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BAREKENG: Jurnal Ilmu Matematika dan Terapan
Published by Universitas Pattimura
ISSN : 19787227     EISSN : 26153017     DOI : https://search.crossref.org/?q=barekeng
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation
BAREKENG: Jurnal ilmu Matematika dan Terapan is one of the scientific publication media, which publish the article related to the result of research or study in the field of Pure Mathematics and Applied Mathematics. Focus and scope of BAREKENG: Jurnal ilmu Matematika dan Terapan, as follows: - Pure Mathematics (analysis, algebra & number theory), - Applied Mathematics (Fuzzy, Artificial Neural Network, Mathematics Modeling & Simulation, Control & Optimization, Ethno-mathematics, etc.), - Statistics, - Actuarial Science, - Logic, - Geometry & Topology, - Numerical Analysis, - Mathematic Computation and - Mathematics Education. The meaning word of "BAREKENG" is one of the words from Moluccas language which means "Counting" or "Calculating". Counting is one of the main and fundamental activities in the field of Mathematics. Therefore we tried to promote the word "Barekeng" as the name of our scientific journal also to promote the culture of the Maluku Area. BAREKENG: Jurnal ilmu Matematika dan Terapan is published four (4) times a year in March, June, September and December, since 2020 and each issue consists of 15 articles. The first published since 2007 in printed version (p-ISSN: 1978-7227) and then in 2018 BAREKENG journal has published in online version (e-ISSN: 2615-3017) on website: (https://ojs3.unpatti.ac.id/index.php/barekeng/). This journal system is currently using OJS3.1.1.4 from PKP. BAREKENG: Jurnal ilmu Matematika dan Terapan has been nationally accredited at Level 3 (SINTA 3) since December 2018, based on the Direktur Jenderal Penguatan Riset dan Pengembangan, Kementerian Riset, Teknologi, dan Pendidikan Tinggi, Republik Indonesia, with Decree No. : 34 / E / KPT / 2018. In 2019, BAREKENG: Jurnal ilmu Matematika dan Terapan has been re-accredited by Direktur Jenderal Penguatan Riset dan Pengembangan, Kementerian Riset, Teknologi, dan Pendidikan Tinggi, Republik Indonesia and accredited in level 3 (SINTA 3), with Decree No.: 29 / E / KPT / 2019. BAREKENG: Jurnal ilmu Matematika dan Terapan was published by: Mathematics Department Faculty of Mathematics and Natural Sciences University of Pattimura Website: http://matematika.fmipa.unpatti.ac.id
Arjuna Subject : Matematika - Matematika Terapan
Articles 1,369 Documents
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FRACTIONAL MATHEMATICAL MODELLING OF THE CORROSION RATE OF ALUMINUM 5083 IN A MARITIME ENVIRONMENT Muhammad Rifki Nisardi; Nur Rahmi; Muhammad Arkam Arifuddin; Hartina Husain; Kusnaeni Kusnaeni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2117-2130

Abstract

Aluminum 5083 is one of the materials used for the construction of ship hulls due to its classification as a material with good corrosion resistance. Despite its high corrosion resistance, Aluminum 5083 remains susceptible to galvanic corrosion and pitting corrosion caused by marine environmental conditions. This study develops a mathematical model by adding chloride and passivation effects using fractional differential equations to describe the corrosion rate of Aluminum 5083. The model construction using a Fractional Differential Equation System (FDES) aims to capture memory effects to represent the complex corrosion dynamics accurately. Stability analysis of the fractional model shows that the system is locally asymptotically stable, with all eigenvalues satisfying the condition . Furthermore, a numerical solution approach using the PECE-PI method is employed to solve the model, demonstrating agreement between the simulation results and real-world corrosion phenomena. Involvement of different fractional orders α reveals an effect on the rates of increase and decrease in concentration for each variable. The smaller the value of fractional order α, the slower the concentration change process occurs.
GREY WOLF-OPTIMIZED XGBOOST REGRESSOR FOR STOCK INDEX PREDICTION WITH FINANCIAL FEATURES Syaiful Anam; Mohd Razif Shamsuddin; Elyas Kustiawan; Dwi Mifta Mahanani; Feby Indriana Yusuf
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2131-2150

