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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 540 Documents
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Modeling Prevalence of Hypertension in Indonesia with Multivariate Adaptive Regression Splines Method Suliyanto, Suliyanto; Saifudin, Toha; Naura, Sheila Sevira Asteriska; Dewanty, Sanda Insania; Wulandari, Indana Zulfa; Aflaha, Nabila Shafa; Aulia, Niswa Faizah
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.28392

Abstract

Hypertension is one of the important public health problems in Indonesia, which contributes to the high prevalence of non-communicable diseases. This study aims to model the prevalence of hypertension in Indonesia using the Multivariate Adaptive Regression Splines (MARS) method to identify significant predictors and their interactions. The data used was secondary data from the 2023 Indonesian Health Survey, including variables such as smoking prevalence, physical inactivity, dietary habits (consumption of fatty and sweet foods), lack of fruit and vegetable consumption, and obesity prevalence. The MARS method was used to analyse the nonlinear relationships and interactions between these predictors. After a trial-and-error process to determine the optimal number of basis functions (BF), maximum interactions (MI), and minimum observations (MO), the best model was achieved with BF = 18, MI = 3, and MO = 1. This model produced a Generalised Cross Validation (GCV) value of 13.428 and R-Square of 0.278. This fairly low R-Square value indicates that the factors analysed have contributed to the variation in hypertension prevalence, but there are still other aspects that can be taken into account to improve the predictive power of the model. The significant predictor variables were consumption of fatty foods (X3), lack of physical activity (X2), and consumption of sweets (X4), with the highest importance on X3 (100%). The findings reveal that interactions between variables, such as dietary habits and physical inactivity, significantly influence the prevalence of hypertension. For example, higher consumption of fatty and sweet foods combined with low physical activity increases the risk of hypertension. These results demonstrate the effectiveness of the MARS method in capturing complex and nonlinear relationships and serve as findings that highlight the need for health policies that focus on healthy diets and increased physical activity, in line with Goal 3 of the SDGs, “Good Health and Well-Being,” which aims to reduce premature mortality from noncommunicable diseases. Recommended interventions include nutrition education campaigns and community-based exercise programs to reduce the prevalence of hypertension in Indonesia.
Regression Model as a Tool for Evaluating Mangrove Degradation in Lembar Bay, West Lombok Johari, Harry Irawan; Rahmat, Nurul Isnaeni; Sukuryadi, Sukuryadi
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.v9i3.31664

Abstract

The mangrove ecosystem plays a vital role in maintaining ecological balance, supporting economic livelihoods, and sustaining socio-cultural functions. However, in Lembar Bay, West Lombok Regency, this ecosystem is increasingly threatened by human activities, particularly land conversion for aquaculture. These activities have led to significant ecological degradation, biodiversity loss, and weakened coastal protection. This study aims to analyze the key factors influencing mangrove degradation and to evaluate the effectiveness of regression models in assessing the contribution of these factors. A quantitative research approach was employed, with data collected through structured questionnaires distributed to 45 purposively selected community members considered knowledgeable about local mangrove conditions. The study also integrated field measurements and satellite imagery interpretation to assess mangrove density, biodiversity, and related environmental variables. Multiple linear regression analysis was used to examine the relationship between anthropogenic pressures such as land clearing, water quality, and rehabilitation efforts and indicators of mangrove degradation, namely biodiversity and mangrove density. Regression analysis showed a strong and significant effect of water quality on both mangrove biodiversity and density. The biodiversity regression model produced a correlation coefficient (R) of 0.820 and a determination coefficient (R²) of 0.673, indicating that 67.3% of the variation in biodiversity can be explained by the analyzed factors. Similarly, the mangrove density model yielded an R of 0.800 and R² of 0.640, meaning that 64.0% of the variation in mangrove density was explained. F-test results confirmed that both models were statistically significant (p-value < 0.05). The findings indicate that aquaculture expansion and land use changes are the most critical contributors to mangrove degradation. These pressures directly impair the physical condition of the ecosystem, leading to biodiversity loss and increased vulnerability to coastal hazards. Based on community perceptions, most respondents supported stricter sanctions against mangrove destruction and agreed that mangrove conservation improves the quality of life. Therefore, this study recommends that policymakers and local governments strengthen their roles in monitoring and controlling land use changes, enforcing environmental regulations, and promoting environmental education programs. It is also essential to enhance community participation in mangrove rehabilitation through inclusive, knowledge-based initiatives and integrate scientific evidence into participatory coastal spatial planning. This study contributes to the scientific literature on mangrove conservation by demonstrating the empirical effectiveness of regression analysis in identifying and quantifying human-induced pressures affecting mangrove ecosystems.
Development of Ethnomathematics-Based Numeracy Literacy Questions in Peci Tapis Lampung Linuhung, Nego; Sudarman, Satrio Wicaksono; Agustina, Rina
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.30095

