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Redaksi BAREKENG: Jurnal ilmu matematika dan terapan, Ex. UT Building, 2nd Floor, Mathematic Department, Faculty of Mathematics and Natural Sciences, University of Pattimura Jln. Ir. M. Putuhena, Kampus Unpatti, Poka - Ambon 97233, Provinsi Maluku, Indonesia Website: https://ojs3.unpatti.ac.id/index.php/barekeng/ Contact us : +62 85243358669 (Yopi) e-mail: barekeng.math@yahoo.com
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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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ROBUSTNESS EVALUATION OF THE 3-SATISFIABILITY REVERSE ANALYSIS METHOD WITH DISCRETE HOPFIELD NEURAL NETWORK AND GENETIC ALGORITHM FOR TRAFFIC FLOW DATASET Amierah Abdul Malik; Mohd. Asyraf Mansor; Nur Ezlin Zamri; Nurul Atiqah Romli
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/barekengvol20iss3pp2413-2426

Abstract

Traffic flow congestion is a pervasive global phenomenon. Nonetheless, the systematic analysis and identification of traffic flow patterns remain a challenge as the volume of traffic data increases. Consequently, robust data extraction methods are required to uncover underlying data patterns. This paper proposes a 3-Satisfiability logic mining approach using a Discrete Hopfield Neural Network, develops the 3-Satisfiability Reverse Analysis method by integrating the Discrete Hopfield Neural Network with a Genetic Algorithm, and implements this method on traffic flow datasets, comparing its accuracy with existing approaches. The 3-Satisfiability Reverse Analysis method employs 3-Satisfiability for logical representation and integrates a Discrete Hopfield Neural Network with a Genetic Algorithm as its learning system. A simulation was conducted using the Urban Traffic dataset for São Paulo, Brazil. The robustness of the method in extracting relationships within traffic flow data was evaluated using selected performance metrics. The results indicated that the proposed 3-Satisfiability Reverse Analysis method, which integrates the Discrete Hopfield Neural Network and Genetic Algorithm, achieved promising performance with an accuracy rate of 80%, outperforming existing methods
OPTIMAL CONTROL USING QUADRATIC-QUADRATIC REGULATOR (QQR) FOR MATHEMATICAL MODEL OF CATTLE FOOT AND MOUTH DISEASE (FMD) Fadilah Akbar; Mardlijah Mardlijah; Mahmud Yunus
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/barekengvol20iss3pp2427-2446

Abstract

Foot and Mouth Disease (FMD), a prevalent disease among cattle in East Java, poses a serious threat to the livestock industry in the Province, Indonesia. Based on field observations, Revifr, the foot-and-mouth disease (FMD) virus, can disseminate via the air, direct contact, and carriers, resulting in decreased appetite and severe bleeding due to toenail loss from infected cattle. It is inevitable that losses will be incurred in the economic and food sectors due to the significant number of cattle that have perished as a result of this FMD infection outbreak. A mathematical model based on the SEIR (Susceptible, Exposed, Infected, and Recovered) model was developed to formulate an optimal strategy for mitigating the impact of FMD outbreaks. The analysis indicates that the model meets the well-posed criteria, thereby validating its use. The control design is presented as the vaccination and treatment of cattle using the Quadratic-Quadratic Regulator (QQR) method, a development of the Linear Quadratic Regulator (LQR). The results of the control design indicate that the optimal vaccination strategy should be administered to 45.93% of susceptible cattle, while treatment should be provided to 32.74% of infected cattle. The simulation results indicate that the QQR method is more optimal for managing FMD outbreaks in cattle. This is evident in its lower performance and cost, as well as its faster containment time when compared to the LQR method.
PREDICTIVE MODELLING OF CLEAN WATER SUPPLY IN RIAU PROVINCE: A DEEP LEARNING APPROACH Agustin Agustin; Junadhi Junadhi; Lusiana Efrizoni; Deshinta Arrova Dewi; Abhishek Saxena
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/barekengvol20iss3pp2447-2460