Abstract

Accurate stock index prediction is essential for effective investment strategies and economic policymaking. While traditional statistical models often fail to capture the nonlinear dynamics of financial markets, machine learning approaches—particularly Extreme Gradient Boosting (XGBoost)—offer greater flexibility, robustness to overfitting, and computational efficiency. However, the performance of XGBoost strongly depends on hyperparameter tuning, which is difficult to optimize using conventional search methods. To address this, we propose a hybrid framework that integrates XGBoost with Grey Wolf Optimization (GWO) for enhanced hyperparameter selection in stock index forecasting. Using historical data from the Indonesian BBNI stock index (2021–2024) and financial features (price, volume, and temporal), the GWO-optimized XGBoost achieved superior performance, recording the lowest testing MAPE (1.79%), RMSE (108.67), and MAE (84.32). These results surpass classical regressors (Decision Tree, Random Forest, Multilayer Perceptron, Gradient Boosting) by margins of 6–26% and outperform conventional tuning methods (Grid Search, Random Search, Bayesian Optimization) as well as other swarm intelligence approaches (PSO, BA). Moreover, the GWO-based approach reduced error variability and required significantly less optimization time, with the 10-wolf configuration providing the best accuracy–efficiency tradeoff. The scope of this study is limited to a single stock index (BBNI.JK) and financial features, without incorporating macroeconomic indicators, sentiment variables, or cross-market validation. These limitations indicate potential directions for future work to enhance generalizability. Overall, the proposed GWO-XGBoost framework provides a powerful, stable, and time-efficient solution for stock index prediction in volatile market conditions.
THIN FILM FLOW ON AN INCLINED CHANNEL Leo Hari Wiryanto; Sudi Mungkasi; Ikha Magdalena
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2151-2162

Abstract

A two-dimensional fluid is considered on an inclined channel. The depth of the fluid is small, so that it can be modeled as a single equation of the fluid depth, from the lubrication theory. The model is then solved numerically by an implicit finite difference method, to observe the surface wave propagation from the parameters such as the inclination of the channel and the ratio of the fluid thickness with respect to the wavelength. The effect of non-linearity of the model indicates that the wave propagates with changing the form, decreasing the amplitude, and tending to an almost shock wave. Those are simulated in this paper.
AN INTEGRATIVE MODEL FOR DRUG INVENTORY OPTIMIZATION IN PHARMACIES USING WMA AND EOQ CONTINUOUS Kwardiniya Andawaningtyas; Raqqasyi Rahmatullah Musafir; Nandia Primasari; Rina Adhista; Trya Rizky Adellia; Cornelia Yosefine Halim; Evi Ardiyani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2163-2178

Abstract

Efficient drug inventory control is essential for pharmacies to maintain service quality, prevent stockouts, and reduce financial losses caused by excessive inventory. This study develops an integrative inventory optimization model combining ABC analysis, Weighted Moving Average (WMA) forecasting, and the Economic Order Quantity (EOQ) Continuous Review approach. ABC analysis identifies high-priority drugs requiring strict control, WMA forecasts demand for Category A items, and the EOQ model determines optimal order quantity, safety stock, and reorder point. Results show that the integration of forecasting and continuous review improves accuracy in estimating demand fluctuations and reduces total inventory costs compared with existing ordering practices. The originality of this work lies in formalizing the integration of WMA forecasting into EOQ Continuous Review, specifically for pharmaceutical inventory systems. Study limitations include the use of a single-pharmacy dataset, fixed lead-time assumptions, and reliance on only one forecasting method. This integrated approach provides a novel and more responsive solution for pharmaceutical inventory management, as the use of WMA enhances forecast accuracy by emphasizing recent demand shifts, while the EOQ Continuous Review model ensures optimal ordering decisions in real time. Together, these methods create a more adaptive framework that reduces uncertainty, improves stock availability, and minimizes overall inventory costs.
EVALUATION OF THE FLEXIBILITY OF NADARAYA-WATSON KERNEL AND PENALIZED SPLINE ESTIMATORS IN BIVARIATE RESPONSE NONPARAMETRIC REGRESSION MODELS Cinta Rizki Oktarina; Sigit Nugroho; Idhia Sriliana
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2179-2194

