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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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OPTIMIZATION OF LIE DETECTION WITH DEEP LEARNING APPROACH USING FUSION METHOD Dewi Kusumawati; Fitriyanti Andi Masse; Wulan Wulan
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/barekengvol20iss3pp2587-2600

Abstract

In lie detection, early fusion methods that combine information from multiple modalities, such as images and sounds, are used. To improve performance, a lie detection system is designed using mean fusion techniques. The feature extraction method, which uses Optical Flow (OF) and GaussianBlur, uses image data as input. This process generates facial feature change data as numeric values, enabling more efficient processing and allowing the model to be trained quickly and effectively. Evaluation of the model with accuracy, precision, recall, and F1 score using 10 (Fold) cross-validation using a Convolutional Neural Network (CNN) architecture to find features associated with lying in visual content. At the same time, voice signals are studied through voice signal processing and voice feature extraction methods using Mel Frequency Cepstral Coefficients (MFCC) feature extraction and classification using Mel Frequency Cepstral Coefficients (LSTM). The purpose of this process is to discover lying patterns through the audio module. The mean fusion model combines the decisions of multiple lie detection models for each modality, enabling the system to leverage the strengths of each modality to create a broader feature representation. The dataset used contains images, and voice is used for performance evaluation. This dataset can show various lying situations and contexts. The experimental results show that the fusion method using the mean fusion model achieves a lie detection accuracy of 99% and an F1-Score of 0.99. In the context of lying, this research helps develop a more comprehensive and reliable lie-detection system model. The main contribution of this work is a measurable multimodal fusion strategy that integrates pupil-based facial landmarks and temporal voice features, yielding an accuracy improvement of over 14% compared to unimodal baselines.
ENHANCING E-COMMERCE REVIEW SENTIMENT ANALYSIS WITH LINEAR SVM: FEATURE-EXTRACTION AND HYPERPARAMETER COMPARISONS Fauziah Hanum; Richi Andrianto; Anita Sri Rejeki Hutagaol; Nurhanna Harahap; ibnu Rasyid munthe
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/barekengvol20iss3pp2575-2586

Abstract

Sentiment analysis of e-commerce reviews is essential for understanding customer perceptions and supporting service and marketing decisions. However, previous SVM-based studies often report results using only one feature representation or one tuning approach, which provides limited guidance on the most effective practical configuration. This study addresses this gap by benchmarking a linear Support Vector Machine across TF IDF and Word2Vec representations and comparing three hyperparameter tuning strategies, Grid Search, Random Search, and Optuna, on an Indonesian language dataset of customer product reviews. The held-out test set contains 871 reviews, while class imbalance in the training data is handled by applying SMOTE only on the training set, resulting in a balanced training set of 2902 samples. Using stratified validation with Accuracy, Precision, Recall, F1 score, and ROC AUC, the best configuration is TF IDF with Optuna-tuned linear SVM, achieving 86.68 percent accuracy, an F1 score of 0.87, and ROC AUC of about 0.93 to 0.94. For Word2Vec, the best result is obtained with Random Search, reaching 84.38 percent accuracy, an F1 score of 0.84, and an ROC AUC of about 0.92. These findings indicate that TF-IDF is a stronger match for linear SVM in this setting, and that Optuna provides the most consistent gains for TF-IDF. Limitations include the use of binary sentiment labels and an evaluation focused on linear SVM with simple document-level Word2Vec aggregation, so performance may differ across other domains, platforms, and languages. Future research will examine richer document embeddings, nonlinear and contextual models, multi-class or aspect-level sentiment, and broader cross-platform validation to improve generalizability.
OPTIMIZATION OF DISASTER RELIEF LOGISTICS DISTRIBUTION USING THE FUZZY TRANSPORTATION PROBLEM MODEL Ihda Hasbiyati; Hasriati Hasriati; Harison Harison; Maimunah Maimunah; Aziza Masli; Ahriyati Ahriyati
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/barekengvol20iss3pp2561-2574

