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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 1,429 Documents
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PATTERN RECOGNITION FOR RF NEURAL SIGNAL PROCESSING USING KNN DISCRIMINANT CLASSIFICATION Nur Irsalina Huda Nazri; Muhammad Rasyid Rosli; Roshakimah Mohd Isa; M. N. Ezzuddean Miswan Hanis
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp3473-3488

Abstract

Radiofrequency (RF) radiation from modern wireless devices has raised concerns about its potential effects on brain activity, especially in the Alpha and Beta frequency bands. However, research in this area faces significant challenges due to small and limited electroencephalogram (EEG) datasets, which often lead to inconsistent results and hinder reliable performance evaluation. This study addresses these limitations by incorporating synthetic data generation to enhance dataset robustness while maintaining realistic signal characteristics. The main objective of this study is to optimize the Power Asymmetry Ratio (PAR) for improved brainwave classification, using a dataset of 97 participants with engineering backgrounds from Universiti Teknologi MARA (UiTM), including both Males and Females, exposed to three types of RF exposure (Left Exposure (LE), Right Exposure (RE), and Sham Exposure (SE)) across two sessions (Before and During). The analysis incorporates advanced signal processing techniques, including ANOVA based feature analysis and K-Nearest Neighbors (KNN) modeling with Mahalanobis distance metric to evaluate gender-specific neural responses. Analysis of RF exposure data reveals clear gender-based patterns in classification performance. With a 70:30 data split for training and testing, in During exposure, Female participants achieved the highest KNN accuracy with 88% for training and 66% for testing when using Mahalanobis distance at K=3, while Male participants reached 80% (training) and 58% (testing) at K=2. The combined (synthetic + actual) data consistently outperformed actual data alone in training. This highlights synthetic data’s role in enhancing model robustness, especially for limited datasets. These results demonstrate that well-tuned classification algorithms can detect gender-specific brain responses to RF exposure, with Mahalanobis distance excelling in capturing subtle neural variations. The integration of synthetic data offers a scalable solution to small-data challenges, improving reliability in RF exposure studies while underscoring the need for gender- specific modeling in neural signals.
DESIGN AND IMPLEMENTATION OF A SMART OUTPASS MANAGEMENT SYSTEM USING FACE RECOGNITION S. Aasha Nandhini; G. M. Deyanesh Krishna; Malathy Batumalay
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp3489-3500

Abstract

In educational institutions, manual outpass verification commonly fails because it is inefficient, relies on people, and is prone to counterfeit. This leads to delays in administration and security breaches. Standard digital substitutes that use barcodes or RFID technology only partially automate the process and don't include biometric validation. To solve these problems, an intelligent web-enabled framework is being built that uses deep learning-based face recognition and connects to the Internet of Things infrastructure. The system uses OptimizedEdgeNet, a new, lightweight convolutional neural network that is developed for embedded edge devices like the Raspberry Pi. A mathematical optimization approach is proposed that takes into account recognition accuracy, decision threshold, computing latency, and energy use. The proposed methodology employs Gaussian distance modelling of facial embeddings to analytically find the ideal matching threshold (τ*) that lowers the total recognition error. Additionally, equations for latency and energy are developed as factors of operation count, processor frequency, and hardware efficiency, creating a multi-objective optimization function that enhances recognition performance while reducing delay and power consumption. The analytical model is confirmed by experimental testing, which shows that the Raspberry Pi 4B can recognize 96.2% of the time with an average delay of 1.64 seconds and an energy use of 2.4 J per inference. The suggested deep learning architecture and mathematical formulation offer a thorough, real-time, and resource-efficient approach for automating secure digital outpasses in regulated settings.
A HYBRID BERT-BILSTM-ATTENTION MODEL FOR PUBLIC SENTIMENT ANALYSIS ON A NATIONAL SOCIAL INSURANCE PROVIDER IN INDONESIA Syaiful Anam; Nur Atiqah Sia Abdullah; Hilmi Aziz Bukhori; Avin Maulana; Regina Vincentia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp3501-3518

