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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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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. 
Estimation of Tail Value at Risk for Bivariate Portfolio using Gumbel Copula Fransiska, Fransiska; Sulistianingsih, Evy; Satyahadewi, Neva
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.v9i2.29952

Abstract

Investing in the stock market involves complex risks, especially under extreme and unpredictable conditions. While Value at Risk (VaR) is a widely used risk measure, it has limitations in capturing tail-end risks. This study employs Tail Value at Risk (TVaR) using the Gumbel Copula approach, which effectively models upper-tail dependence in return distributions—an aspect often overlooked by traditional linear correlation methods. This quantitative research utilizes copula-based Monte Carlo simulation. The data consists of daily closing prices of PT Adaro Energy Indonesia Tbk (ADRO) and PT Indo Tambangraya Megah Tbk (ITMG) from July 3, 2023, to July 30, 2024. The analysis begins with return calculation and tests for autocorrelation and homoskedasticity. The Gumbel Copula parameter is estimated using Kendall’s Tau, resulting in a dependence parameter of 1.7791. Based on this, 1,000 simulations are conducted to generate new return data that reflect extreme dependencies between the two stocks. An optimal portfolio is constructed using the Mean-Variance Efficient Portfolio (MVEP) method, assigning weights of 31.61% to ADRO and 68.39% to ITMG. TVaR is then calculated from the simulated portfolio returns. The results show increasing TVaR values at higher confidence levels: 2.08%, 2.64%, 3.14%, and 4.11% for 80%, 90%, 95%, and 99%, respectively. These findings demonstrate that TVaR provides more accurate insights into potential losses in extreme market conditions, supporting investors in developing more informed and risk-sensitive portfolio strategies.
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.
Forest Fires in Peatlands Analyzed from Various Perspectives: Spatial, Temporal, and Spatial-Temporal Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri; Ayyash, Muhammad Yahya; Pratiwi, Hesty
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.v9i2.28884

Abstract

Peatland fires are characterized by the compaction of organic matter below the soil surface. If dry conditions occur, the organic matter can burn, making it difficult to extinguish the fire. This study aims to analyze peatland forest fires with three perspectives, namely temporal, spatial, and spatial-temporal. The data used is the confidence level data of hotspots in forest fires in Kubu Raya Regency, West Kalimantan from January 2014 to December 2023. The methodology used includes collecting fire data from satellite imagery and prepocessing the data. Furthermore, three different data analyzes were carried out, namely temporal, spatial, and spatial-temporal analysis. The results of the study obtained three perspectives, namely from the time period, handling of forest fire cases because they have an impact on the future as seen from the ARIMA model. Regarding spatiality, the distribution of hotspots spread to surrounding areas that were heavily affected by hotspots as seen from the contour map using Kriging interpolation. Finally, regarding spatiality and temporality, forest fire projections show that locations that are close together and have a history of being affected by forest fires have a strong potential for the distribution of forest fire cases as seen from the GSTAR space-time model.
Mathematical Modeling and Integration of Machine Learning-Based Prediction System on E-Learning Platform to Improve Students' Academic Performance Farida, Anisatul; Atina, Vihi; Suwandi, Djatmiko
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.v9i3.30994

Abstract

The purpose of this study was to develop and integrate a student academic performance prediction system into an e-learning platform using a mathematical modelling approach combined with machine learning algorithms. The method employed was Research and Development (R&D), encompassing stages of needs analysis, mathematical modelling, development of a machine learning-based prediction system, and implementation and evaluation. The study was conducted at Duta Bangsa University, Surakarta, involving 100 students from the Informatics Engineering study program. Data were collected through the e-learning platform, covering student activity logs such as access frequency, quiz scores, assignment completion time, and forum participation. This behavioral data was then analyzed using supervised learning algorithms, namely logistic regression and decision tree, to build a predictive model for academic performance. The resulting predictive system was integrated into the e-learning platform to deliver risk notifications and adaptive learning material recommendations automatically. To measure the improvement in academic performance, a validated academic achievement test was administered as both a pre-test and a post-test to the experimental group. This test consisted of multiple-choice and short-answer items aligned with the course learning objectives. The results showed that the decision tree model achieved a prediction accuracy of 87.4%, while logistic regression reached 81.2%. Evaluation of the system’s effectiveness using the pre-test and post-test scores revealed a significant increase in students’ academic performance. Statistical analysis with a paired t-test yielded a significance level of p < 0.001, indicating that the adaptive prediction system effectively supports more personalized and impactful learning. This study contributes to the advancement of machine learning-based prediction systems in e-learning by designing and implementing a model that leverages real student activity data. The system enables early detection of academic risks and provides automated, adaptive content recommendations, thus fostering personalized and data-driven learning in higher education. Its practical implementation helps students identify learning weaknesses promptly and receive appropriate supporting materials immediately, promoting proactive and self-regulated learning behavior. 
Analyzing Multiclass Land Cover and Spatial Point Patterns on Sentinel-2 Imagery Using Machine Learning and Deep Learning Nabilah, Muna Faizatun; Fauzan, Achmad
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.v9i2.29683

