cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota mataram,
Nusa tenggara barat
INDONESIA
JTAM (Jurnal Teori dan Aplikasi Matematika)
Published by Universitas Muhammadiyah Mataram
ISSN : 25977512     EISSN : 26141175     DOI : 10.31764/jtam
Core Subject : Education,
Mathematics
Jurnal Teori dan Aplikasi Matematika (JTAM) dikelola oleh Program Studi Pendidikan Matematika FKIP Universitas Muhammadiyah Mataram dengan ISSN (Cetak) 2597-7512 dan ISSN (Online) 2614-1175. Tim Redaksi menerima hasil penelitian, pemikiran, dan kajian tentang (1) Pengembangan metode atau model pembelajaran matematika di sekolah dasar sampai perguruan tinggi berbasis pendekatan konstruktivis (PMRI/RME, PBL, CTL, dan sebagainya), (2) Pengembangan media pembelajaran matematika berbasis ICT dan Non-ICT, dan (3) Penelitian atau pengembangan/design research di bidang pendidikan matematika, statistika, analisis matematika, komputasi matematika, dan matematika terapan.
Arjuna Subject : -
Articles 27 Documents
 1   2   3 
Search results for , issue "Vol 9, No 3 (2025): July" : 27 Documents clear
Teaching Strategy: Adaptive Mathematics Learning with Liveworksheets Platform Sariningsih, Ratna; Kusuma, Jaka Wijaya; Linda, Linda
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.30072

Abstract

Technology is a supporting asset for the implementation of the teaching and learning process in educational institutions, and its application can be studied through research. This study uses a qualitative descriptive method with the aim of identifying the application of adaptive learning in mathematics learning with the help of Artificial Intelligence (AI) in the digitalization of mathematics education course.  This study aims to identify the application of adaptive learning in mathematics instruction with the assistance of Artificial Intelligence (AI) in the course on the digitalization of mathematics education. The research was developed using the design thinking method (empathize, define, ideate, prototype, and test), resulting in a product consisting of teaching tools (media, assessments, and student worksheet) independently created by students of class B1 Mathematics Education at IKIP Siliwangi. The instruments used were validation sheets included in form applications and polls on the WhatsApp application. The validation and poll results were analyzed using real-time techniques with Apache Spark. The teaching tools in this study were validated by practitioners and tested on a group of students. Based on the practitioner’s validation and evaluations results, and student tests, the AI-assisted teaching tools for the digitalization of mathematics education course were deemed suitable for use and beneficial in the teaching and learning process.
Mathematical Modeling of Student Learning Outcomes using E-learning-Based Remedial Programs Fadhiliani, Dwi; Umam, Khairul; Suhartati, Suhartati; Johar, Rahmah
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.31404

Abstract

Research on mathematical modeling of learning outcomes remains limited, despite its potential to evaluate educational processes and inform placement decisions in schools, classes, learning resources, and remedial programs. This quantitative study aims to construct a mathematical model in the form of an ordinary differential equation (ODE) to represent the dynamics of students’ learning in a remedial program. The model is developed using empirical data obtained from multiple-choice diagnostic tests designed to identify common learning difficulties and a series of three remedial assessments conducted after e-learning-based interventions. The dataset includes students’ assessment scores and time records during the remedial learning process. The Nelder–Mead method was used to estimate the model parameters, followed by a stability analysis and an RMSE-based evaluation of the model’s accuracy. The model captures changes in student understanding over time and reveals that students with lower initial scores tend to show greater improvements through remedial programs. However, as the duration of the remedial program increases, the rate of score improvement decreases—suggesting a decline in student focus and learning efficiency over time. These findings highlight the nonlinear nature of learning progress in remedial program. The model provides for predicting student outcomes and analyzing the effectiveness of remedial programs. It offers practical implications for optimizing the structure and timing of remedial programs and can support the development of adaptive learning systems tailored to student needs. This research demonstrates the potential of mathematical modeling for decision-making in education.  
Enhancing Students' Statistical Literacy Through Physical Education Long Jump Athletics Learning at Elementary School Arsyad, Rahmatullah Bin; Bakar, Abu; Mardiah, Ainun; Al-lahmadi, Nazmi; Basna, Erik Norbertus
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.30090