Abstract

The low contextual relevance of conventional mathematics learning materials often results in limited student engagement and understanding, especially in diverse cultural settings. To address this gap, this study aims to develop valid, practical, and effective ethnomathematics-based numeracy literacy questions by integrating the cultural elements of Peci Tapis Lampung into mathematics instruction. The research employed a Research and Development (R&D) method using the 4D model (Define, Design, Develop, Disseminate). In the development phase, the questions were validated by four experts in material, design, and assessment using expert validation sheets. Student responses were measured through a practicality questionnaire. Quantitative data from expert validation were analyzed using descriptive statistics to determine the validity level, while student response data were processed to assess practicality. The validation results indicated that the questions were "Very Valid" with a score of 84%, and student responses revealed a practicality level of 83.75%, categorized as "Very Practical". Additionally, the normalized gain (N-Gain) was calculated at 61.03%, indicating a “High” effectiveness level in improving students' numeracy literacy.  These findings suggest that incorporating local cultural contexts into numeracy literacy can enhance the meaningfulness of mathematical learning and foster a greater appreciation for regional heritage. The ethnomathematics approach, therefore, offers a promising strategy for creating culturally responsive mathematics education.
Investigating STEM Career Interests: How Can Spatial Orientation, Mental Rotation, and Spatial Visualization Explain Them? Saputra, Andari; Priatna, Nanang; Dahlan, Jarnawi Afgani; Husni, Niakmatul
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.28891

Abstract

Spatial ability plays a crucial role in shaping students' interest and career paths in STEM (Science, Technology, Engineering, and Mathematics). This quantitative study involved 60 science students aged 15–17 in Bandung, Indonesia, utilizing a spatial ability test to measure mental rotation, spatial visualization, and spatial orientation, along with a career interest questionnaire to assess STEM and non-STEM preferences. Logistic regression analysis confirmed that spatial ability significantly influenced students' STEM interest (p = 0.004) with a moderate contribution. Further analysis using the Independent Samples T-Test revealed that students interested in STEM had significantly higher mental rotation (p < 0.001, Cohen’s d = -1.000) and spatial visualization (p = 0.002, Cohen’s d = -0.797) abilities than non-STEM students, while spatial orientation showed no significant difference (p = 0.112, Cohen’s d = -0.317). These findings highlight the role of spatial ability as a predictor of STEM interest, emphasizing the need for educational interventions such as visualization-based learning, three-dimensional object manipulation, and technology-assisted spatial training, including computer-aided design (CAD) software and mental rotation exercises. Integrating these strategies into mathematics and science curricula can enhance spatial skills and support students' engagement in STEM education and careers. 
Analysis of Students' Academic Performance in the Department of Mathematics Based on Semester GPA Dynamics: A Case Study of the 2017–2024 Cohorts Rahmat, Shafa Khadijah; Abdullah, Sarini
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.v9i3.30287

