cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Andri Putra Kesmawan
Contact Email
andriputrakesmawan@gmail.com
Phone
+6281990251989
Journal Mail Official
journal@idpublishing.org
Editorial Address
Perumahan Sidorejo, Jl. Sidorejo Gg. Sadewa No.D3, Sonopakis Kidul, Ngestiharjo, Kapanewon, Kasihan, Kabupaten Bantul, Daerah Istimewa Yogyakarta 55184
Location
Kab. bantul,
Daerah istimewa yogyakarta
INDONESIA
Jurnal Pendidikan Matematika
Published by Indonesian Journal Publisher
ISSN : -     EISSN : 30309263     DOI : https://doi.org/10.47134/ppm
Core Subject : Education,
Education Mathematics Other
Jurnal Pendidikan Matematika ISSN 3030-9263 is a scientific journal published by Indonesian Journal Publisher. This journal publishes four issues annually in the months of November, February, May, and August. This journal only accepts original scientific research works (not a review) that have not been published by other media. The focus and scope of Jurnal Pendidikan Matematika include mathematics learning strategies, mathematics learning design, development of mathematics learning tools, analysis in the field of mathematics education, and various things related to mathematics learning from elementary school to college level.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 60 Documents
 2   3   4   5   6 
Implementasi Etnomatematika dalam Pembelajaran Matematika menggunakan Budaya Jawa Berbasis Batik Famella, Ajeng; Panggabean, Ellis Mardiana; Harahap, Tua Halomoan
Jurnal Pendidikan Matematika Vol. 2 No. 4 (2025): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v2i4.1944

Abstract

Penelitian ini bertujuan untuk mendeskripsikan implementasi etnomatematika dalam pembelajaran matematika dengan menggunakan budaya Jawa, khususnya motif batik, pada siswa tingkat SMA. Metode yang digunakan adalah studi literatur dengan pendekatan deskriptif kualitatif, mengkaji berbagai referensi ilmiah dan terpercaya yang relevan dengan teori serta model pembelajaran etnomatematika. Subjek dalam penelitian ini adalah 28 siswa SMA Islam Plus Adzkia. Hasil kajian menunjukkan bahwa integrasi unsur-unsur matematika dalam motif batik, seperti simetri, transformasi, dan pola geometri, dapat meningkatkan minat belajar, pemahaman konsep, serta memperkuat identitas budaya siswa. Selain memperkaya strategi pembelajaran, penelitian ini juga berkontribusi dalam mengharumkan nama SMA Islam Plus Adzkia sebagai lembaga pendidikan yang aktif mengembangkan inovasi pembelajaran berbasis budaya lokal.
Teaching Probability Theory and Mathematical Statistics with Practical Problems Yusubjanova Musharraf Tursunali Kizi
Jurnal Pendidikan Matematika Vol. 2 No. 4 (2025): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v2i4.1969

Abstract

This article is devoted to the role, content, and opportunities for effective teaching of elements of Probability Theory and Mathematical Statistics in the mathematics curriculum of general secondary education. It reveals the necessity of explaining the fundamental concepts of school-level probability theory—such as event, event probability, and its definitions, as well as probability calculation—through practical problems, and provides relevant problem examples. In the international PISA assessment test, questions related to this subject are also included, and students from our country have shown low performance specifically in these types of questions. This indicates that a special approach is needed for teaching this subject. In other words, in teaching the subject, it is necessary to increase students' interest through practical problems, strengthen their knowledge, and ensure that the lesson process is conducted effectively. In conclusion, it can be stated that the use of real-life, interdisciplinary, and integrated problems by the teacher in the lesson process serves to increase the effectiveness of the lesson, engage more students in the learning process, and develop their logical, statistical, and probabilistic thinking
A Comparative Study of Resampling Techniques for Handling Class Imbalance in Binary Classification Habash, Hussein Kareem
Jurnal Pendidikan Matematika Vol. 2 No. 4 (2025): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v2i4.1990