Abstract

This study aims to improve elementary school students' statistical literacy through Physical Education (PJOK) learning, particularly in the long jump athletic material. The study used a quasi-experimental method with a pre-test post-test control group design. This research was conducted at SD Muhammadiyah 2 Kota Sorong with a total sample of 56 sixth-grade students, divided into two groups: the experimental class applying PJOK learning based on long jump athletics and the control class using a conventional learning model. Data were collected through pre-test, post-test, questionnaires, and observations of student activities. The results showed a significant improvement in statistical literacy in the experimental group compared to the control group. The t-test revealed a significance value indicating a significant difference between the two groups in achieving statistical literacy. The average N-Gain increase in the experimental group categorized as high, while the control group in moderate category. Additionally, students' responses to the learning model were positive. Based on these findings, it can be concluded that PE learning based on long jump athletics is effective in improving elementary students' statistical literacy. This learning model not only enhances conceptual understanding but also develops data analysis skills in real-world contexts.
Spatial Fuzzy Clustering Algorithm for Optimizing Inclusive Da'wah Distribution Patterns Karim, Abdul; Adeni, Adeni; Riyadi, Agus; Mursyid, Achmad Yafik
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.30599

Abstract

This article aims to identify and analyse the potential for inclusive da'wah in Central Java Province, Indonesia, by focusing on three fundamental aspects that determine the success of the da'wah process: the subject, the object, and the environment of the da'wah. This research applies an empirical approach through a series of spatial clustering analyses using the fuzzy geographically weighted clustering (FGWC) method to determine the optimum number of clusters in mapping the potential for da'wah. FGWC is a spatial analysis method that combines the concept of fuzzy clustering with a geographically weighted approach, allowing for more flexible and contextual identification of distribution patterns based on location. This method was chosen for its ability to handle uncertainty in spatial data as well as considering geographical variations in clustering. The data used in this study came from the Ministry of Religious Affairs of the Republic of Indonesia and the Central Statistics Agency (BPS) of Central Java Province, covering demographic, social, and religious data from 35 districts/cities in Central Java. The results of the FGWC analysis show that the optimum number of clusters is two, with districts/cities in the second cluster identified as having higher da’wah potential. This is evidenced by six high-value variables in the second cluster, while the first cluster has only one high-value variable. These findings have significant implications for inclusive da'wah strategies in Central Java. These results can be used as a strategy for mapping priority da'wah areas, allocating effective resources, and developing a more contextualised da'wah approach according to the characteristics of each cluster. This research's originality lies in applying the FGWC method in the context of da'wah mapping. This article is the first to combine spatial analysis with a study of the potential for inclusive da'wah, thus contributing to developing an interdisciplinary approach in the study of contemporary Islam in Indonesia.
Multilevel Semiparametric Modeling with Overdispersion and Excess Zeros on School Dropout Rates in Indonesia Tarida, Arna Ristiyanti; Djuraidah, Anik; Soleh, Agus Mohamad
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.30102

Abstract

This study aims to identify key factors influencing high school dropout rates in Indonesia by applying advanced statistical modeling that accounts for complex data characteristics. Dropout data often display overdispersion (variability greater than expected) and excess zeros (many students not dropping out), which, if ignored, can bias conclusions.  To address this, we compare parametric models, Zero-Inflated Poisson Mixed Model (ZIPMM), Zero-Inflated Generalized Poisson Mixed Model (ZIGPMM), and Zero-Inflated Negative Binomial Mixed Model (ZINBMM), with their semiparametric counterparts (SZIPMM, SZIGPMM, SZINBMM). The semiparametric models use B-spline functions to capture nonlinear relationships between predictors and dropout rates, with flexibility. Model performance was evaluated using Akaike Information Criterion (AIC) and Root Mean Square Error (RMSE) across 100 simulation repetitions to ensure robustness. Results show that the semiparametric ZIGPMM (SZIGPMM) outperformed other models, achieving the lowest average AIC (18969.62), suggesting the best trade-off between model fit and complexity. The optimal spline configuration used knot point 2 and order 3, with a Generalized Cross-Validation (GCV) score of 9.4107. Key predictors of dropout include school status (public or private), student-teacher ratio, distance from home to school, parental education level, parental employment status, and number of siblings. These findings provide actionable insights for education policymakers, emphasizing the need to address structural and socioeconomic barriers to reduce dropout rates effectively.
Spatial Clustering Analysis of Hand, Foot, and Mouth Disease in Jakarta using Local Indicator of Spatial Association Cluster Map and K-Means Clustering Sabila, Fatsa Vidyaningtyas; Widyaningsih, Yekti
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.30339