Abstract

The supply of clean water remains a critical issue in many regions, including Riau Province, where factors such as population growth and climate variability significantly affect its availability and distribution. This study aims to develop a time-series–based predictive model for clean water supply in Riau Province using deep learning approaches. Using historical data from 2019 to 2023, including variables such as the number of customers, water volume, economic value, and input costs, this research identifies temporal patterns to support proactive water resource management. The methodology consists of exploratory data analysis, data preprocessing, and model training using several architectures, namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Feedforward Neural Network (FNN). Among these models, the LSTM achieved the best performance, with a Mean Absolute Error (MAE) of 1.25, a Mean Squared Error (MSE) of 2.56, and an R-squared (R²) of 0.92. After hyperparameter optimization, further improvements in predictive accuracy were obtained. Based on the optimized LSTM predictive model, the forecasted clean water volume for 2024 is 19,496.90 thousand m³, a slight decline from the previous year. The novelty of this study lies in the comprehensive comparison of multiple deep learning architectures for regional-scale clean water time-series forecasting and the optimized implementation of LSTM for operational prediction. In practical terms, the results can support local water authorities in improving planning, infrastructure development, and demand management strategies. However, this study is limited by the use of secondary data from a single province and a relatively short observation period, which may affect the model's generalizability. The proposed predictive framework can serve as a reference for future studies in sustainable water resource management.
EXTRACTIVE CLINICAL NOTES SUMMARIZATION USING SINGLE MACHINE LEARNING, ENSEMBLE, AND STACKING APPROACHES Junadhi Junadhi; Agustin Agustin; Deshinta Arrova Dewi; Abhishek Saxena
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/barekengvol20iss3pp2461-2474

Abstract

Summarizing clinical notes is pivotal to supporting medical decision-making by presenting relevant information concisely and efficiently. However, the complexity of clinical language, the unstructured nature of the text, and the inherent class imbalance pose major challenges for the development of automatic summarization systems. This study develops a framework for extractive clinical notes summarization and compares the performance of single-model machine learning, simple ensembles, and stacking. A synthetic dataset comprising 2,000 clinical notes was segmented into 22,000 sentences, each labeled as important or not important according to a reference extractive summary. The methodology includes text preprocessing (normalization, expansion of medical abbreviations, tokenization, and stopword removal), feature extraction (TF-IDF, Named Entity Recognition, and structural features), and implementation of multiple models. Evaluation relies on Accuracy, Precision, Recall, and F1-score, complemented by Entity-F1, redundancy analysis, and latency per document. Experimental results show that the best single model, XGBoost, achieves an F1-score of 0.76, reflecting its ability to capture non-linear interactions among heterogeneous clinical text features under class imbalance, while simple ensembles further improve performance to 0.78. The most substantial gains are obtained with stacking, which reaches an F1-score of 0.80, precision of 0.83, and recall of 0.78. The confusion matrix indicates low false negatives, and the Precision–Recall curve (AP = 0.73) demonstrates consistent behavior under imbalanced data conditions. Overall, the findings establish stacking as the most effective approach for extractive summarization of clinical notes. Beyond theoretical relevance, the results carry practical implications for developing clinical decision support systems that are safe, efficient, and readily integrable into digital health services.
MULTI-OBJECTIVE MIXED-INTEGER PROGRAMMING MODEL WITH BATTERY AND CHARGING CONSTRAINTS FOR ELECTRIC FEEDER BUS NETWORKS Rini Yanti; Parlindungan Kudadiri; Eka Setia Novi; Febria Marta Siska; Deshinta Arrova Dewi; R. Raja Subramanian
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/barekengvol20iss3pp2475-2490

Abstract

The deployment of electric vehicle (EV)–based feeder bus networks is increasingly promoted to support sustainable urban transportation systems. However, their operational planning is challenged by limited battery capacity, charging time requirements, and restricted charging infrastructure, which introduce complex trade-offs between operational efficiency, energy consumption, and service coverage. This study aims to develop a Multi-Objective Mixed-Integer Programming (MOMIP) model that explicitly incorporates battery state-of-charge dynamics and charging station constraints for optimizing electric feeder bus networks. The proposed model simultaneously minimizes operational costs and total energy consumption while maximizing service coverage, enabling a comprehensive evaluation of conflicting operational objectives. The use of MOMIP is justified by the need to capture Pareto-optimal trade-offs among these competing objectives within a unified mathematical formulation. Numerical experiments based on hypothetical operational scenarios demonstrate that the model generates feasible Pareto-optimal solutions, revealing clear trade-offs between cost efficiency, energy usage, and network accessibility. Analysis further indicates that increasing charging capacity significantly enhances system performance, reducing energy consumption by more than 20% and improving service coverage by over 7 percentage points. The proposed model provides a robust decision-support tool for transport planners and contributes to the development of energy-efficient and sustainable electric feeder bus operations.
ENHANCING MAIZE YIELD PREDICTION IN INDONESIA USING HYBRID MACHINE LEARNING MODELS Adyanata Lubis; Eko Oktafanda; Juliarni Juliarni; Junadhi Junadhi
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/barekengvol20iss3pp2491-2506