Abstract

This quantitative exploratory study investigates changes in students' Semester Grade Point Average (GPA) and their relationship with graduation status and study duration. It uses academic records from the Department of Mathematics at a public university in Indonesia for cohorts from 2017 to 2024. The study addresses concerns raised after the COVID-19 pandemic, which may have disrupted academic progression and altered the predictive power of initial GPA on graduation outcomes a gap not sufficiently explored in existing literature. Data were collected directly from the university's academic database, ensuring accuracy and consistency without relying on self-reported surveys. Descriptive statistical methods and visual analytics (e.g., line charts, boxplots, and scatter plots) were applied to uncover trends and patterns. Results show that earlier cohorts (2017–2020) have high graduation rates (82.7%–94.4%), while the 2019 cohort recorded the highest dropout rate (11.1%). Newer cohorts (2021–2024) predominantly consist of students still enrolled, though some early graduations and dropouts occurred. A positive correlation was found between first-semester GPA and graduation success, yet the pandemic likely introduced new variables that affect academic outcomes. These findings provide actionable insights for academic policy and support the development of early detection systems to identify students at academic risk.
Analysis of Online Game Addiction with Crowley-Martin Incident Rate Function Syata, Ilham; Halim, St. Nur Humairah
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.v9i3.31641

Abstract

This study aims to build and analyze a new mathematical model of online game addiction with the Crowley-Martin type incidence rate function approach. This research is categorized as a theoretical-quantitative study using mathematical modeling as its primary approach. The research instruments used include symbolic computation, simulation software, and parameter estimation techniques derived from literature. Stability analysis is conducted through Jacobian linearization, the Routh-Hurwitz criterion, and the Next Generation Matrix method to calculate the basic reproduction number. Optimal control is formulated using Pontryagin’s Minimum Principle with two strategies: parental guidance and counseling therapy. Data analysis combines analytical techniques in stability and control theory with numerical simulations to evaluate the system. The results show that: The addiction-free fixed point T_0 is locally asymptotically stable if R_0<1, the addiction fixed point T^*is locally asymptotically stable if R_0>1. Numerical simulations demonstrate that combined control strategies effectively reduce the number of exposed and addicted individuals.
Practical Applications of Deep Learning in Mathematics to Enhance Student Engagement and Conceptual Mastery Nurdiana, Aty; Zulianti, Hajjah; Ciciria, Deri; Fitria, Nur; Kirana, Arinta Rara
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.33102

Abstract

This study examines the application of deep learning strategies in mathematics education to enhance student engagement and conceptual mastery at a higher education institution in Lampung, Indonesia. Traditional teaching methods, which often focus on rote memorization and procedural fluency, are limited in fostering critical problem-solving skills and deeper conceptual understanding. This research investigates how deep learning strategies such as active learning, collaborative problem-solving, and self-regulated learning can bridge these gaps. A mixed-methods approach was used, combining quantitative data from the Deep Learning Engagement Questionnaire (DLEQ) with qualitative insights from focus group discussions, reflective journals, and interviews with lecturers. Interactive tools like GeoGebra were also incorporated to support the learning process. The findings indicate that deep learning strategies significantly improved student engagement, motivation, conceptual understanding, and problem-solving abilities. Students demonstrated better application of mathematical concepts in practical settings, and lecturers observed improved student performance. This study concludes that the integration of deep learning principles into mathematics education significantly enhances learning outcomes and equips students with the skills needed to navigate real-world challenges. These findings provide meaningful implications for curriculum developers, educators, and policymakers in fostering sustainable, student-centered learning environments within higher education.
Modelling Consumer Price Index Effect on 10-year US Treasury Bond Yields using Least Square Spline Approach Widiyanti, Julia; Salsabila, Safira; Harsanti, Dwika Maya; Amelia, Dita; Rifada, Marisa
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 1 (2026): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i4.33020