Abstract

The distribution of disaster relief logistics faces significant challenges due to uncertainty in demand, supply constraints, and accessibility constraints in affected areas. The novelty of this study lies in integrating trapezoidal fuzzy numbers to represent uncertainty in disaster logistics, thereby offering a more realistic model than conventional deterministic models. This study proposes developing a fuzzy-logic-based transportation model to optimize logistics resource allocation. The model was applied to a disaster relief distribution scenario with five source locations and five destination points. The model is solved using the Vogel Approximation Method and optimality test using the Simplex Transportation Method. Next, to determine the distribution route that minimizes costs and distance, a simulation was conducted in MATLAB. The results show that the fuzzy transportation problem model produces more efficient distribution solutions than conventional transportation models, which can be used only for certain data. However, this study is limited to single-objective cost minimization using simulated data. Therefore, future research should consider applying multi-objective optimization to minimize both distribution cost and time simultaneously using real-time geospatial data.
IMPLEMENTATION OF CUCKOO SEARCH-BASED ENSEMBLE VARIABLE IMPORTANCE IN THE CLASSIFICATION OF NON-CASH FOOD ASSISTANCE (BPNT) RECIPIENTS IN WEST JAVA Indra Mahib Zuhair Riyanto; Laras Suprapti; Salsabila Fayiza; Elke Frida Rahmawati; Farid Yafi Suwandi; Sachnaz Desta Oktarina; Rahma Anisa
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/barekengvol20iss3pp2601-2612

Abstract

The BPNT program is a government initiative to efficiently distribute social assistance to poor households. However, the challenge of achieving accurate recipient identification remains a major obstacle. This research aims to build a classification model for BPNT recipients in West Java using machine learning methods (Random Forest, XGBoost, CatBoost, and LightGBM) and a Cuckoo Search-Based Ensemble Variable Importance (EVI) approach to identify which predictors most strongly affect classification. Class imbalance in the response data was addressed through weighting during model training, and performance was evaluated using balanced accuracy through 10-fold cross-validation. Although all models performed well, the variable importance results varied across models. Using the Random-Key Cuckoo Search algorithm, an EVI ranking was generated that integrated VI rankings from each model, achieving a minimum Spearman correlation of 0.6538. The results show that roof quality, living status, calorie consumption, and per capita expenditure are the main indicators for classifying BPNT recipients. This approach shows great potential to improve modeling interpretability and to provide stronger data-driven support for social policy-making.
NATURAL DISASTER REPORT ON SOCIAL MEDIA CLASSIFICATION METHOD BASED ON WORD EMBEDDING AND GRAPH ATTENTION NETWORK Mohammad Reza Faisal; Irwan Budiman; Dodon Turianto Nugrahadi; Muhammad Rafi; Mera Kartika Delimayanti; Luu Duc Ngo; Moses Okechukwu Onyesolub
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/barekengvol20iss3pp2613-2630

Abstract

Natural disasters frequently occur unexpectedly and seriously threaten human safety and infrastructure. Traditional detection systems rely heavily on IoT sensors and satellite monitoring, which are often costly and less accessible in resource-limited or remote areas. In contrast, social media provides a rich and real-time source of information, as users frequently post eyewitness reports during disaster events. However, automatically classifying these posts into relevant disaster categories remains challenging due to the short and informal nature of the text. The research aims to develop a high-performing classification model for disaster-related tweets using graph-based neural architectures and structured word embedding representations. The method used is a comparative implementation of Graph Convolutional Network (GCN) and Graph Attention Network (GAT) models, with input constructed by concatenating vectors from three word embedding techniques—Word2Vec, FastText, and GloVe—across seven multilingual datasets. The result of this study is that GAT outperformed GCN in all scenarios, with FastText embeddings yielding the highest individual performance. In contrast, combined embeddings sometimes led to performance degradation due to redundancy. The average F1-score for GCN is 0.749, while GAT achieves 0.915. The research conclusions indicate that GAT with word embedding input provides a novel and effective multilingual disaster tweet classification framework, offering valuable insights for future AI-based natural disaster monitoring systems.
A COMPARATIVE ANALYSIS OF COLOR SPACES FOR TOMATO RIPENESS CLASSIFICATION USING MACHINE LEARNING AND DEEP LEARNING APPROACHES Firda Fadri; Yoyok Yulianto; Kiswara Agung Santoso
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/barekengvol20iss3pp2631-2644

Abstract

The classification of tomato ripeness is crucial for post-harvest processing, quality assurance, and agricultural automation, as manual evaluation is often subjective, inconsistent, and time-consuming. This research investigated the impact of color space selection and hyperparameter optimization on tomato ripeness classification using machine learning (SVM, Random Forest, K-NN, GNB) and deep learning (CNN) approaches. Evaluation results indicated that YCbCr was the best-performing color space for classical models, with SVM achieving the highest accuracy (91.24%) and RF following closely (89.54%), whereas HSV yielded optimal performance for CNN (90.46%), highlighting differences in feature extraction mechanisms. Confusion matrix and ROC curve analyses demonstrated that models capturing nonlinear and interdependent color features, such as SVMs and CNNs, achieved superior class separability, particularly for the Ripe and Unripe classes. Dominant channel analysis revealed that chrominance channels, Cb in YCbCr and H in HSV, played a critical role in ripeness discrimination. These findings emphasized the importance of preprocessing for feature selection and provided guidance on selecting appropriate models and color spaces to improve the accuracy and reliability of automated tomato ripeness classification.
EVALUATING ROBERTA AND GPT-BASED MODELS FOR SDG MULTICLASS TEXT CLASSIFICATION ACROSS DIFFERENT DOCUMENT LENGTHS Uswatun Hasanah; Agus Mohamad Soleh; Cici Suhaeni; Anwar Fitrianto
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/barekengvol20iss3pp2645-2664