Abstract

Maize is a strategic commodity in Indonesia’s national food system, yet traditional yield-prediction methods based on statistical or survey approaches often fail to capture the nonlinear and dynamic relationships among agronomic, climatic, and socio-economic variables. Accurate forecasting remains essential for supporting food self-sufficiency and climate-resilient agricultural planning. To address these challenges, this study proposes SMART-JAGUNG, a machine learning–based maize yield prediction system employing three ensemble and regression models: Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost). The dataset comprises five years of maize production data from the Indonesian Central Bureau of Statistics (BPS), along with auxiliary variables including rainfall, temperature, NDVI, seed type, and fertilizer use. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination before and after hyperparameter tuning with GridSearchCV. Results indicate that RF achieved the best performance before tuning (MAE = 36,310.53; RMSE = 95,343.05; = 0.9758), followed closely by XGBoost, while SVR consistently underperformed. Although post-tuning performance slightly decreased, the predicted-versus-actual visualization confirmed the robustness of RF and XGBoost for non-extreme data. Overall, SMART-JAGUNG demonstrates strong potential as a reliable, data-driven decision-support tool for precise maize yield estimation, contributing to sustainable food security and national self-sufficiency policies.
EFFECTIVENESS OF DIMENSIONALITY REDUCTION METHODS ON DATA WITH NON-LINEAR RELATIONSHIPS Lukmanul Hakim; Asep Saefuddin; Kusman Sadik; Anwar Fitrianto; Bagus Sartono
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/barekengvol20iss3pp2507-2522

Abstract

The phenomenon of big data presents distinct challenges in the analysis process, especially when the data contains a very large number of variables. High complexity, potential redundancy, and the risk of overfitting are major issues that must be addressed through dimensionality reduction techniques. Principal Component Analysis (PCA) is a common method effective for data with linear relationships but has limitations in identifying nonlinear patterns. This research aims to improve performance of classification by introducing autoencoder for dealing with nonlinear relationship, data noise, missing values, outliers, and data with various scales. This study employs a quantitative approach through analysis of simulated and empirical data in the form of the Village Development Index from the Central Statistics Agency, which contains variables with various measurement scales. Both dimensionality reduction methods—PCA and neural network-based autoencoders—are tested across various data scenarios. The evaluation is conducted based on their effectiveness in preserving data structure, as well as the Mean Squared Error (MSE) values in the reconstruction process. The results indicate that PCA excels in computational efficiency and accuracy for data with linear relationships. In contrast, the autoencoder demonstrates superior performance in detecting nonlinear patterns, achieving lower Mean Squared Error (MSE) values with stable MSE standard deviations. Additionally, the autoencoder proves to be more robust in handling missing values and outliers compared to PCA. The selection of dimensionality reduction methods highly depends on the characteristics of the analyzed data. Autoencoders represent a superior alternative for handling complex and nonlinear data, although they require model parameter tuning. Further research is recommended to explore the influence of network architecture and training strategies of autoencoders on dimensionality reduction performance.
WEB BASED GEOGRAPHIC INFORMATION SYSTEM FOR OPTIMAL TOURIST ROUTE PLANNING IN NORTH SUMATRA USING THE ANT COLONY OPTIMIZATION ALGORITHM Faridawaty Marpaung; Mulyono Mulyono; K M A Fauzi; Eni Yuniastuti; Arnita Arnita; Suvriadi Panggabean
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/barekengvol20iss3pp2523-2534