Abstract

Hand, Foot, and Mouth Disease (HFMD) is a infectious disease characterized by ulcers and blisters, primarily affecting children. The objective of this quantitative study is to identify areas with the highest HFMD cases (hotspot areas) in Jakarta in 2024 and to classify areas (districts) based on the number of HFMD cases and variables associated with the disease. The analysis employs the Local Indicator of Spatial Association (LISA) Cluster Map to detect spatial hotspots and K-Means Clustering to group districts by HFMD cases and related variables. LISA is a univariate method for detecting hotspots based on the local Moran’s Index that measures spatial dependence, whereas K-Means Clustering is a multivariate method for grouping individuals based on multiple variables. This study uses data from official government sources, including the number of HFMD cases, population density, average number of students per kindergarten, and average number of students per elementary school. The results of this study show that the LISA clustering reveals Kalideres and Cengkareng as High-High (H-H) clusters, while Tanah Abang, Menteng, and Senen form Low-Low (L-L) clusters. Makasar is classified as a Low-High (L-H) cluster. In contrast, the K-Means clustering groups districts into four clusters based on HFMD cases and related demographic factors, sorted in ascending order of HFMD cases. Areas with the lowest HFMD cases tend to have a moderate population density and fewer average number of students per kindergarten, while areas with the highest cases tend to have a lower population density but a higher average number of students per kindergarten. Areas classified as high cases HFMD by both methods, such as Cengkareng, should be prioritized for intervention. Cengkareng represents a district with the highest HFMD cases despite having a relatively low population density, along with a high average number of students per kindergarten and per elementary school. 
The Future of Augmented Reality Immersive Technology-Based Mathematics Learning: A Meta-Analysis Study Tamur, Maximus; Subaryo, Subaryo; Marzuki, Marzuki; Ngao, Ayubu Ismail; Castulo, Nilo Jayoma
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.31033

Abstract

Immersive technology with augmented reality (AR) as a didactic support has gone global and enriched the learning process with various packages of advantages. Although there have been many meta-analyses to test the aggregate effect of AR on students' academic performance, few have considered the duration of treatment as a moderator variable and also the comparison of the effect of AR in mathematics learning with other subjects. This study was conducted to. This random effect meta-analysis study was conducted to test the effectiveness of the application of immersive AR technology in learning by considering the duration of treatment and subject matter as research features and specifically identifying data from the Scopus database. This objective was achieved by examining 73 independent comparisons (n = 2822) that met the requirements and were identified from the Scopus database. The results of the CMA software-assisted analysis showed that the integration of immersive augmented reality technology in learning had a moderate effect (g = 0.75, p < 0.005) compared to learning conditions without AR. These results also add empirical validity to the relationship between categorical variables and the size of the research effect, as needed to understand research in the context of the application of AR in mathematics learning in the future. By clarifying the impact of AR implementation in mathematics learning, this study contributes to teachers to improve teaching effectiveness, enrich interactive learning media, and arouse students' interest and understanding of mathematical concepts in a more concrete and visual way. These findings also provide new directions for teachers, lecturers, stakeholders, and professionals in their efforts to develop a didactic framework by considering the duration of treatment in future AR applications.
Digital Teaching Material Transformation: Design Student Worksheets using GeoGebra Based on Local Wisdom by Pre-service Teachers Sugiarni, Rani; Jusniani, Nia; Rodríguez-Nieto, Camilo Andrés
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.31034

Abstract

The limited ability of teachers to design interesting digital mathematics teaching materials, as well as the context that is free from local participant wisdom, also causes misconceptions and difficulties in understanding mathematical concepts in depth for students. This study aims to analyze the ability of pre-service mathematics teachers to design student worksheets using the GeoGebra application based on local wisdom in mathematics learning. This study uses a qualitative method with a descriptive analytical approach to describe the ability of the pre-service mathematics study program to design student worksheets with Geogebra software based on local wisdom in mathematics learning. Participants in this study were in the pre-service mathematics education study program in West Java. The data collection technique used documentation in the form of student digital open material designs. The digital teaching materials designed by pre-service mathematics students are worksheets with GeoGebra software based on local wisdom. The research instruments were lecturer and teacher assessment sheets to ensure the feasibility of digital teaching materials and student response questionnaires for the practicality of the results of the digital teaching material plan used by students. The results of the study can be explained by the fact that the ability of students in the mathematics education study program to create digital open materials, namely student worksheets and GeoGebra software media, is worthy of the high category as assessed by lecturers and teachers. Meanwhile, the students' response to the socialization of digital open media design by students gave a positive response with a high category. Thus, students' digital teaching materials support meaningful mathematics learning for students with technology and local wisdom.
An Informative Prior of Bayesian Kriging Approach for Monthly Rainfall Interpolation in East Java Damayanti, Rismania Hartanti Putri Yulianing; Astutik, Suci; Astuti, Ani Budi
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.31027

