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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 1,309 Documents
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QUANTILE BASED PLS-SEM WITH WILD BOOTSTRAP Balami, Abdul Malik; Otok, Bambang Widjanarko; Purnami, Santi Wulan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1775-1790

Abstract

Partial Least Squares SEM (PLS-SEM) is the recommended technique for structural equation modeling (SEM), which assesses correlations between latent components concurrently, particularly for small samples and non-normal data. But because traditional PLS-SEM only calculates average correlations between constructs, it runs the risk of overlooking variances in the quantile distribution. Consequently, the creation of the Quantile PLS-SEM approach, which incorporates quantile regression, provides a means to examine correlations across the entire data distribution. To improve estimation, wild bootstrap is used to address heteroscedasticity issues and produce more reliable inferences. The purpose of this study is to develop and apply Quantile based PLS-SEM with Wild Bootstrap to analyze the gizi data status of the Indonesian population based on the Survey Status Gizi Indonesia 2024. The analysis's findings indicate that specific and sensitive interventions have a significant impact on the gizi status of different quantities.
A COMPARATIVE STUDY OF PIPELINE-VALIDATED MACHINE LEARNING CLASSIFIERS FOR PERMISSION-BASED ANDROID MALWARE DETECTION Lubis, Arif Ridho; Wulandari, Dewi; Adha, Lilis Tiara; Ariyani, Tika; Lase, Yuyun; Lubis, Fahdi Saidi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1675-1692

Abstract

The growing prevalence of Android malware distributed through third-party APK sideloading poses a significant security threat to users and developers. This study aims to evaluate the effectiveness of three machine learning algorithms—Logistic Regression (LR), Random Forests (RF), and Gradient Boosting Machine (GBM)—for static Android malware detection based on permission features. The experiment employs the publicly available Android Malware Prediction Dataset (Kaggle, accessed 2025), containing 4,464 application samples with 328 binary permission attributes. A leakage-free CRISP-DM workflow was implemented, integrating data cleaning, automated feature selection via SelectKBest (Mutual Information), and hyperparameter optimisation using GridSearchCV with stratified 5-fold cross-validation. Results on the unseen hold-out test set show that GBM achieved the best performance, with 96.05% accuracy and 0.9924 ROC-AUC, outperforming LR and RF. In addition, GBM exhibited superior probability calibration (Brier Score = 0.0344) and interpretability, as confirmed through SHAP analysis. The ablation study further validated that optimal model performance saturates at 30–40 selected features. This research contributes a reproducible and pipeline-validated comparative framework for static Android malware detection, addressing prior studies’ limitations regarding feature selection bias and data leakage. Nevertheless, the study is limited by its reliance on static permission features and the absence of dynamic behavioural data, which may restrict generalisation to evolving malware families.
APPLICATION OF THE RANDOM FOREST ALGORITHM FOR ESTIMATING CONDITIONAL VALUE AT RISK (CVAR) ON THE STOCK PORTFOLIO OF INSURANCE COMPANIES IN INDONESIA Purwanto, Purwanto; Olivia, Agna
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1693-1708

Abstract

This study aims to estimate Conditional Value at Risk (CVaR) for insurance company stock portfolios using a machine learning approach to improve the accuracy of financial risk measurement under extreme market conditions. The application of machine learning, particularly the Random Forest algorithm, is crucial for the Indonesian insurance sector, which faces increasing exposure to market volatility and uncertainty. The model predicts stock returns based on technical indicators such as moving averages, volatility, and lagged returns. The analysis uses historical data from ten insurance companies listed on the Indonesia Stock Exchange (IDX) for the period 2022–2025. To assess model performance, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Kupiec backtesting are employed. The model produces CVaR estimates of 1.65% and 1.94% at the 95% and 99% confidence levels, respectively. It also achieves a low MAE of 0.006701 and MSE of 0.000091, indicating high estimation accuracy. The Kupiec test results further confirm the statistical reliability of the CVaR estimates. This study contributes methodologically by highlighting the effectiveness of non-parametric ensemble learning in financial risk modeling. The findings offer practical implications for insurance firms and portfolio managers in adopting adaptive, data-driven risk mitigation strategies, especially in volatile market environments.
ANALYSIS OF MULTILINGUAL OPINION POLARIZATION WITH CROSS-LINGUAL LANGUAGE MODEL-ROBUSTLY OPTIMIZED BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS APPROACH (XLM-ROBERTA) Kananta, Ghaitsa Shafa Cinta; Saputro, Dewi Retno Sari; Sutanto, Sutanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1709-1718