Abstract

The transition toward Tourism 4.0 has redefined travel planning as a multifaceted optimization challenge, specifically the Personalized Tourist Trip Design Problem (PTTDP). While conventional navigation services offer basic routing, they frequently lack the capacity to integrate multi-objective constraints with interactive, preference-based spatial visualizations. This research addresses this gap by developing an integrated Spatial Decision Support System (SDSS) that merges the Ant Colony Optimization (ACO) metaheuristic with a Web-based Geographic Information System (WebGIS). The study employs a quantitative methodology, using a weighted-sum scalarization technique to harmonize divergent goals: maximizing destination attraction scores while simultaneously reducing travel distance and duration. Based on empirical validation in Berastagi City, North Sumatra, the results reveal that the ACO-WebGIS framework substantially outperforms traditional routing methods, achieving 17.84% reduction in total distance, 17.24% improvement in time efficiency, and 42.85% increase in the number of POIs visited within identical time constraints, all supported by a swift computational latency of only 1.45 seconds. The scientific value of this work lies in the seamless coupling of algorithmic optimization and dynamic spatial mapping, providing a scalable, robust tool for intelligent tourism management that delivers a mathematically sound yet practical solution for modern travelers.
COMPARATIVE STUDY OF LIGHTGBM, CATBOOST, AND RANDOM FOREST IN MODELING PUBLIC COMPLAINTS CLASSIFICATION Oktaviyani Daswati; Hari Wijayanto; Farit Mochamad Afendi
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/barekengvol20iss3pp2535-2548

Abstract

Public complaints data on maladministration in Indonesia is a dataset with high-cardinality categorical variables and imbalanced category distributions, posing significant challenges for conventional machine learning algorithms. To address this issue, this study aims to evaluate and compare the performance of three widely used classification algorithms (LightGBM, CatBoost, and Random Forest) on actual public complaint data that has never been analysed using machine learning methods. Hyperparameter tuning was applied to obtain optimal configurations and ensure robust performance. Analysis was conducted using 30 repeated simulations with accuracy and sensitivity as the primary metrics. ANOVA followed by Tukey HSD was used to explicitly determine whether there were differences in performance between models at a 95% confidence level. The results show that LightGBM performed best with an accuracy of 74.50% and a sensitivity of 76.70%, followed by CatBoost with an accuracy of 74.12% and a sensitivity of 75.54%, while Random Forest lagged far behind. Statistical tests confirmed significant performance differences between the three models. This study is not without limitations. Only three classification algorithms were evaluated, encoding strategies were not systematically compared, and the hyperparameter search space was restricted, meaning broader model exploration may yield improved performance. Nonetheless, the study provides originality and value by representing the first empirical application of machine learning to Indonesian public complaint data on maladministration, demonstrating how algorithm selection directly affects predictive outcomes when handling complex categorical structures. The findings offer practical insights for government agencies, highlighting how data-driven models can support policy design, strengthen transparency, and improve the quality of public services.
AG-FE3O4 HYBRID NANOFLUID DYNAMICS: EXPLORING SLIP AND MAGNETIC EFFECTS ON THE FLOW OVER EXPONENTIALLY ELONGATED/CONTRACTED SURFACE Rahimah Jusoh; Zulkhibri Ismail; Mikhail Sheremet; Nooraini Zainuddin; Mohd Hisyam Ariff
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/barekengvol20iss3pp2549-2560

Abstract

This study explores the unique advantages of hybrid nanofluids, known for their exceptional ability to boost heat transfer efficiency, making them ideal for advanced thermal applications. The objective is to assess the impact of slip and magnetic field on the velocity and temperature profiles over an exponentially elongated/contracted surface. Through the application of an appropriate similarity transformation, the governing equations for energy, momentum, and mass are converted into ordinary differential equations. These resulting equations are subsequently solved numerically via the bvp4c function in MATLAB. Results imply that magnetic fields decelerate the fluid while thickening the thermal boundary layer due to the Lorentz force. Increased viscous dissipation elevates temperature levels, while surface elongation promotes convective heat transfer. In contrast, surface contraction and velocity slip suppresses thermal transport by limiting momentum exchange. Thermal slip further reduces surface heat flux These findings underscore both the novelty and practical potential of Ag–Fe₃O₄ hybrid nanofluids in enhancing performance across thermal regulation systems, such as energy-efficient cooling devices, biomedical heat exchangers, and industrial applications.

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