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Journal of Mathematics and Applied Statistics
Published by Yayasan Insan Literasi Cendekia Indonesia
ISSN : -     EISSN : 29874548     DOI : -
Core Subject : Education,
Mathematics
Journal of Mathematics and Applied Statistics (ISSN: 2987-4548) is scientific, peer-reviewed, and open access journal managed by Yayasan Insan Literasi Cendekia (INLIC) Indonesia. Published twice a year on June and December. Mathstat publishes original research and/or library analysis on Mathematics and Statistics. This journal is useful to researchers, engineers, scientists, teachers, managers and students who are interested in keeping a track of original research and development work being carried out in the broad area of Mathematics and Statistics.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 25 Documents
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Prediction of Domestic and Foreign Tourist Visits Using the Long Short-Term Memory (LSTM) Method Afnidia, Tria; Herlina Putri, Novi; Soraya, Siti; Firmansyah, Firmansyah
Journal of Mathematics and Applied Statistics Vol. 3 No. 1 (2025): June 2025
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v3i1.286

Abstract

The tourism sector makes an important contribution to supporting regional economic growth. Among the various provinces in Indonesia, West Nusa Tenggara (NTB) stands out as one of the main tourist destinations that has shown a fairly rapid increase in the number of tourist visits in recent years. This study uses Adam optimization and gradient clipping techniques to predict domestic and foreign tourist visits in NTB using the Long Short-Term Memory (LSTM) method. Monthly historical data for the period 2014–2023 from the NTB Tourism Office was processed through Min-Max Scaling normalization and divided with a ratio of 70:30 and 80:20. The LSTM model with a 4-layer architecture (2 LSTM layers with 50 units and 2 Dense layers) was tested using the Root Mean Squared Error (RMSE) metric. Based on the results obtained, the best configuration was shown at a ratio of 70:30 with 200 epochs, producing the lowest RMSE of 66.70 on the training data and 33.24 on the testing data. This implies that the model can capture seasonal patterns and visit trends, although it is less responsive to outliers such as natural disasters. This implementation provides a basis for tourism capacity planning and data-based destination management.
Analysis of Factors Influencing Chili Production Using the Spatial Regression Method Ananda, Laraswati; Gusnayanti, Riski; Ratmaji, Muji; Firmansyah, Firmansyah; Soraya, Siti
Journal of Mathematics and Applied Statistics Vol. 3 No. 1 (2025): June 2025
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v3i1.287

Abstract

Chili pepper production in West Nusa Tenggara is the second largest producer in Indonesia. However, the distribution of production still shows a significant imbalance between regions. This imbalance can affect the stability of supply and prices both locally and nationally. This study aims to examine the factors that influence chili pepper production in NTB using the spatial regression method. The data used are secondary data from agricultural statistics in 2024, which cover ten districts/cities. The SEM model was chosen because it can identify the effects that occur between geographically close regions, which are often not visible in traditional regression models. The results of the analysis show that the area of ​​harvested land and the level of chili pepper productivity have a significant effect on the total production at a real level of 10%, with coefficients of 96.6132 and 44,385.5, respectively. The lambda value reaching 1.6667 provides support for evidence of positive spatial autocorrelation between regions. The SEM model also showed a lower AIC value (316.58) compared to the classical regression model, indicating that this model is more efficient and accurate.
Designing a Web Application for Recognizing Past Learning Using the Laravel Framework Jaya, Arsan Kumala; Hanif, Abdullah; Triadi, Fara; Biabdillah, Fajerin
Journal of Mathematics and Applied Statistics Vol. 2 No. 2 (2024): December 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i2.239

Abstract

This study aims to provide information on the application design process using the Laravel framework. This study aims to design a web application that can help higher education institutions manage students who take prior learning recognition (RPL) classes effectively and efficiently. The problem often faced by universities is the difficulty in recording the formal/non-formal education history of RPL students. This application is expected to provide a solution by providing features such as recording education history, training history, conference history, award history, organizational history, and employment history. The system development method used in the design is the System Development Life Cycle (SDLC) by utilizing the Laravel framework as a framework for the system development process. The expected results of this study are a web application that is user-friendly, reliable, and able to increase the efficiency of student data collection in universities.
Attention-Enhanced Convolutional Networks for Fine-Grained Batik Motif Classification with Statistical Feature Modeling Abdal, Nurul Mukhlisah; Tangsi
Journal of Mathematics and Applied Statistics Vol. 3 No. 1 (2025): June 2025
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v3i1.309

Abstract

This study examines a hybrid method for classifying fine-grained Indonesian batik motifs under limited data conditions. The research focuses on two objectives: (1) assessing the contribution of attention mechanisms to the extraction of discriminative visual features, and (2) evaluating the role of Gray-Level Co-occurrence Matrix (GLCM) texture descriptors when combined with deep convolutional representations. The proposed approach employs a ResNet-50 backbone equipped with a Convolutional Block Attention Module (CBAM) and integrates second-order GLCM features through a feature-fusion framework. The dataset consists of authentic batik photographs representing 38 motif categories. Model performance is assessed using accuracy, macro-averaged metrics, Cohen’s Kappa, and ablation experiments supported by statistical tests. The model reaches a test accuracy of 75.90%, with a macro F1-score of 0.7598 and a Cohen’s Kappa value of 0.7456. Training and validation curves show stable behavior after the initial epochs. Per-class evaluation indicates that motifs with distinctive structural elements tend to be classified correctly, whereas motifs with subtle or overlapping patterns exhibit lower accuracy. The ablation study records a 4.79% accuracy increase attributed to CBAM and a 3.51% increase associated with GLCM features; both effects fall within statistically significant confidence intervals. The combination of both components yields an 8.38% improvement over the baseline model. Two-way ANOVA identifies main effects for attention and GLCM, with a small interaction term. These results provide information on how spatial attention and statistical texture features contribute to the classification of fine-grained batik motifs within the examined setting.
Understanding Students’ Proportional Reasoning through Contextual Tasks in RME: A Qualitative Perspective etmy, desventri; amalia putri , prismadian; yandi raharjo, Eko; susanta, agus; yensy astuty, nurul
Journal of Mathematics and Applied Statistics Vol. 3 No. 1 (2025): June 2025
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v3i1.312

Abstract

This study aims to describe students’ mathematical reasoning processes within the framework of Realistic Mathematics Education (RME), particularly focusing on how learners develop proportional and logical reasoning through contextual tasks. Using a qualitative case study design, the research involved classroom observations, task-based interviews, and analysis of students’ written work collected from two purposively selected university students. Data were examined through thematic analysis to identify patterns of reasoning related to multiplicative strategies, symbolic manipulation, and the use of contextual or visual representations. The findings indicate that both students demonstrated strong procedural and symbolic proficiency, especially in applying logical laws and performing formal transformations; Student A showed consistent vertical mathematization, while Student B provided partial conceptual explanations but lacked systematic justification. However, neither student exhibited meaningful horizontal mathematization, contextual interpretation, nor the use of emergent models characteristic of RME-based learning. Their reasoning predominantly reflected imitative-procedural approaches rather than creative or relational reasoning, suggesting that their learning experiences did not sufficiently support guided reinvention or conceptual development through realistic contexts. These results highlight the need for instructional designs that integrate contextual tasks, visual models, and scaffolding aligned with RME principles to foster deeper conceptual understanding and more flexible reasoning. Keyword : Proportional Reasoning; Realistic Mathematics Education (RME); Mathematical Reasoning.

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