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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.
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Articles 25 Documents
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Search results for , issue "Vol 10, No 1 (2026): January" : 25 Documents clear
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.
Comparing MARS and Binary Logistic Regression to Modelling Hepatitis C Cases using the SMOTE Balancing Method Chamidah, Nur; Ramadhanti, Aulia; Ramadhani, Azzah Nazhifa Wina; Syahputra, Bimo Okta; Ariyawan, Jovansha; Kurniawan, Ardi
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.v10i1.33196

Abstract

Hepatitis is an inflammatory liver disease caused by viral infection and remains a major global public health concern, responsible for approximately 1.4 million deaths annually. Egypt is among the countries with the highest prevalence of Hepatitis C. To address this issue and support Goal 3 of the Sustainable Development Goals (SDGs), this study applies a quantitative approach using secondary data to analyze factors influencing Hepatitis C infection in Egypt. Two statistical models Binary Logistic Regression and Multivariate Adaptive Regression Splines (MARS) were compared, with the SMOTE method implemented to correct class imbalance. The dataset consisted of 608 patient observations, initially imbalanced at a ratio of 86.5:13.5, and were balanced to 52.6:47.4 after SMOTE application. The results revealed that the MARS model demonstrated superior predictive performance compared to binary logistic regression. All independent variables were found statistically significant (p < 0.05), except sex. Additionally, all odds ratios were less than 1, indicating a lower probability of Hepatitis C infection relative to non-infection. These findings highlight the relevance of statistical modeling and data-driven strategies in supporting preventive health measures. 
Analysis of the Spatial Error Model with Queen Contiguity Matrix Weights on Dengue Fever Soraya, Siti; Aziza, Istin Fitriani; Arisandi, Rizwan; Verma, Kirti; Isasi, Widani Darma; Sufahani, Suliadi Firdaus
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.v10i1.33477

Abstract

Dengue Fever is one of the deadly diseases caused by a rapidly spreading virus transmitted through the Aedes aegypti mosquito. This study focuses on the NTB region, which has different geographical characteristics and infrastructure challenges. The variables used in this study are: dengue fever incidence, population, hospitals, community health centers, poor residents, and floods. The aim of this study is to model the factors that influence the occurrence of dengue fever in NTB. The method used is the Spatial Error Model (SEM), which serves to analyze spatial data to observe spatial correlation in the error variables. The research results indicate that the Moran Index and the Lagrange Multiplier test confirm the existence of spatial dependence in the error aspects. Significant variables at the 5% level affecting dengue fever cases are population size, the number of hospitals, and the number of community health centers. These findings provide an important scientific contribution as they represent one of the early studies that specifically identify and model the spatial dependence patterns of dengue fever cases in West Nusa Tenggara using a spatial econometric approach, thereby enriching the literature on spatial epidemiology at the regional level. The findings indicate that population growth and disparities in healthcare facilities increase the risk of dengue fever. This implies that more equitable spatial planning of healthcare services, strengthening of primary care, population density control, and increased community participation in sanitation and regular mosquito breeding site eradication are necessary as part of an to reduce dengue fever cases in NTB.
Sentiment Analysis of Hotel Reviews in Senggigi using Decision Tree and Support Vector Machine Algorithm Putra, Lalu Muhammad Reza Suganda; Wijayanto, Heri; Wedashwara, I Gede Putu Wirarama
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.v10i1.34960

Abstract

The tourism industry is a rapidly growing sector that significantly contributes to the economy, including Indonesia. One of the popular tourist destinations in Indonesia is Senggigi, located on the island of Lombok. This destination offers high natural and cultural appeal. In the tourism industry, hotels are crucial as primary accommodations for travelers to stay and rest. Tourist reviews on hotel services greatly influence potential visitor’s decisions in selecting the right accommodation. Therefore, sentiment analysis of hotel reviews is essential for understanding customer satisfaction levels and assisting hotel managers in improving service quality. This research applies a comparative quantitative approach using Decision Tree and Support Vector Machine (SVM) algorithms. The dataset consists of 6,920 hotel reviews collected from TripAdvisor platforms through web scraping techniques. Data preprocessing included data cleaning, case folding, tokenization, stop word removal, and stemming to enhance classification performance. Sentiment labels were categorized into positive, neutral, and negative classes. Model performance was evaluated using multiple metrics, including accuracy, precision, recall, and F1-score, to ensure a comprehensive assessment. The word frequency distribution reveals that that accommodation experience and room quality play a crucial role in customer satisfaction. Positive sentiment is dominated by adjectives like great, nice, and beautiful, reflecting pleasant experiences. Negative sentiment is expressed more politely through phrases such as not good or not very nice. Neutral sentiment tends to be descriptive without strong emotional expression. In terms of model performance, SVM outperformed the Decision Tree model, achieving an accuracy of 90%, precision of 91%, recall of 90%, and an F1-score of 85%. In comparison, the Decision Tree achieved an accuracy of 87%, precision of 84%, recall of 87%, and an F1-score of 85%. These findings demonstrate the superior capability of SVM in handling complex and diverse textual data. This study contributes academically by strengthening empirical evidence on the effectiveness of machine learning–based sentiment analysis in the tourism domain and practically by providing actionable insights for hotel managers to improve service quality and customer satisfaction.
Application on Hypergraph in Vigenere Chiper Asari, Okta Endri; Dafik, Dafik; Adawiyah, Robiatul; Kristiana, Arika Indah; Prihandini, Rafiantika Megahnia
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.v10i1.34572

