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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 577 Documents
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Forecasting Rupiah Exchange Rate Volatility using a Hybrid ARIMA–SVR Model as an Early Warning System to Address Global Dynamics Idrus Syahzaqi; Selvina Cindy Kusumaningrum; Naufal Ainul Hayat; M. Fariz Fadillah Mardianto
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.38570

Abstract

Exchange rate volatility of the Indonesian Rupiah against the US Dollar has increased due to global uncertainty. This study addresses the limitation of prior research that predominantly relies on single linear or nonlinear models in emerging markets by developing a Hybrid ARIMA SVR approach, thereby enhancing exchange rate predictability to support macroeconomic stability. This study contributing to the advancement of quantitative forecasting methods aligned with SDG 8 and SDG 16 through enhanced financial predictability. This research uses a univariate time-series dataset of weekly Rupiah US Dollar exchange rates obtained from Bank Indonesia, comprising 150 observations from March 2023 to January 2026. Novelty from this research is ARIMA model selected to capture linear temporal dependencies, while SVR is employed to model nonlinear patterns in residuals justifying the hybrid approach as a complementary integration of statistical and machine learning methods. Data preprocessing includes Box-Cox transformation and second order differencing to ensure stationarity, followed by diagnostic tests (Ljung Box, Kolmogorov Smirnov, and ARCH LM). SVR parameters are optimized using grid search to ensure robust model performance. The analysis included visualization, Box–Cox transformation (λ = −1), and second-order differencing to achieve stationarity. Diagnostic tests (Ljung Box, Kolmogorov Smirnov, ARCH LM) confirmed that ARIMA (3,2,0) met model assumptions. ARIMA residuals were subsequently model using SVR, with parameters optimized through grid search, forming the Hybrid ARIMA–SVR model. Results show that the Hybrid ARIMA SVR model outperformed the standalone ARIMA, achieving a lower MAPE. The best performance (MAPE = 0.56%) was obtained using the Radial kernel with ε = 0.2, C = 23, and γ = 28. These findings indicate that integrating linear and nonlinear models improves forecasting accuracy.
A Comparative Study of PCA-Based Dimensionality Reduction and Best Subset Selection in Disease Classification Andreas Rony Wijaya; Atika Ratna Dewi; Muhammad Bayu Nirwana; Respatiwulan Respatiwulan; Sri Sulistijowati Handajani
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.38265

Abstract

Real-world datasets often contain many variables, some of which may be irrelevant or redundant. To build an effective classification model, it is important to simplify the data by keeping only the most influential features. One common approach that can be used for selecting the most influential variables is feature selection. However, when dealing with many variables, removing some may result in the loss of information. Hence, it is also necessary to consider methods that can simplify the model while retaining most of the information from the original variables. Dimensionality reduction is one such approach that effectively addresses this issue. This study employs a comparative quantitative research approach to evaluate the effectiveness of principal component analysis (PCA) as a dimensionality reduction method and best subset selection as a feature selection method in improving classification performance. The study utilizes a heart disease dataset from the UCI Machine Learning Repository consisting of 303 observations and 13 predictor variables as a case study. Both approaches are applied to reduce the number of predictor variables and make the model more interpretable. After applying both methods, three classification models — logistic regression, naïve Bayes, and linear discriminant analysis — are trained and evaluated using accuracy, recall, precision, and F1-score, and the results are further illustrated through ROC curves. Feature selection using best-subset selection yields seven variable combinations with the most significant predictors, whereas PCA requires eight principal components to explain 80% of the total variation.  The best classification performance was obtained using the feature-selected dataset, achieving an accuracy of 87% and an AUC of 0.93, outperforming both the original dataset model and the PCA-reduced dataset model. These results show that feature selection using best subset selection provides a better balance between simplicity and classification performance. Furthermore, the models obtained after feature reduction, both from best subset selection and PCA, still maintain good predictive ability as indicated by their relatively high AUC values.
Nonparametric Biresponse Penalized Spline Regression for Modeling Stunting and Wasting in Kalimantan Samsul Arifin; Ardiansyah Abubakar; A. Fahmi Indrayani
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.38418