Abstract

Class-imbalance skews most binary classifiers toward the majority class, hiding the very events that matter (e.g., fraud and malignancy). We present a clear, quick-to-replicate comparison of four representative resampling families—Random Over-Sampling (ROS), SMOTE, the hybrid SMOTE-ENN cleaner, and the ensemble balancer EasyEnsemble—paired with two widely used learners (Logistic Regression and Random Forest). Experiments run on two public tabular benchmarks that span extreme (0.17 % fraud) and moderate (2.3 % cancer) skew. A simple two-fold stratified split replaces heavy cross-validation, and each model is evaluated on the two metrics that matter most under imbalance: AUROC and PR-AUC. Results finish in under ten minutes on any laptop yet reproduce the qualitative hierarchy seen in much larger studies: SMOTE-ENN attains the best PR-AUC on both datasets, EasyEnsemble leads AUROC, and naïve ROS trails in every case. Three visuals—(i) an end-to-end pipeline schematic, (ii) a one-glance bar chart of class ratios, and (iii) a radar plot of mean PR-AUC scores—make the findings transparent at first sight. All code and figures come in a single Jupyter notebook (supplementary ZIP); running one command installs dependencies, and a second command reproduces every number and image. This streamlined study offers practitioners an evidence-based starting point while remaining fully reproducible for reviewers and students alike.
Nonlinear Programming Models for Robust Queuing Systems under Fuzzy Sets Ahmed, Hasanain Hamed
Jurnal Pendidikan Matematika Vol. 2 No. 4 (2025): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v2i4.1991

Abstract

This paper proposes a procedure for constructing the membership functions of performance measures in finite-capacity queuing systems where both arrival and service rates are represented as fuzzy numbers. By applying the (\alpha)-cut method, a fuzzy queue with finite capacity is transformed into a family of conventional crisp queues, allowing for more precise modeling of system characteristics. The study focuses on a queuing model with an unreliable server, where key parameters such as service and breakdown rates are fuzzy values. The developed parametric nonlinear programming approach facilitates the derivation of new constraints, providing a robust framework for analyzing queuing behaviors under uncertainty. The findings demonstrate that the proposed fuzzy mathematical model yields more realistic outcomes than traditional crisp models, thereby enhancing the applicability of queuing theory in practical scenarios.
Optimizing Finite Difference Schemes for Partial Differential Equations Naseef, Qasim Hashim
Jurnal Pendidikan Matematika Vol. 2 No. 4 (2025): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v2i4.1992

Abstract

Effective numerical methods for solving partial differential equations (PDEs) are finite difference (FD) approaches used in many fields including heat transfer, fluid dynamics, and environmental sciences. Breaking the continuous domain in both space and time, these methods convert partial differential equations into sets of algebraic equations solvable repeatedly. The time step and grid resolution—which must be carefully selected to balance computational accuracy and efficiency—will define FD techniques. Like adaptive mesh refinement (AMR), adaptive methods dynamically alter the grid in areas of rapid solution changes to improve accuracy without adding computational expense. Especially in explicit approaches for FD, where the Courant Friedrichs Lewy (CFL) condition controls stability, it is a critical consideration. Higher stability of implicit methods results from more numerically demanding Since actual challenges frequently involve complex geometries and nonlinear dynamics, FD methods have to be modified for Multiphysics simulations with fluid-structure interactions and coupled heat-mass transfer applications. Future developments in FD techniques center on developing more efficient algorithms to manage multiscale, Multiphysics problems, therefore ensuring accuracy while lowering computer load.
Statistical Challenges in Spatial Data Analysis: The Role of Kriging Models Farhan, Ammar
Jurnal Pendidikan Matematika Vol. 2 No. 4 (2025): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v2i4.2011

Abstract

Using Kriging models, a complex geostatistical technique for extrapolating and forecasting unknown spatial values based on known data, this study investigates spatial data analysis. Traditional statistical techniques that suppose observations to be independent are considerably challenged by spatial autocorrelation—the tendency for nearby spatial points to show comparable features. The research highlights the application of Kriging to environmental data, especially air quality measurements like PM2.5 concentrations, in order to better comprehend and forecast pollution patterns over several geographical areas. Using both Ordinary and Universal Kriging approaches, the research shows how these methods can efficiently address spatial dependencies, nonstationarity (where data characteristics change across space), and anisotropy (directional spatial variability). Moreover, the research combines Kriging with machine learning algorithms to record more sophisticated spatial interactions, therefore enhancing prediction accuracy. Methods of crossvalidation are used to thoroughly evaluate the models' performance. The study emphasizes how Kriging enables precise spatial predictions, hence giving important information for environmental monitoring and well-informed decision-making.
Recursive Algorithms Preserving Properties in Constrained Geometric Graphs Hussein, Sami Nazim; Tamr, Sabah Abdullah
Jurnal Pendidikan Matematika Vol. 2 No. 4 (2025): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v2i4.2048