Abstract

Message protection remains a major focus in the field of cryptography. This study proposes a new development on the Caesar cipher algorithm by utilizing hypergraph as a keystream generation source. The research designs a super (a,d)-hyperedge antimagic total labeling method applied to three hypergraph structures (Volcano, Semi Parachute, and Comb) to generate the keystream. Security is evaluated using four mechanisms: brute force analysis, processing time, ciphertext character distribution, and ciphertext bit size. The findings prove that the hypergraph based approach is robust against brute force attacks, improve memory and time efficiency. Quantitatively, the Comb hypergraph demonstrates the best efficiency, achieving an encryption time of 0.0030 seconds for 512 bytes and superior storage efficiency (e.g., 136 bytes for 16 bytes ), outperforming the Semi Parachute and Volcano structures. The main contributions include the hypergraph labeling-based keystream generation algorithm, dynamic block key construction, and a Vigenere protocol that is more adaptive to storage constraints and computationally efficient..
Modeling Zero-Inflated Poisson Invers Gaussian Regression Bayesian Approach Jannah, Berliana; Wardhani, Ni Wayan Surya; Sumarminingsih, Eni
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.v10i1.34068

Abstract

Deaths due to dengue hemorrhagic fever (DHF) remains one of the most pressing public health issues in Indonesia, especially in urban areas such as Semarang City, which has a high population density and diverse environmental conditions that potentially increase the risk of transmission and death from DHF. This study aims to model the number of DHF in Semarang City using a Bayesian-based Zero-Inflated Poisson Inverse Gaussian Regression (ZIPIGR) approach. The research data was obtained from the Semarang City Health Office and the Central Statistics Agency (BPS) in 2024, with the response variable being the number of DHF deaths and five predictor variables. The data showed overdispersion and a high proportion of zeros (around 50%), indicating the presence of excess zeros in count data with a small sample size. The Bayesian ZIPIGR method was chosen because it can produce more stable parameter estimates than classical methods such as Maximum Likelihood Estimation (MLE), especially for data with complex likelihood functions, small sample sizes, and many zero values. Parameter estimation was performed using Gibbs Sampling simulation in the Markov Chain Monte Carlo (MCMC) framework. The results show that the Bayesian ZIPIGR model performs better than the MLE ZIPIGR model based on the Root Mean Square Error (RMSE) value. Factors that significantly influence DHF mortality are population density, slum area, and number of health workers. These results confirm that regional density and health worker capacity play an important role in increasing the risk of DHF mortality in urban areas. The developed model has been proven to be highly accurate in modeling count data with excess zero characteristics and makes an important contribution to health policy formulation. In practical terms, this model can be used to improve early warning systems and DHF control strategies in densely populated urban areas such as the city of Semarang.
Mathematics Learning Activities using Vignette Activity Sequence (VAS) and Braille Clock for Visually Impaired Students Maghfiroh, Diva Lailatul; Damayanti, Nia Wahyu
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.v10i1.34839

Abstract

Mathematics learning for students with visual impairments is often challenging because many concepts are visual in nature, including units of time that require an understanding of the spatial position of clock hands. This condition requires a more concrete, tactile, and structured learning approach so that concepts can be understood meaningfully. This study aims to describe how Vignette Activity Sequence (VAS) based mathematics learning with braille clock media supports the understanding of time unit concepts in students with visual impairments. This study uses a double case study qualitative approach with two subjects, one with low vision and one with full blind student, selected through purposive sampling. The intervention lasted for one month through a series of VAS sessions that integrated contextual narratives, tactile exploration, and manipulative activities using braille clocks. Data were obtained through observation, semi-structured interviews, and documentation, then analyzed using the Miles and Huberman interactive model. The results showed that VAS helped both subjects understand the relationship between the movement of the short hand and the rotation of the long hand, albeit at different rates of development. The low vision subject was quicker to recognize numbers and understand time units, while the totally blind subject showed gradual improvement in tactile orientation and number touching strategies. Both experienced increased accuracy in reading time and moving the clock hands after attending repeated sessions. These findings confirm that the integration of VAS and braille clocks provides an effective and inclusive multisensory learning experience for students with visual impairments.
Dimensionality Reduction Evaluation of Multivariate Time Series of Consumer Price Index in Indonesia Valentika, Nina; Sumertajaya, I Made; Wigena, Aji Hamim; Afendi, Farit Mochamad
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.v10i1.34151