Abstract

Nonparametric regression is a flexible approach used when the functional relationship between predictors and responses is unknown. In the context of multiple responses, bivariate nonparametric regression allows modeling two correlated response variables, such as stunting and wasting prevalence, which remain critical issues in public health. This study aims to evaluate the flexibility and performance of two nonparametric estimators, the Nadaraya-Watson Kernel and the Penalized Spline, for modeling bivariate response data. The research was conducted in two stages: (1) simulation using variations in sample sizes (50, 100, 150, 200) and error variances based on exponential and trigonometric functions, and (2) application to real data on stunting and wasting prevalence in Indonesia (2024) obtained from Statistics Indonesia (BPS), with socioeconomic and health-related predictors. Model performance was assessed using RMSE, MSE, and R-squared, complemented by MANOVA, orthogonal polynomial contrasts, and Tukey’s post-hoc test to examine significant differences across scenarios. Simulation results indicate that the Nadaraya-Watson Kernel estimator consistently outperformed the Penalized Spline, providing lower RMSE and MSE values and greater stability, particularly for larger sample sizes and smaller error variances. Orthogonal polynomial analysis revealed a quadratic relationship between sample size and RMSE, with occasional cubic patterns, while error variance consistently exhibited a quadratic trend. In the applied study, the Nadaraya-Watson Kernel with a Gaussian kernel achieved high accuracy, with an MSE of 0.00086 and an R-squared value indicating a strong model fit. However, this high R-squared value may reflect potential overfitting, which warrants further validation through cross-validation. These findings demonstrate that the Nadaraya-Watson Kernel offers an effective approach for bivariate nonparametric regression, supporting data-driven policy decisions in nutrition and public health.
MULTIVARIATE TIME SERIES MODELING USING VECTOR AUTOREGRESSION FOR RICE PRICE PREDICTION IN INDONESIA Atika Ratna Dewi; Andreas Rony Wijaya; Mirza Ghanimi; Talitha Veda Azaria Ramadhani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2195-2212

Abstract

This study analyzes the dynamic relationship between rice prices and selected economic variables using a Vector Autoregression (VAR) model. The analysis utilizes daily data from January 2022 to December 2023, encompassing rice prices, chicken meat prices, chicken egg prices, the Rupiah-to-USD exchange rate, inflation, and crude oil prices. The estimated VAR model is stable, as all eigenvalues lie within the unit circle. Residual diagnostics based on the Portmanteau (Ljung–Box) test indicate no residual autocorrelation across all equations (LB statistics with df = 1, p-values > 0.05), confirming the adequacy of the model specification. The model demonstrates good predictive performance for the rice-price series, achieving a Mean Absolute Percentage Error (MAPE) of 0.42% over the out-of-sample testing period (the last 20% of observations). Empirical results suggest that rice prices are influenced by dynamic interactions within the system, particularly through their relationships with chicken meat prices and the Rupiah–USD exchange rate. These findings offer valuable policy insights for maintaining rice price stability, a crucial component of national food security.
SURVIVAL ANALYSIS OF CORONARY HEART DISEASE PATIENTS USING THE KAPLAN-MEIER METHOD AND COX PROPORTIONAL HAZARDS REGRESSION (BRESLOW METHOD) Sudianto Manullang; Marlina Setia Sinaga; Pardomuan N. J. M. Sinambela; Fauza Nadya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2213-2228

Abstract

This study aims to analyze the survival of CHD patients during hospitalization using survival analysis methods. The Kaplan–Meier method was applied to estimate survival probabilities, and group differences were tested using the log-rank test. Furthermore, the Cox proportional hazards model with the Breslow approach was used to assess the effect of clinical factors on survival, with assumptions verified using Schoenfeld residuals. By integrating nonparametric and semiparametric survival methods, this study provides a more comprehensive assessment of CHD patient survival compared with previous studies that relied on a single analytical approach. Data were collected retrospectively from 150 inpatients at Haji General Hospital, Medan, between 2021 and 2022, with 45 cases identified as censored. The Kaplan–Meier analysis revealed a progressive decline in survival probability during hospitalization, with the survival rate decreasing from 69.3% on the first day to 5.3% by day 40. The log-rank test results indicated that only hypertension had a statistically significant effect on patient survival (p < 0.001), while age, gender, and cholesterol status were not significant (p > 0.05). The Cox regression analysis confirmed these findings, showing that CHD patients with hypertension had more than three times higher risk of death (HR = 3.13; 95% CI: 2.06–4.78) compared to those without hypertension. These findings highlight hypertension as the most dominant risk factor reducing survival among CHD patients during hospitalization. This supports prioritizing early detection and intensive monitoring for hypertensive CHD patients to improve in-hospital clinical outcomes. However, this study has limitations due to its single-center retrospective design and the use of only four variables, leaving out other clinical factors that may influence survival outcomes.
BDAMP AND BAMP OPTIMIZATION OF COMMUNICATION IN SDN-BASED FOG AND CLOUD COMPUTING Mustafa Hasan Albowarab; Nurul Azma Zakaria; Z. Zainal Abidin; Fairul Azni Jafar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2229-2244