Abstract

In spatial data analysis, interpolation is used to estimate values at unobserved locations, but often faces challenges in capturing complex spatial patterns and estimation uncertainty. One of the main obstacles is the small sample size, which makes the empirical variogram difficult to define well in conventional Kriging methods. The Bayesian Kriging approach overcomes this problem by integrating prior information, so it can still produce stable estimates despite limited data. This study is a quantitative, spatial-based research aimed at interpolating monthly rainfall in East Java Province using the Bayesian Kriging approach. The data consist of monthly rainfall measurements from 11 rain gauge stations distributed across East Java, obtained from the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) for the period of January to April 2024. The entire analysis was conducted using R software. A spherical semivariogram model was selected due to its superior fit to the spatial characteristics of the rainfall data in the study area with the smallest RMSE 37.17. This study demonstrates the effectiveness of Bayesian Kriging for rainfall interpolation in tropical regions with sparse data, providing more stable and accurate estimates compared to conventional methods. The scientific contribution of this research lies in showcasing how the integration of informative priors and Bayesian inference enhances interpolation accuracy in data-limited tropical environments. The resulting interpolated maps can inform land-use planning and flood risk mitigation by identifying areas of high rainfall for improved water infrastructure and lower-rainfall regions for targeted irrigation planning. 
Interpretable Ensemble Learning for Online Public Acces Catalog Technology Acceptance Prediction Fernanda, Jerhi Wahyu; Tsani, Iskandar; Nuraini, Anisya
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.30262

Abstract

The Online Public Access Catalog (OPAC) is a digital system that enables users to search for library references through an online interface using keywords. OPAC has been implemented to enhance IAIN Kediri library services. However, its usage has never been evaluated, resulting in limited understanding of user acceptance levels. This study aims to predict the acceptance of OPAC and identify the most influential variables using interpretable ensemble learning methods. This research used cross sectional design with data collected via a survey involving 400 IAIN Kediri students who had experience using the OPAC system. The study integrates the Technology Acceptance Model (TAM) with the Value-Based Adoption Model (VAM) framework. Predictor variables consist of Perceived Usefulness, Perceived Ease of Use, Intention, Technicality, and Enjoyment. The target variable was Actual Use. The measurement scale uses a Likert scale of 1 to 5. The instrument has been tested for validity and reliability. Ensemble learning algorithms used include Random Forest, AdaBoost, XGBoost, Lightgbm, and Catboost, with SHAP applied for model interpretability. Among the models tested, XGBoost achieved the highest predictive accuracy. SHAP analysis revealed that Enjoyment was the most significant factor influencing OPAC acceptance. These results demonstrate the effectiveness of interpretable ensemble models in predicting technology acceptance and suggest their potential as an alternative to data analysis methods. OPAC development can be done by improving the user interface and developing applications on Android.

Page 2 of 3 | Total Record : 27

 1   2   3 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 10, No 2 (2026): April Vol 10, No 1 (2026): January Vol 9, No 4 (2025): October Vol 9, No 3 (2025): July Vol 9, No 2 (2025): April Vol 9, No 1 (2025): January Vol 8, No 4 (2024): October Vol 8, No 3 (2024): July Vol 8, No 2 (2024): April Vol 8, No 1 (2024): January Vol 7, No 4 (2023): October Vol 7, No 3 (2023): July Vol 7, No 2 (2023): April Vol 7, No 1 (2023): January Vol 6, No 4 (2022): October Vol 6, No 3 (2022): July Vol 6, No 2 (2022): April Vol 6, No 1 (2022): January Vol 5, No 2 (2021): October Vol 5, No 1 (2021): April Vol 4, No 2 (2020): October Vol 4, No 1 (2020): April Vol 3, No 2 (2019): October Vol 3, No 1 (2019): April Vol 2, No 2 (2018): October Vol 2, No 1 (2018): April Vol 1, No 1 (2017): October More Issue