Abstract

Stunting and wasting remain major nutritional problems in Indonesia, including in Kalimantan, with considerable interregional variation. This condition suggests that the relationships between determinants and indicators of child nutritional status may be nonlinear and interdependent. This study aims to develop and implement a biresponse nonparametric penalized spline regression model to simultaneously model the prevalence of stunting (Y₁) and wasting (Y₂) in Kalimantan. The data used secondary data from the 2024 Indonesian Nutritional Status Survey (SSGI) of the Ministry of Health and official publications from Statistics Indonesia (BPS), with districts/cities in Kalimantan as the unit of analysis. The predictor variables included the percentage of households with access to improved sanitation (X₁), low birth weight (X₂), and the percentage of the population covered by health insurance (X₃). The Pearson correlation test indicated a significant association between stunting and wasting (p-value = 0.012), supporting the application of a biresponse modeling approach. Model selection was conducted simultaneously for the knot points and the smoothing parameter (λ) using the minimum Generalized Cross-Validation (GCV) criterion. The optimal configuration was obtained with one knot for each predictor, namely X₁ = 66, X₂ = 107, and X₃ = 53, with λ = 63.09 and GCV = 20.83. Model performance evaluation yielded MSE = 28.012 and R² = 0.241 for stunting, and MSE = 4.810 and R² = 0.106 for wasting. These results indicate that the biresponse penalized spline model can serve as a flexible approach for simultaneously analyzing stunting and wasting and for capturing heterogeneous, nonlinear relationships between predictors and response variables.
The Development of a Financial Risk Meter for Indonesian Public Banks Using LASSO-QR and LASSO-QRNN Husna Afanyn Khoirunissa; Dedy Dwi Prastyo; Isnandar Slamet; Sugiyanto Sugiyanto; Bayutama Isnaini
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.38631

Abstract

Banking companies will have a domino effect when one company fails that causes systemic risk in Indonesia. Moreover, Indonesia has a history of economic crises. This study presents a series of systemic risk measures for Indonesia, the Financial Risk Meter (FRM) with the LASSO-QR model, a novel application within the context of Indonesian data. Then, this study enhances the FRM methodology by incorporating the QRNN method to account for the nonlinear dependencies of return values across different companies, and applies the novel LASSO-QRNN method to measure FRM for Indonesia. This study employs a quantitative empirical approach using secondary financial and macroeconomic time-series data. This study developed LASSO-QR and LASSO-QRNN models applied to log-return data of public banks in Indonesia and macroeconomic variables to measure the FRM. These models captured financial risk characteristics by adjusting LASSO parameters with a moving window approach. The FRM indicated high-risk periods in mid-2020 and the first quarter of 2021 for the LASSO-QR, extending into the third quarter of 2021 for the LASSO-QRNN. This study contributes new insights into risk measures for individual banks and the banking system in Indonesia. Additionally, this offers solutions for measuring daily systemic risk that can account for both linear and nonlinear dependencies among companies.
Structural Equation Modeling Based Partial Least Square of Student Misconceptions in Estimating Probability Distribution Parameters Wardhani Utami Dewi; Tri Cahyo Wahyudi; Miftahul Irfan
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.38398