Abstract

Multiclass text classification remains a difficult task, primarily due to semantic ambiguity and differences in input length. This study evaluates RoBERTa and GPT-based models for multiclass text classification, focusing on how prompting strategies and document length affect accuracy and robustness. Experiments were conducted using the OSDG Community Dataset, which contains approximately 15,000 labeled samples. The dataset was partitioned into four subsets based on input length: short, medium, long, and all combined. Three GPT variants (zero-shot, few-shot, and fine-tuned) were compared against a RoBERTa baseline. Fine-tuning was implemented via OpenAI’s supervised API with prompt-response formatting. Performance was assessed through F1-score, precision, recall, and balanced accuracy. Fine-tuned GPT achieved the strongest results in all settings, with a macro F1-score of 0.9204 on the all-combined dataset, representing a 4.61% improvement over RoBERTa. Consistent gains were also observed across short (8.63%), medium (3.83%), and long (20.31%) texts. The largest improvement occurred on long documents, while medium-length inputs provided the most stable performance across models. These findings highlight the effectiveness of task-specific fine-tuning in enhancing GPT’s capability to classify SDG-related texts across diverse input lengths.
GENE SELECTION FOR TYPE 2 DIABETES MELLITUS (T2DM) DISEASE USING MULTIPLE SUPPORT VECTOR MACHINE – RECURSIVE FEATURE ELIMINATION (MSVM-RFE) ALGORITHM Andi Khalil Gibran Basir; Ahmad Husain; A. Fuad Ahsan Basir
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/barekengvol20iss3pp2665-2680

Abstract

Gene selection is essential for improving classification performance and interpretability in high-dimensional microarray data. This study applies a Multiple Support Vector Machine–Recursive Feature Elimination (MSVM-RFE) framework for gene selection in Type 2 Diabetes Mellitus (T2DM). Experiments were conducted on a GEO microarray dataset comprising 118 samples (73 controls and 45 T2DM cases) with 25,770 genes. MSVM-RFE employs multiple linear SVM models within a 10-fold cross-validation scheme as feature selection to enhance accuracy and was evaluated under different train–test splits, with and without SMOTE resampling. The selected gene subsets were classified using SVM with linear, RBF, and polynomial kernels. The best configuration achieved 95.67% accuracy, with high sensitivity, specificity, and AUROC, using fewer than 100 genes. These results demonstrate that MSVM-RFE provides a robust and effective gene selection strategy for T2DM microarray analysis.
COMPARATIVE EVALUATION OF CNN ARCHITECTURES FOR ROAD DAMAGE CLASSIFICATION USING DIGITAL IMAGES IN SLEMAN REGENCY Anggun Puspita Sari; Kariyam Kariyam; Feri Wijayanto; Edy Widodo
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/barekengvol20iss3pp2681-2692

Abstract

Reliable road condition monitoring is fundamental to maintenance decision-making and transportation safety, particularly in regional contexts where data resources are often scarce. This study presents a comparative evaluation of convolutional neural network (CNN) architectures for classifying road damage types using digital images collected in Sleman Regency. Three widely used CNN architectures, VGGNet-16, InceptionV3, and Xception, were evaluated under a unified experimental framework employing transfer learning, consistent preprocessing, explicit hyperparameter tuning, and four-fold cross-validation. The dataset comprises three road damage categories, alligator crack, corrugation, and pothole, captured under heterogeneous pavement and lighting conditions. Image preprocessing includes resizing, augmentation, and contrast enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE). To assess the contribution of preprocessing choices, an ablation study was conducted by comparing model performance with and without CLAHE. Experimental results indicate that all evaluated architectures achieve high classification performance. Among them, Xception consistently demonstrates the best overall performance across validation and test sets, achieving the highest accuracy, precision, recall, and F1-score. The ablation analysis further shows that including CLAHE consistently improves performance, particularly in recall and F1-score, indicating enhanced robustness under uneven illumination conditions. Although the contribution of this study is incremental rather than algorithmically novel, the findings provide empirical insights into the comparative behavior of CNN architectures under region-specific road conditions. The results highlight the importance of systematic preprocessing and controlled evaluation in CNN-based road-damage classification and provide practical guidance for selecting suitable architectures for regional road maintenance decision-support systems.

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