Abstract

Sentiment analysis in Indonesian social media presents significant challenges due to informal language, code-mixing, and sentiment ambiguity in multi-clause expressions. While transformer-based models such as IndoBERT effectively capture contextual semantics, they may struggle to model sentiment transitions and resolve conflicting polarity in noisy and heterogeneous text. To address this limitation, this study employs a hybrid BERT–BiLSTM–Attention model that integrates contextual, sequential, and attention-based representations. Although the architecture itself is not novel, the contribution lies in its systematic integration and empirical evaluation under realistic conditions. Experimental results show that the proposed model achieves an accuracy of 0.85 and a Macro-F1 score of 0.85, outperforming the IndoBERT baseline (0.83) by approximately 2.4%. This improvement is statistically significant (p = 0.027) and supported by effect size analysis, indicating meaningful performance gains. Robustness evaluation under controlled perturbations—including slang injection, character elongation, emoji usage, code-mixing, and class imbalance (up to 30% minority downsampling)—shows only minor performance degradation (0.02–0.03 Macro-F1), demonstrating stable generalization under noisy conditions. These findings provide empirical evidence that the integration of sequential modeling and attention improves the handling of sentiment transitions and multi-clause structures beyond transformer-only approaches, offering practical value for real-world sentiment analysis in Indonesian social media.
THE LOCATING RAINBOW EDGE CONNECTION NUMBERS OF SOME GENERALIZED SUN GRAPHS Muhammad Ahnaf Yusuf; A. N. M. Salman; Mukayis Mukayis
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp3519-3532

Abstract

Throughout this paper, G denotes a finite, simple, connected, and undirected graph. The concept of the locating rainbow edge connection number is motivated by the concept of the locating rainbow connection number in which the coloring is assigned to edges while preserving the locating property. The coloring focuses on determining the smallest natural number k such that there exists a locating rainbow edge -coloring of a graph such that every edge has a distinct rainbow code. In this paper, we define standard generalized sun graphs and leveled generalized sun graphs . We determine their locating rainbow edge connection numbers. The results are obtained through a theoretical approach and observations of graph structures with rigorous mathematical proofs. The results of Sun(n,p) and depend on their cycle order and the number of bridges.
SCALABLE DETECTION OF COST INEFFICIENCY IN BPJS KESEHATAN CLAIMS USING XGBOOST WITH IMBALANCE LEARNING AND VAEX-BASED BIG DATA PROCESSING Alpian Roymundus Siringo-ringo; Ramadhan Paninggalih; Rizal Kusuma Putra
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp3533-3546

Abstract

BPJS Kesehatan Indonesia's national health insurance system, faces significant challenges in managing large-scale claim data, particularly in identifying cost inefficiencies such as abnormal claims, duplication, and misuse. Although machine learning methods have been widely applied in healthcare analytics, limited studies address both large-scale data processing and class imbalance issues in inefficiency detection. This study aims to develop a scalable classification model using XGBoost integrated with Vaex for efficient big data processing. The dataset was obtained from the JKN Healthkathon, consisting of millions of claim records with predefined inefficiency labels. Data preprocessing was performed using Vaex to enable out-of-core computation, followed by model development using XGBoost with various data splitting strategies and imbalance handling techniques, including random oversampling and undersampling. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results indicate that the balanced split with random oversampling achieved the most stable performance, with precision, recall, and F1-score of 0.91. In contrast, stratified splitting without imbalance handling yielded higher accuracy but lower recall. This study contributes by proposing a scalable and efficient framework that integrates XGBoost with Vaex for large-scale healthcare claim analysis and systematically evaluates the impact of imbalance handling strategies on model performance. However, the use of predefined labels with undisclosed generation processes may introduce potential bias in the results.
RANDOM FOREST-BASED CARDIOVASCULAR DISEASE PREDICTION WITH SHAP-DRIVEN INTERPRETABILITY Farrel Rafa Akbar; Dina Tri Utari
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp3547-3558

Abstract

Cardiovascular disease continues to be a significant worldwide health issue, where early detection is essential due to the frequently asymptomatic beginning of heart attacks. This research presents a model for predicting the risk of heart disease utilizing the Random Forest (RF) algorithm, trained on clinical data obtained from Zheen Hospital in Erbil, Iran. The data can be found online through the Mendeley Data website. The Synthetic Minority Oversampling Technique (SMOTE) was used to fix the problem of uneven class sizes, and Shapley Additive Explanations (SHAP) were used to explain how the model made its predictions. The RF model, improved with the best settings and evaluated with the F1-score, achieved impressive results, which are more than 99% for training, validation, and testing data. These results underscore its capacity to discover minority class patterns, crucial for recognizing rare yet significant occurrences. SHAP analysis identified troponin, creatine kinase-MB, and age as the primary predictors. The new idea is not just about individual methods but how they work together for predicting cardiovascular disease, especially by making AI easier to understand and dealing with uneven data using real clinical information. The subsequent study will aim to enhance robustness and generalizability across diverse patient populations.
EARLY DETECTION OF RAINFALL ANOMALIES USING LSTM AND ISOLATION FOREST Adhystira Raihannoeza Almadiva; Atika Ratna Dewi; Aina Latifa Riyana Putri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp3559-3574