Abstract

Students' misconceptions in estimating the parameters of probability distribution are still an important problem in learning statistics in universities. Most previous studies have examined partial misconceptions from cognitive aspects, so the structural relationship between concept understanding, learning experience, learning motivation, and problem-solving skills in explaining misconceptions has not been widely analyzed in an integrated manner. This study aims to develop and validate a structural model that explains the factors that influence student misconceptions in estimating probability distribution parameters using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. This study uses an explanatory quantitative design involving 200 students who have studied probability distribution and parameter estimation in several universities in Lampung Province. Data was collected through a Likert scale questionnaire that measured five latent constructs, namely concept comprehension, learning experience, learning motivation, problem-solving skills, and student misconceptions. The analysis was carried out through the evaluation of the measurement model (loading factor, composite reliability, and average variance extracted), structural model testing using the bootstrapping technique, and evaluation of the overall suitability of the model. The results showed that concept comprehension (β = 0.74) and learning experience (β = 0.82) had a significant effect on problem-solving skills. Problem-solving skills further affect learning motivation (β = 1.92), while learning motivation affects the level of student misconception (β = 0.67). The developed model was able to explain 65% of the variation in problem-solving skills and 88% of the variation in student misconceptions. This research contributes in the form of a SEM-PLS model that integrates cognitive and affective factors in explaining the emergence of statistical misconceptions, as well as providing an empirical basis for the development of more effective statistical learning strategies to reduce student misconceptions.
Integrating Project-Based Learning with Palembang Culinary Context in Teaching Statistics for Supporting Pre-Service Teachers’ Flexibility Jumroh Jumroh; Misdalina Misdalina; Tika Dwi Nopriyanti; Eka Fitri Puspa Sari
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.38171

Abstract

Flexibility is a crucial component of statistical reasoning, as it enables students to interpret data using multiple representations and adapt problem-solving strategies in different contexts. However, many pre-service teachers still demonstrate limited flexibility when learning basic statistics, often relying on procedural calculations rather than conceptual understanding. This study aims to develop a Project-Based Learning (PBL)-based instructional device integrated with the Palembang culinary context to support pre-service teachers’ flexibility in learning statistics. This study employed a Research and Development approach using the ADDIE model, which includes analysis, design, development, implementation, and evaluation stages. The participants were pre-service mathematics teachers enrolled in a basic statistics course at a university in Indonesia. The developed learning tools consisted of Student Activity Sheets (LAM) and evaluation tasks supported by the Jamovi statistical application. Flexibility was operationally defined as the ability to use multiple representations, apply different solution strategies, and adapt statistical reasoning when analyzing data. The instruments included expert validation sheets, student response questionnaires, and a flexibility test developed based on representational, strategic, and conceptual indicators. The validation results obtained an average score of 86 (very valid), the practicality test reached 85.2 (very practical), and the field test showed an average flexibility score of 72.7 (good category). These results indicate that the developed instructional device is valid, practical, and effective. The novelty of this study lies in integrating project-based learning, culturally relevant culinary data, and Jamovi-assisted statistical analysis to foster flexible statistical thinking among pre-service teachers.
Analysis of Intergenerational Health-Related Quality of Life using Multiple Group Confirmatory Factor Analysis Ismail Husein; Novyra Tedi Natasya; Rina Widyasari
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.35927

Abstract

Health-Related Quality of Life (HRQoL) is a broad concept that includes physical, mental, emotional, and social aspects of well-being. Because different generations often view health in distinct ways, this study compared HRQoL among Generations X, Y, and Z in Medan using a quantitative, cross-sectional design. The main goal was to test and validate the factor structure of the SF-36 questionnaire and examine whether it measures HRQoL consistently across these groups through Multi-Group Confirmatory Factor Analysis (MG-CFA). The initial analysis supported a reliable four-factor model with 12 items covering Physical Functioning, Role Limitations due to Physical Health, Emotional Well-being, and Role Limitations due to Emotional Health. This model showed strong convergent validity. However, further testing revealed that the SF-36 does not maintain structural equivalence across the three generations, indicating that people interpret its items differently depending on their generational cohort. This finding highlights a key methodological issue: direct comparisons of HRQoL scores across generations are statistically invalid without adjustments. As a result, researchers must apply partial invariance techniques or refine the model before making meaningful cross-generational comparisons.

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