Abstract

This article discusses recursive algorithms to construct geometric t-spanners with structural properties such as planarity, unit-disk adjacency, and locally bounded edge crossings. A geometric t-spanner is a sparse subgraph that bounds the distances of the initial geometric graph within a factor t≥1. The suggested recursive approach adopts a three-phase process: (1) Decomposition — the set of vertices is divided into clusters through topological or geometric separators; (2) Local construction — for every cluster, a local spanner is constructed subject to strictly enforcing geometric constraints; and (3) Merging — a sparse set of inter-cluster edges is added in order to link clusters into a global spanner. The model ensures low stretch, bounded degree, and global connectivity at the minimum total number of edges.We demonstrate the scheme through an example of a quadtree-based decomposition where the 2D Euclidean plane is recursively partitioned into subregions that contain a bounded number of vertices. Figures indicate how local spanners and inter-cluster links are combined to form a global structure that closely approximates Euclidean distances and is planar and degree-constrained. The recursive construction is distributable, scalable, and can be used in spatial networks such as wireless sensor systems, road infrastructures, and robotic motion.
Beyond Point Estimates: Bayesian Deep Nonparametric Regression with Rigorous Uncertainty Quantification Iskander, Hasan Mohammed
Jurnal Pendidikan Matematika Vol. 2 No. 4 (2025): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v2i4.2052

Abstract

Uncertainty quantification is essential in regression tasks where predictions inform high-stakes decisions. We present a practical framework for Bayesian deep nonparametric regression that moves beyond point estimates to deliver calibrated predictive intervals and uncertainty decomposition. The approach employs a heteroscedastic Bayesian neural network trained via Monte Carlo Dropout, enabling the estimation of both epistemic and aleatoric uncertainties without costly Markov chain Monte Carlo sampling. We evaluate the method on a synthetic heteroscedastic regression problem, demonstrating accurate predictive means, well-calibrated 90% prediction intervals, and computational efficiency on CPU-only hardware. The results highlight the method’s suitability for uncertainty-aware regression in resource-constrained settings, and all code is released for reproducibility.
Employing Artificial Intelligence Algorithms to Estimate the Hazard Function of the Inverse Gompertz Distribution with a Practical Application Jawad Noah Sulaiman, Qasim Muhammad
Jurnal Pendidikan Matematika Vol. 2 No. 4 (2025): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v2i4.2053

Abstract

Environmental pollution is one of the most important and serious problems facing humanity today, due to its direct impact on the health of humans and other living organisms. In recent years, an increase in environmental pollution rates has been observed, significantly impacting human health and leading to the emergence of numerous diseases, such as cancer, pneumonia, poisoning, birth defects, and others. Given the importance and seriousness of the issue and its direct impact on human life, this research was conducted to determine the percentage of pollution caused by two of the most important factors in air pollution, CO2. This research was conducted based on the explanatory variables: average temperature, average dew point, average humidity, average wind speed, and the average amount of crude oil used in the refining process. In this research, the risk function of the inverse Gompertz model was estimated using artificial intelligence algorithms, namely the genetic algorithm. These methods were applied to air pollution data obtained from the Central Refineries Company in Baghdad (Dora Refinery), which represents daily measurements of environmental pollution compounds based on time for the period from 2019 to 2025.
Preconditioning Techniques in Krylov Subspace Methods Najm, Zina Jabbar
Jurnal Pendidikan Matematika Vol. 2 No. 4 (2025): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v2i4.2054

Abstract

This study discusses preconditioning approaches to address large, sparse linear systems as well as Krylov subspace methods. Among others, computational fluid dynamics, structural analysis, and electromagnetic simulations use Krylov methods like the Conjugate Gradient (CG) and Generalized Minimal Residual (GMRES). These techniques use iterative approximations that approach to the solution by projecting the problem onto a Krylov subspace. The efficiency of Krylov methods is greatly influenced by the selection of preconditions, which help the system's conditioning and so accelerate convergence. Jacobi Preconditioning, Incomplete LU Decomposition (ILU), and Multigrid Preconditioning are examples of preconditioning techniques. Though it has advantages, preconditioning has disadvantages including choosing the proper conditions and controlling memory and costing calculations. Further investigated were possible changes including adaptive and nonlinear preconditioning as well as the integration of Artificial Intelligence

Page 5 of 6 | Total Record : 60

 2   3   4   5   6 

Filter by Year

2023 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 1 (2025): November Vol. 2 No. 4 (2025): August Vol. 2 No. 3 (2025): May Vol. 2 No. 2 (2025): February Vol. 2 No. 1 (2024): November Vol. 1 No. 4 (2024): August Vol. 1 No. 3 (2024): May Vol. 1 No. 2 (2024): February Vol. 1 No. 1 (2023): November