Abstract

Multivariate time series (MTS) analysis of the Consumer Price Index (CPI) in Indonesia often encounters challenges such as outliers, missing data, and inter-variable correlations. Principal Component Analysis (PCA) is a practical approach for dimensionality reduction; however, its performance may vary depending on the data characteristics. This study is a quantitative comparative study that integrates empirical analysis and Monte Carlo simulation based on a first-order Vector Autoregressive (VAR(1)) model to evaluate three PCA approaches: Classical PCA, Robust PCA (RPCA), and PCA of MTS. These methods were applied to weekly price data of eight strategic food commodities across 70 districts and cities in Indonesia. The evaluation employed three criteria: (1) dimensionality reduction efficiency (empirical and simulation), (2) reconstruction accuracy measured using Root Mean Square Error (RMSE) (empirical), and (3) robustness to outliers and inter-variable correlations (simulation). Empirical results indicate that Classical PCA (lag 1) and RPCA (lag 1) are both efficient and effective in reducing dimensionality with minimal information loss. Using the first three principal components, all three methods were able to explain at least 85% of the total variance, with lag 1 identified as optimal. Simulation results reveal that RPCA (lag 1) provides the most stable and consistent performance in the presence of outliers, while Classical PCA (lag 2) performs better under conditions of high inter-variable correlation and a low proportion of outliers. These findings suggest that robust covariance estimation can improve the accuracy of dimensionality reduction and enhance the stability of multivariate time-series analysis for food price data in Indonesia.
Innovative Mathematics Learning: The Impact of Augmented Reality and Ethnomathematics on Communication Skills Tamur, Maximus; Jehadus, Emilianus; Jackaria, Potchong M.; Castulo, Nilo Jayoma; Ngao, Ayubu Ismail
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.v10i1.34363

Abstract

The integration of digital technology with a cultural approach has become an important innovation in mathematics education to enhance meaningful learning. However, there is still limited research combining Augmented Reality (AR) with ethnomathematics to strengthen students' mathematical communication skills. This study aims to analyze the impact of Augmented Reality and ethnomathematics-based learning on students' mathematical communication skills. The study employed an experimental design involving 60 seventh-grade students selected randomly from eight classes at SMP Negeri 6 Langke Rembong, Ruteng, Indonesia, during the 2024/2025 academic year. The research instrument consisted of a five-item mathematical communication test, which was validated through expert judgment and empirical testing, and demonstrated satisfactory reliability based on internal consistency analysis. SPSS and CMA software were used to support data analysis. A t-test was conducted to examine differences in mathematical communication ability between the experimental and control groups after fulfilling prerequisite assumptions. The findings indicate that the integration of AR and ethnomathematics significantly improved students’ ability to express mathematical ideas clearly, both orally and in written form. Additionally, students showed higher levels of cultural engagement and appreciation, which positively contributed to the development of their communication skills. This study recommends the integration of AR and ethnomathematics as a sustainable innovation in mathematics learning and suggests further research to explore its application across diverse mathematical topics and broader educational contexts.
Application of Kernel Nonparametric Biresponse Regression with the Nadaraya-Watson Estimator in Poverty Analysis in South Sulawesi Husain, Hartina; Nisardi, Muhammad Rifki; Sasolo, Ryo Hartawan
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.v10i1.33543

Abstract

Poverty is a complex social issue that requires in-depth analysis to identify its contributing factors. South Sulawesi, as one of the provinces in Indonesia, continues to face various challenges in poverty alleviation. This study is a quantitative research that aims to model the poverty rate and poverty severity index using a biresponse nonparametric kernel regression with the Nadaraya-Watson estimator and Gaussian kernel function. The analysis is based on 2024 data form the Central Bureau of Statistics (BPS), which includes poverty indicators as response variables and socio-economic factors, processed using R Studio 2025. The nonparametric biresponse kernel regression analysis yielded optimal bandwidths of h_1=0,188; h_2=0,083; h_3=0,159; and h_4=0,028. Model accuracy is demonstrated by a Generalized Cross-Validation (GCV) value of 5.515 and a Mean Squared Error (MSE) of 0.585, indicating high stability and low prediction error. The model demonstrates adaptive accuracy in simultaneously modeling the two response variables and highlights the strength of kernel-based biresponse regression as an evidence-based tool for policymakers to design targeted, region-specific poverty alleviation strategies.

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