Abstract

The uncertainty of daily rainfall patterns in Cilacap Regency with extreme variations makes it difficult to detect hydrological anomalies early using traditional methods. This study aims to obtain the most optimal LSTM parameters for rainfall prediction models, evaluate model performance using the Mean Squared Error (MSE) also Root Mean Squared Error (RMSE) metric, and predict rainfall anomalies for the next year using Isolation Forest. Daily BMKG data from January 2015 to December 2024 were processed through preprocessing stages, including missing data handling and time sequence creation. The Long Short Term Memory model was trained using regularization techniques to avoid overfitting, and the prediction results were analyzed using Isolation Forest to identify anomalies. The experiment showed that the best combination of hyperparameters was LSTM with 50 units and a tanh activation function, dropout 0.3, followed by a first dense layer of 50 units with ReLU activation and a single output layer. This configuration resulted in a validation MSE of 23.5247161 and RMSE 4.8502, this result identified five cases of anomalies that were validated for suitability based on rainfall data from BPBD. These results demonstrate the model's ability to reconstruct rainfall patterns and detect potential anomalies, so the system has potential to support early warning efforts for disaster mitigation and water resource management in Cilacap.
FORECASTING OIL PRODUCTION USING SSA AND TREND REGRESSION IN THE WORLD’S TOP THREE OIL PRODUCERS Ega Saherti; Salwa Azzah Imtiyaz; Gumgum Darmawan; Budi Nurani Ruchjana
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp3575-3588

Abstract

The world's primary energy source, which plays a significant role in various global sectors, is petroleum. Due to geological influences, energy policies, and geopolitical factors, oil production patterns are often fluctuating and non-stationary. This also applies to the world's of top three oil-producing countries, that is, the United States, Saudi Arabia, and Iraq. This situation poses a challenge in developing accurate forecasting models to support global energy planning. This study aims to forecast oil production trends in the United States, Saudi Arabia, and Iraq using the Singular Spectrum Analysis (SSA) method combined with Trend Regression to obtain a forecasting model capable of capturing long-term patterns and mitigating the influence of short-term fluctuations. Annual oil production data for the period 1936–2024 were taken from Our World in Data. The analysis stages include data decomposition using SSA to separate trends, noise, and cycles, followed by trend component modeling using trend regression. Model evaluation was carried out using the coefficient of determination (R²). The results of the study indicate that the SSA and Trend Regression methods are able to produce stable and accurate projections, with the highest R² value in Saudi Arabia (0.98), followed by Iraq (0.75), and the United States (0.48). All three countries show an increasing production trend until 2034 with different patterns. The SSA and Trend Regression methods are effective in capturing the complex and non-stationary dynamics of oil production. This study provides both academic and practical contributions in the application of the SSA–Trend Regression hybrid method for global oil production forecasting as well as practical contributions for policymakers in projecting global oil production trends.
OPTIMISATION OF IMAGE DATA PREPARATION USING HYBRID WHITE BALANCE METHOD FOR CLASSIFICATION OF STRAW MUSHROOM IMAGE QUALITY Bayu Priyatna; Titik Khawa Abdurahman; April Lia Hananto; Aviv Yuniar Rahman
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp3589-3604

Abstract

This study aims to enhance the quality of straw mushroom images by applying a Hybrid White Balance (HWB) preprocessing method to improve classification accuracy. Variations in illumination often introduce color distortion, which negatively affects feature extraction and reduces the performance of machine learning models. Therefore, robust preprocessing techniques are required to handle lighting inconsistencies and improve image quality. In this study, the HWB method is combined with normalization and histogram equalization to produce more consistent visual representations. Straw mushroom images were collected under varying lighting conditions from different agricultural environments. The preprocessing stage includes color correction using HWB followed by normalization to reduce variability. The processed images were then classified using a Convolutional Neural Network (CNN). The results show that preprocessing significantly affects classification performance. The model without preprocessing achieved an mAP@0.5 of approximately 0.927, while Standard White Balance improved performance to around 0.978 in terms of precision, recall, and F1-score. The best results were obtained using HWB, achieving precision of approximately 0.996, mAP of about 0.994, and F1-score around 0.9395, indicating more accurate and robust classification. Additionally, image quality evaluation shows that HWB reduces Mean Squared Error (MSE) and increases Peak Signal-to-Noise Ratio (PSNR) and Signal-to-Noise Ratio (SNR), outperforming standard preprocessing methods. In conclusion, HWB-based preprocessing effectively enhances both image quality and classification performance. This method has strong potential as a reliable preprocessing approach for agricultural image analysis, particularly under varying illumination conditions, and can support the development of more robust and adaptive classification systems.

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