Abstract

Inflation measured by the Consumer Price Index (CPI) is a critical indicator in the government bond market that directly affects the yields of long-term securities such as the 10-year US Treasury Bond. This study is an explanatory quantitative study that aims to examine the complex dynamics of this relationship using the nonparametric least square spline method. The analysis uses monthly CPI data from FRED and 10-year US Treasury bond yield data from Investing.com for the period 2013-2025. This method divides the data into simple polynomial segments that are smoothly connected at transition points (knots), enabling the modelling of nonlinear patterns without assuming an initial curve shape. The analysis results indicate that a first-degree polynomial spline model (piecewise linear) with three knots successfully represents the bond yield response to inflation shocks with R^2 = 86.48%. Model segmentation identified four regimes: (1) Post-crisis recovery phase, with a negative relationship driven by Fed monetary stimulus suppresing yields despite initial inflation emergence; (2) Policy normalization phase, with a positive relationship aligned with monetary tightening in response to moderate inflation; (3) During the COVID-19 pandemic, a negative relationship due to a surge in demand for safe-haven bonds despite rising inflation; (4) Post-pandemic, the relationship turned positive again following the Fed’s aggressive monetary tightening in response to high global inflation. These findings highlight the urgency of regime-based monitoring for investors and policymakers, while contributing concretely to SDG 8 (decent work and economic growth) through the facilitation of appropriate interest rate policies for sustainable macroeconomic stability, and supporting SDG 9 (industry, innovation, and infrastructure) through the identification of inflation patterns that strengthen shock-resistant infrastructure investment planning and financial innovation during turbulent economic transitions.
Analysis of Unmet Need for Health Services Based on the Percentage of Public Health Complaints with a Kernel Estimator Approach Rifada, Marisa; Amelia, Dita; Setyaningrum, Jeny Praesti; Septiandini, Niswah; Kalista, Yovita Karin; Dwitya, Shabrina Nareswari
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 4 (2025): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i4.32555

Abstract

Healthcare services are a fundamental need that governments must guarantee to ensure optimal health outcomes for all citizens. However, many individuals still face significant barriers in accessing necessary healthcare services. This quantitative research employs a spatial analysis to examine the unmet need for health services based on public health complaints, utilizing a nonparametric regression approach with Kernel estimator. The Kernel estimator method was chosen for its flexibility in capturing unstructured data patterns, allowing the analysis to better reflect real-world conditions. The study uses health complaint data from the Central Bureau of Statistics, covering 38 provinces in Indonesia in 2024. However, data from 4 provinces were incomplete, so only 34 provinces were included in the analysis. The independent variable is the percentage of public health complaints, while the dependent variable is the percentage of unmet healthcare needs. A Gaussian kernel function was applied for nonparametric regression, identified as the optimal method based on the lowest Generalized Cross Validation (GCV) value of 1.052939 at a bandwidth of 0.33. The model demonstrates high predictive accuracy, with an R² of 82.44% and a Mean Squared Error (MSE) of 30.7%. These findings provide actionable insights for targeting healthcare disparities and improving service accessibility.
Epistemological Obstacles in Solving PISA Adapted Problems on System of Linear Equations In Two Variables Ningrum, Yunia Jumita; Dasari, Dadan; Prabawanto, Sufyani
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 4 (2025): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i4.31718

Abstract

This study aims to identify and analyze the epistemological obstacles encountered by junior high school students when solving PISA-based mathematical literacy problems on the topic of System of Linear Equations in Two Variables (SLETV), viewed from the perspective of PISA competency levels. The research seeks to contribute to the development of more effective mathematics instruction. This study is deemed essential because epistemological obstacles can hinder students' ability to apply mathematical concepts in real-world contexts an ability that is central to international assessments such as PISA. A qualitative approach was employed through Didactical Design Research (DDR), involving 23 ninth-grade students. Data were collected through a written test consisting of a mathematical literacy problem adapted from PISA items, and supported by interviews and classroom observations. The data were analyzed using qualitative descriptive methods, focusing on students’ thinking processes and emerging error patterns. The findings reveal that students encounter various epistemological obstacles across all PISA competency levels (1b to 6), such as conceptual misconceptions, procedural errors, and difficulties in translating contextual information. At the lower levels (1b–3), students struggled to formulate basic algebraic models and perform arithmetic operations. At the higher levels (4–6), they experienced challenges in handling complex calculations, verifying solutions, and applying reasoning in abstract or multi-step situations. These obstacles stem from fragmented prior knowledge, limited exposure to contextual problems, and a lack of reflective habits. This study highlights the importance of integrating authentic real-world problems, providing systematic scaffolding, and fostering continuous self-verification practices in instructional design. The findings offer practical insights for developing targeted pedagogical interventions to enhance students' mathematical literacy and better prepare them to tackle SLETV problems in contexts similar to those featured in PISA.

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