was the CAR Localised model with distance as a covariate (M9), specified at G = 3 with hyperprior IG (1; 0.01). Distance exhibited a negative association with DHF incidence, suggesting that the farther a district is from the city center, the lower its relative risk. Among the districts, Rappocini exhibited the highest relative risk followed by Panakkukang, while the lowest risks were observed in Sangkarrang Islands. These findings provide valuable insights for designing spatially targeted DHF prevention and control strategies in Makassar City.
Tourism Forecasting Using Chen and Singh Fuzzy Time Series Models Vivianti, Vivianti; Aswi, Aswi
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/w2554664

Abstract

The tourism sector is one of the main drivers of the national economy, which experienced a significant decline due to the COVID-19 pandemic. In the post-pandemic era, the recovery of international tourist arrivals shows a positive trend, thus requiring accurate forecasting methods to support tourism policy planning. ARIMA method are less effective in handling nonlinear and fluctuating data. This study applies the Fuzzy Time Series (FTS) approach, specifically the Chen and Singh models, which are capable of managing data uncertainty and representing linguistic patterns adaptively. The purpose of this study is to compare the accuracy of both models using two interval determination approaches, namely the Sturges method and the mean-based method, in forecasting international tourist arrivals through Sultan Hasanuddin International Airport during the period from January 2023 to September 2025. The analytical steps include defining the universe of discourse, performing fuzzification, constructing fuzzy logical relationships (FLR) and fuzzy logical relationship groups (FLRG), and applying defuzzification to obtain forecasted values. The forecasting accuracy was evaluated using the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results show that the choice of interval determination method significantly affects forecasting performance, with the mean-based method producing more detailed and accurate intervals. Based on the evaluation, the FTS Singh model demonstrated the best performance, with MAPE of 2.16% and RMSE of 31.05, outperforming the Chen model under both interval approaches. Therefore, the combination of the FTS Singh model with the mean-based interval method is recommended as the optimal approach for forecasting post-pandemic international tourist arrivals, as it can capture fluctuating data patterns more precisely and consistently.
Evaluation of Tree-Based Models for Predicting Social Assistance Recipient Status Based on National Socio-Economic Survey (SUSENAS) 2024 Hiola, Yani Prihantini; Zulhijrah; Putra, I Gusti Ngurah Sentana; Limba, Syella Zignora; Sartono, Bagus; Firdawanti, Aulia Rizki; Susetyo, Budi; Dito, Gerry Alfa
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/xyyv0f37

Abstract

Abstract. Poverty is a major socioeconomic challenge in Indonesia that affects the effectiveness of social protection programs. In response to this challenge, the government has created social assistance programs to improve the welfare of the people. However, the distribution of social assistance is often considered to be inaccurate, resulting in households that are deemed eligible for social assistance not being identified as recipients. One solution to improve the accuracy of distribution is the application of machine learning in the context of classification. Several tree-based models, such as LightGBM, Random Forest, and XGBoost, were selected because of their superior capabilities compared to classical models such as logistic regression, especially in handling complex data and fulfilling model assumptions. This study compares the performance of these three models in predicting social assistance recipient status using data from the 2024 West Java Provincial National Socioeconomic Survey (SUSENAS). Model evaluation was conducted on several data pre-processing scenarios involving outlier handling, class balancing, and feature engineering. The results show that LightGBM consistently outperforms the other models on six metrics, namely Accuracy, Balanced Accuracy, F1-Score, ROC-AUC, PR-AUC, and Brier Score, out of a total of eight evaluation metrics used. SHAP analysis identifies Social Assistance History and Asset Score as the most influential features for model prediction. Friedman and Nemenyi nonparametric tests confirmed significant performance differences between LightGBM and other models based on the F1-Score, PR-AUC, and Brier Score metrics. These findings indicate that tree-based models, particularly LightGBM, can support the development of a more targeted and data-driven social assistance targeting system. Keywords: Social Assistance; Tree-Based; SHAP; SUSENAS; Hybrid Bayesian Optimization
Analyzing the Factors Influencing Generation z Students’ Entrepreneurial Interest at Universitas Jambi Using SEM-PLS Sarmada, Sarmada; Z, Gusmanely.; Safitri, Yuliana; Multahadah, Cut; Shahadah, Nabilah
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/a6d88a16

Abstract

Unemployment is one of the most serious issues facing Indonesia and must be addressed promptly, as it has a direct impact on public welfare and economic growth. This challenge is also evident in Jambi Province, which continues to face difficulties in providing sufficient employment opportunities for its residents. A key solution to reducing unemployment is to encourage entrepreneurship among Generation Z—who are creative, innovative, and capable of leveraging digital technology to create new business opportunities. This study aims to analyze the factors influencing entrepreneurial interest among students at the University of Jambi using the Structural Equation Modeling–Partial Least Squares (SEM-PLS) approach. The research employed a quantitative survey method, with data collected through a Likert-scale questionnaire. The exogenous variables examined include social and family environment, innovation and creativity, and technology. The Entrepreneurial interest serving as the endogenous variable. The results of the analysis indicate that all exogenous variables have a significant effect on entrepreneurial interest, with an R² value of 0.59. It is means that 59% of the variation in entrepreneurial interest can be explained by the model. Innovation and creativity were found to have the most dominant influence, with a parameter value of 0.352. These findings highlight the importance of strengthening innovation and creativity in fostering students’ entrepreneurial interest.
Comparison of Multiple Kernel Learning and Single Kernel Support Vector Machine for Public Opinion Classification Poliyama, Shafiah; Achmad, Novianita; Abdussamad, Siti Nurmardia
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/qea76e33

Abstract

Abstract. Social media has become a digital public space where public opinion is expressed on various government policies. Social media platform X has become a major venue for openly expressing support and criticism, making it relevant to sentiment analysis. This condition is useful for understanding public perceptions of government policies, such as the Makan Bergizi Gratis (MBG) Programme, which has elicited various public responses since its implementation. Support Vector Machine (SVM) is a widely used method for sentiment classification, but its performance is highly dependent on kernel selection. Using a single kernel type often fails to capture both linear and non-linear patterns in social media texts. Therefore, this study aims to compare the performance of Single Kernel and Multiple Kernel Learning (MKL) in classifying public sentiment from social media X. The research methods included collecting Indonesian language tweets through scraping techniques, text pre-processing, feature extraction using Term Frequency–Inverse Document Frequency (TF–IDF), data division with a ratio of 80:20, and the classification process using SVM with linear kernel, Radial Basis Function (RBF) kernel, and a combination of both through the MKL approach. The results show that MKL based SVM provides the best performance with an accuracy of 93.17%, while Linear and RBF kernels produce accuracies of 91.81% and 92.49%, respectively, on the same dataset and testing scheme.

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