Abstract

Land cover conversion around educational centers, such as universities, is an inevitable consequence of increasing urban activity. The development of boarding houses, commercial zones, and other infrastructure often follows the expansion of academic institutions. To support sustainable spatial planning, early identification of land cover and analysis of spatial distribution patterns are crucial for zoning regulation and infrastructure management. This study focuses on classifying land cover and analyzing spatial patterns around Universitas Riau (UNRI) using Sentinel-2 satellite imagery with a 10-meter spatial resolution. The research applied a supervised classification approach, utilizing spectral bands—specifically Near-Infrared (NIR) and Short-Wave Infrared (SWIR)—as explanatory variables. The response variable was land cover, categorized into vegetation, non-vegetation, and water. Three machine learning models—Support Vector Machine (SVM), Naïve Bayes (NB), and Backpropagation Neural Network (BNN)—were compared based on overall accuracy and the Kappa coefficient. The models were trained and tested using a stratified 80-20 data split to ensure a balanced evaluation. Among the models, SVM demonstrated the highest accuracy, achieving an average of 91.15% in 2022 and 83.90% in 2023 with minimal variance, confirming its reliability for land cover classification. Spatially, non-vegetation areas were concentrated near major access routes and facilities, highlighting the influence of infrastructure development on land conversion. The study also identified potential growth zones within a 3–5 km radius from UNRI, emphasizing the need for anticipatory and sustainable land use policies. These findings support the formulation of spatial strategies aligned with Law No. 26 of 2007 on Spatial Planning and offer valuable insights for guiding urban development around academic hubs.
Spatial Panel Regression Modelling of Rainfall in Indonesia Saniyawati, Fang You Dwi Ayu Shalu; Astutik, Suci; Pramoedyo, Henny
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.v9i2.29882

Abstract

Rainfall is amount of water that falls to the earth's surface in the form of rain during a certain period of time, usually measured in millimeters. Rainfall data in Indonesia usually includes temporal and spatial dimensions, so the appropriate method for its analysis is spatial panel regression analysis. This study aims to identify factors that influence the amount of rainfall in Indonesia. This type of research is quantitative using secondary data from the central statistics agency website. The predictor variables used include air temperature, sunshine radiation, humidity, wind speed, and air pressure, while the response variable is amount of rainfall in 34 provinces in Indonesia. Spatial panel regression analysis is carried out using maximum likelihood estimation, which is used to estimate the regression coefficient and intercept that maximizes the likelihood of the existing data. Based on the lagrange multiplier test, spatial autocorrelation was found in the lag, so the appropriate model is SAR-FE. This model can overcome spatial autocorrelation by taking into account spatial interactions between locations, as well as controlling unobserved heterogeneity through fixed effects. The results show that sunshine radiation, humidity, and wind speed have significant effect on the amount of rainfall in Indonesia. The AIC value of SAR-FE model (-4.352594×〖10〗^(-13)) is smaller than SEM-FE model (-1.642001×〖10〗^(-12)), indicating that SAR-FE model is better at explaining the data.
Formation of Linear Programming Models of Water Price Compliant to the Regulation of Ministry of Home Affairs, Indonesia Hek, Tan Kim; Hou, Amin
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.v9i2.29484