Abstract

Software-Defined Networking (SDN) has emerged as a revolutionary paradigm. The integration of SDN within fog networks represents a synergistic convergence of two cutting-edge technologies. With the complexity of SDN serving fog networks, the optimization of communication cost becomes paramount. Addressing the intricate challenges of communication cost optimization necessitates the application of sophisticated methodologies. Multi-Objective Optimization (MOO) algorithms present a robust solution, allowing for the simultaneous optimization of multiple conflicting objectives. By employing MOO, this research proposes a bi-objective optimization model for the intra- and inter-domain communication cost of controller deployment in an SDN-based computing network. The evaluation performed has captured two aspects of the performance of using Binary Angle quantization Multi-objective Particle swarm optimization (BAMP) and Binary crowding Distance Angle quantization Multi-objective Particle swarm optimization (BDAMP) for SDN controllers’ deployment. The first aspect is multi-objective-based evaluation, and the second aspect is the SDN network performance. Our developed BAMP and BDAMP have shown superiority over the benchmarks in terms of both aspects. Most importantly, the best performance is achieved by BDAMP in terms of both intra- and inter- communication cost.
LEVEL SOFT SEMIRING FROM FUZZY SUBSEMIRING Saman Abdurrahman; Thresye Thresye
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2245-2258

Abstract

This paper introduces the concept of a level soft set and a level soft semiring over a semiring derived from a fuzzy subsemiring. By employing the level subset approach of fuzzy sets, we construct soft structures whose images form subsemirings of the underlying semiring. Several fundamental properties of level soft semirings are established, including their behavior under intersection, union, and AND operations. These results extend classical semiring theory into the fuzzy–soft framework and provide a rigorous algebraic foundation for further theoretical developments. The proposed framework may serve as a basis for future applications in information systems, decision-making, and computational algebra.
MATHEMATICAL MODELLING AND OPTIMAL INTERVENTION STRATEGIES FOR MENINGITIS TRANSMISSION DYNAMICS Ayodeji Sunday Afolabi; Abdulwahab Ridwan; Princess Chiamaka Uche
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2259-2280

Abstract

Meningitis remains a major public health concern, particularly within the African meningitis belt, where recurrent outbreaks pose severe health and socio-economic challenges. The disease is transmitted through close contact and is driven by complex human-to-human dynamics. While vaccination campaigns and treatment programs are central to control efforts, limitations such as waning immunity and delayed case detection often reduce their long-term impact. In alignment with global health recommendations, integrated control approaches that combine prevention, vaccination, and timely treatment are increasingly being advocated. In this study, a novel deterministic nonlinear seven-compartmental model, SVECITR, is developed. The model’s validity is confirmed through positivity and boundedness analyses, and key thresholds such as the DFE and the , are derived using the next-generation matrix method. Stability analysis was also performed. The framework is extended into an optimal control problem using three time-dependent interventions: public health education, booster vaccine administration, and prophylactic chemoprophylaxis. Using Pontryagin’s Maximum Principle, optimal strategies are obtained, and numerical techniques are employed to simulate various intervention scenarios. Our results reveal that while dual interventions moderately reduce disease prevalence, the combined application of all three control measures resulted in the most substantial decline in transmission. Cost-effectiveness analysis, which employs the Incremental Cost-Effectiveness Ratio (ICER), Average Cost-Effectiveness Ratio (ACER), and Infection Averted Ratio (IAR), shows that combining booster vaccination and prophylactic chemoprophylaxis emerges as the most cost-effective option. These findings suggest that, in resource-limited settings, public health authorities should prioritize booster vaccination and chemoprophylaxis administration to curb the spread of meningitis while optimizing the use of available resources.

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