Abstract

The rapid growth of digital communication has intensified opinion exchanges across languages and cultures on social media, enriching public discourse while also increasing the risk of polarization that deepens social divisions. Conventional sentiment analysis methods that rely on translation often distort meaning, overlook emotional nuances, and fail to capture rhetorical devices such as irony and sarcasm, thereby limiting their reliability in multilingual contexts. This study examines the capability of XLM-RoBERTa, a multilingual transformer model pretrained on more than 100 languages, to address these challenges by generating consistent semantic representations and accommodating linguistic and cultural diversity without translation. The research employs bibliometric analysis using VOSviewer on 357 Scopus-indexed publications from 2020 to 2025 to map research trends, combined with a literature review that evaluates XLM-RoBERTa in sentiment and opinion analysis. The findings reveal that although XLM-RoBERTa has been widely employed for sentiment classification, text categorization, and offensive language detection, research explicitly focused on multilingual opinion polarization remains limited. Benchmark evaluations further indicate that XLM-RoBERTa surpasses earlier multilingual models, achieving 79.6% accuracy on XNLI and an 81.2% F1-score on MLQA, confirming its robustness in capturing semantic nuances, cultural variations, and rhetorical complexity without translation. The novelty of this research lies in integrating trend-mapping with methodological evaluation, thereby establishing XLM-RoBERTa as a reliable framework for real-time monitoring of global public opinion, supporting evidence-based policymaking, and advancing scholarly understanding of multilingual communication dynamics in the digital era.
HYBRID INTEGRATION OF BERT AND BILSTM MODELS FOR SENTIMENT ANALYSIS Tambunan, Nicolas Ray Amarco; Saputro, Dewi Retno Sari; Widyaningsih, Purnami
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1719-1730

Abstract

The rapid growth of sentiment analysis research has driven increasing interest in deep learning models, particularly transformer-based architectures such as BERT and recurrent neural networks like BiLSTM. While both approaches have shown substantial success in text classification tasks, each presents distinct strengths and limitations. This study aims to analyze the integration of BERT and BiLSTM models to enhance sentiment classification performance by combining contextual and sequential learning. A bibliometric analysis was conducted using VosViewer based on Scopus-indexed publications from 2020 to 2025, identifying four major thematic clusters related to transformer modeling, recurrent architectures, hybrid integration, and methodological advancements. Comparative findings from benchmark datasets, including SST-2, IMDb, and Yelp Reviews, indicate that hybrid BERT–BiLSTM models achieve superior accuracy compared to single models, reaching up to 97.67% on the IMDb dataset. However, this improvement is associated with increased computational complexity. The proposed framework reinforces the integration between BERT’s contextual embeddings and BiLSTM’s sequential modeling, offering a foundation for developing adaptive, and multilingual sentiment analysis systems. The results highlight future directions in optimizing hybrid architectures for efficiency, cross-lingual adaptability, and domain-specific sentiment understanding.
SMARTPHONE PURCHASING DECISION MAKING USING AN INTERVAL-VALUED INTUITIONISTIC FUZZY AHP APPROACH: A CASE STUDY IN MALANG CITY Hidayat, Noor; Krisnawati, Vira Hari; Abusini, Sobri; Khairi, Desfi Rahmatul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1731-1742

Abstract

The advancement of communication technology, particularly in the smartphone industry, has significantly influenced consumer purchasing behavior. This study aims to analyze the priority criteria in smartphone purchasing decisions using the Interval-valued Intuitionistic Fuzzy Analytic Hierarchy Process (IVIF-AHP) method. Data was collected through interviews with three experts in the smartphone industry in Malang City. The analysis results showed that camera quality had the highest weight of , followed by RAM/storage capacity with a weight of , and the multiple SIM feature with a weight of . Although battery life and pricewere also considered, they had lower weights of and , respectively. These findings indicate that consumers prioritize features and quality over price. The application of the IVIF-AHP method allows handling uncertainty and produces more realistic priority weights that can be directly applied in marketing decision-making. This study also provides strategic implications for smartphone manufacturers: focus on promoting camera features and device performance, and consider the multiple-SIM feature in specific markets. In the future, adding other criteria, such as brand or screen size, could provide more comprehensive insights into decision-making.
COMPARISON OF LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK MODELS FOR PREDICTING FISH CATCH VOLUME IN URENG VILLAGE, CENTRAL MALUKU Kasriana, Kasriana; Ode, Rasid; Lukman, Eryka; Henaulu, Agung K.
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1743-1756