Abstract

Regional Drinking Water Company (PDAM) Tirtanadi manages Medan City's clean water. PDAM divides its clients into seven categories, with Group I being low-income areas that require water subsidies. PDAM does not yet have a mathematical model for water rates, but it depends on Minister of Home Affairs Regulation No. 23, which gives legal guidance. Since there is no mathematical model, this research uses linear programming (LP) to establish each group's minimal water tariff. PDAM bases its water pricing on the Minister of Home Affairs Regulation No. 23 of 2006 and uses the LP model. The LP model reduces complicated computations into linear equations, making them easier to comprehend and apply. The computed water rates match PDAM pricing for six consumer categories, according to the research. One group, Group II Block I, has a higher tariff ratio of 2.3:1. Model parameters are more susceptible to changes in costs, consumption volume, and payment capability of this group, causing this disparity. This study's new goal is to minimize high-water-use clients. Other groupings remained unchanged. LP models establish PDAM's minimum water rates and optimize water tariff calculation effectively and equitably using analytical methods. PDAM may utilize the LP model to determine pricing that cover all expenditures, but Minister of Home Affairs Regulation No. 23 of 2006 continues to keep tariffs cheap and not a social burden on Medan. This research suggests that additional monopolistic enterprises and other water resource management for 37 Indonesian provinces may utilize its minimum water selling price approach. In the mathematical equation, the LP model is correct. Thus, PDAM Tirtanadi may use it to calculate the minimal water selling price by considering economic considerations. Water firms must help PDAM researchers who are willing to supply data, including polite service from each division's workers.
Mathematics in Transition: Assessing the Impact of Curriculum Changes on Student Performance Metrics Siregar, Juni Satria; 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.v9i3.30514

Abstract

Curriculum changes in higher education, especially in mathematics, are intended to align academic content with scientific advancements and evolving workforce demands; however, such reforms often bring unintended academic challenges for students. In Indonesia, recent changes in the 2016, 2020, and 2024 mathematics curricula introduced shifts in course credit allocations, course learning outcomes (CLOs), material scope, instructional methods, and evaluation systems. This study specifically aims to evaluate the impact of these curriculum changes on student academic performance across five core mathematics courses: Introduction to Data Science, Calculus 1, Calculus 2, Linear Algebra, and Mathematical Statistics. Employing a quantitative, exploratory approach, the research analyses academic records from 586 students using descriptive statistics and visualisation techniques such as boxplots and bar-line charts. The findings reveal fluctuating average grades and a general decline in pass rates, particularly under the 2024 curriculum, which introduced more complex CLOs, deeper content coverage, and application-oriented assessments. These results highlight the urgent need to balance curriculum innovation with student readiness and provide valuable insights for curriculum development and educational policy planning. 
Modeling the Human Development Index of the West Nusa Tenggara Province using Panel Data Regression Astuti, Alfira Mulya; Islamiyah, Pizatul; Choir, Achmad Syahrul; Setambah, Mohd Afifi Bahurudin
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.v9i3.31055

Abstract

The human development index is the primary indicator used to measure the level of success of human development. It is important to study because the human development index can provide a more comprehensive picture of a region or country's progress in improving its people's quality of life and guide the government in designing more effective development policies, identifying social gaps, and directing efforts to improve the quality of life of society as a whole. This research aims to identify the most significant component of the HDI calculation through the application of standardized coefficients and to analyze the influence of the number of poor people on the human development index in West Nusa Tenggara (NTB) province during 2010-2023 period. This research is quantitative in essence. The independent variables were life expectancy at birth, expected years of schooling, mean years of schooling, adjusted per capita expenditure, and number of poor people. The individual observation units in this study were 10 districts/cities in the NTB province. Data were sourced from Badan Pusat Statistik (BPS) NTB Province and analyzed using the panel regression method. The results of model selection show that the Fixed Effect Model is the best model for modeling the human development index in NTB province. The adjusted per capita expenditure had the greatest impact on the human development index of NTB Province in 2010–2023. The expected years of schooling was the variable that contributed the least to the entire components of the HDI in NTB province. The number of poor people had a significant effect on the human development index of NTB province from 2010 to 2023.  

Page 48 of 54 | Total Record : 540

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