Abstract

This study aims to develop a predictive model for fish catch volume in Ureng Village, Central Maluku, using a mathematical modeling approach based on artificial intelligence with the Scikit-Learn and TensorFlow libraries. The research dataset consists of 24 monthly data records collected from July 2024 to June 2025. The data were obtained through a combination of primary and secondary collection methods. Primary data were gathered through interviews, field observations, and fishermen’s catch records, while secondary data included oceanographic parameters such as sea surface temperature, weather conditions, and current velocity. Two main models were developed: a linear regression model using Scikit-Learn as the baseline and a neural network model using TensorFlow as the comparator, both trained and evaluated on the same dataset to ensure consistency. The testing results show that the linear regression model produced a Mean Squared Error (MSE) of 0.8821 and a coefficient of determination (R²) of 0.682, while the neural network model achieved an MSE of 0.5423 and an R² of 0.815. These findings indicate that the neural network model is more capable of capturing nonlinear patterns among temperature, weather, and current variables, resulting in higher prediction accuracy than the linear model. Nevertheless, this study is limited by the relatively small sample size and the need for a more detailed description of the data period and measurement units to allow a more objective evaluation of the model’s performance. Overall, this AI-based approach has the potential to support more efficient, adaptive, and sustainable decision-making in fishery planning for coastal communities.
HYBRID SES-LSTM RECURRENT NEURAL NETWORK MODEL FOR TIME SERIES FORECASTING OF ELECTRICITY EXPENDITURE IN A UNIVERSITY Nadas Treceñe, Jasten Keneth De las; Barbosa, Reynalyn O.
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1757-1774

Abstract

Efficient energy management has become a critical concern across all sectors due to rising costs and sustainability imperatives. In universities, electricity expenditure represents a substantial share of operational budgets, prompting the need for accurate forecasting models to support financial planning and sustainability initiatives. This study proposed a hybrid forecasting model integrating Simple Exponential Smoothing (SES) and Long Short-Term Memory (LSTM) networks to predict monthly electricity expenditure in a university setting. SES acts as a linear smoothing operator, emphasizing recent trends, while LSTM serves as a nonlinear sequence learner capable of modeling long-term dependencies. The hybrid formulation embeds SES forecasts as auxiliary input features to LSTM, thereby balancing interpretability with predictive power. A dataset of 60 monthly electricity expenditure observations (2019–2023) from Eastern Visayas State University–Tanauan Campus was analyzed. The proposed model was compared against classical (SES, ARIMA) and deep learning (LSTM, FB Prophet) approaches. Results show that the hybrid model achieved superior performance (RMSE = 33760.68, MAPE = 32.32%, MAE = 24580.12), with statistical validation through the Diebold-Mariano test, which confirmed significant improvements. Residual and uncertainty analyses demonstrated the model's robustness and practical applicability. The proposed model positioned it as a valuable decision-support tool for energy cost forecasting and risk-aware planning in universities.
RETRACTION NOTICE TO “PID PATH FOLLOWING CONTROL SYSTEM DESIGN ON UNMANNED AUTONOMOUS FORKLIFT PROTOTYPE” [BAREKENG: J. Math. & App., vol. 18(4), pp. 2093-2112, Dec. 2024] Herlambang, Teguh; Nurhadi, Hendro; Indriawati, Katherin; Akbar, Reza Maliki
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1791

Abstract

This article has been retracted: please see Elsevier’s Article Correction, Retraction and Removal Policy (https://www.elsevier.com/about/policies-and-standards/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief and the authors. After publication, the Editorial Office received documented communications indicating concerns about the provenance, authorship/attribution, and permission to publish the content. The journal’s assessment concluded that the published paper exhibits substantial overlap with an earlier academic work (a prior thesis deposited in an institutional repository) and that the reuse/publication of that material was not properly authorized and/or was insufficiently disclosed and attributed during the submission process. These matters fall within circumstances where retraction is appropriate to safeguard the integrity of the scholarly record, including situations involving copyright-related concerns and serious breaches of publication ethics. The retraction is issued to maintain the clarity, transparency, and integrity of the scholarly record. The journal apologizes to readers for any inconvenience caused. All authors have been informed of this retraction and agree to it. Supporting documentation, including signed statements of consent to retract from all authors, is available via a Google Drive folder link that has been provided to the Editorial Office (see documents in this link).

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