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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 26 Documents
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Search results for , issue "Vol 9, No 4 (2025): October" : 26 Documents clear
Spatial Modelling of Child Malnutrition in East Java using Geographically Weighted Regression Gunawan, Syifa' Azizah Putri; Fortunata, Regina; Ana, Elly
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.v9i4.32103

Abstract

Child malnutrition is a persistent public health issue in East Java, Indonesia, characterized by uneven spatial distribution across its 38 regencies and cities. This study aims to model the prevalence of malnutrition among children under five using Geographically Weighted Regression (GWR) to identify locally significant determinants. Secondary data used in this study is prevalence of child nutritional status by regencies/cities in East java, taken from the 2023 Indonesian Health Survey, incorporating seven predictor variables: low birth weight prevalence, complete immunization coverage, exclusive breastfeeding, access to improved sanitation, number of community health posts (Posyandu), access to clean water, and poverty rate. Spatial dependence and heterogeneity were confirmed through Moran’s I (p = 0.009) and Breusch-Pagan tests (p = 0.024), validating the application of GWR. Spatial dependence and heterogeneity were confirmed through Moran’s I (p = 0.009) and the Breusch-Pagan test (p = 0.024), indicating the relevance of a spatial modelling approach. The best-performing model used an adaptive bi-square kernel (CV = 0.133; R² = 94.15%). All predictors exhibited spatial variability with statistically significant effects in specific regions (p < 0.05). In Tuban Regency, for instance, five variables including low birth weight, breastfeeding practices, and sanitation were significantly associated with malnutrition rates. These findings suggest that the relationship between predictors and malnutrition is not uniform across regions. GWR enables the identification of local patterns often overlooked by global models, offering a more accurate understanding of spatial disparities. The results provide strong evidence for developing targeted, region-specific public health strategies to address child malnutrition more effectively in East Java 
Mapping Food Insecurity: Spatial Modelling of Undernourishment Prevalence in Indonesia using Geographically Weighted Regression Saifudin, Toha; Chamidah, Nur; Ramadhina, Fidela Sahda Ilona; Al Hasri, Ilham Maulana; Trisa, Nadya Lovita Hana; Valida, Hanny; Setyawan, Muhammad Daffa Bintang
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.v9i4.32063

Abstract

Undernourishment is a major global issue, with significant impact observed in Indonesia. A method of assessing the prevalence of energy deficiency resulting from inadequate nutrition is through the Prevalence of Undernourishment (PoU) index. From 2019 to 2022, Indonesia's PoU increased gradually, reaching 10.21% in 2022, indicating growing undernourishment and unstable food availability. This study aims to utilize Geographically Weighted Regression (GWR) to identify and analyze the factors contributing to undernourishment. The data were obtained from the Central Bureau of Statistics (BPS) in 2024, covering 38 provinces in Indonesia. This study examined six factors: per capita spending, access to potable water, mean years of schooling, access to adequate sanitation, college participation rate, and mean food expenditure. The findings show that the GWR model outperformed the conventional model, demonstrating greater explanatory power by accounting for 96.1% of the spatial variation in undernourishment and achieving the lowest AIC value of 176.7052. These findings highlight the need for region-specific food security policies, particularly in eastern Indonesia. The results can inform targeted government interventions and guide future spatial econometric research on food security.
Monitoring PH of Shrimp Water using Progressive Max Chart Rosyadi, Niam; Syahzaqi, Idrus; Ibrahim, Auron Saka; Sihotang, Raja Van Den Bosch; Ahsan, Muhammad; Mashuri, Muhammad
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.v9i4.30255

Abstract

Control charts aim to reduce variability in the process and monitor for out-of- control processes. So far, the process of monitoring quality is usually carried out partially, namely monitoring the mean process and process variability. This approach is less effective and time-consuming because two separate charts must be created simultaneously. One alternative is to analyze both parameters simultaneously, such as through the Progressive Max Chart method (Mixed-Methods Research: Quantitative and Applied). The Progressive Max Chart is a control chart designed for monitoring both the mean and variability by considering the case of subgroup observations. This study uses a quantitative approach, combining primary data collection and simulations to generate findings through statistical analysis and quantifiable measurements. The purpose of this research is to compare methods such as the Progressive Max Chart, EWMA-Max, and Max Chart. The analysis results show that the Progressive Max Chart method performs better than the Max Chart and EWMA- Max Chart, both in terms of mean, variance, and mean-variance detection, for small shifts and large shifts. The control chart performance results provide optimal outcomes for monitoring out-of-control signals at subgroup sizes of n = 2, 3, 5. This is characterized by ARL₁ values that approach 1 more quickly. This method is applied to pH data from vannamei shrimp pond water located in Madura. The Progressive Max Chart method provides optimal results by maximizing the detection of in-control signals. Additionally, it is tested on synthesized data and demonstrates optimal performance in detecting both small and large shifts in mean, variance, and mean-variance.
Creative Geometry through Roof Modeling: Enhancing Angle Understanding via Deep Learning Herliana, Puspa; Hermawan, Hendry; Sulistiyana, Sulistiyana; Aslamiah, Aslamiah; Suriansyah, Ahmad
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.v9i4.33710

Abstract

This study aimed to evaluate the effectiveness of deep learning-based geometry instruction using miniature roof construction in improving students’ understanding of angles. A quasi-experimental design was conducted at SMPN 1 Kotabaru with 64 seventh-grade students divided into experimental and control groups. The experimental group participated in eight sessions of project-based learning that integrated contextual modeling and collaborative exploration, while the control group received conventional textbook-based instruction. Students’ comprehension of angle concepts including classification, measurement, and application was assessed using a validated geometry test and structured reflection journals. The results showed that students in the experimental group demonstrated significantly greater improvement in angle understanding compared to the control group. Statistical analysis confirmed the effectiveness of the intervention, with a large performance gap favoring the experimental group. These findings suggest that deep learning strategies, when combined with hands-on modeling and contextual relevance, can substantially enhance conceptual mastery in geometry education. 
Interpolation of Fire Radiative Power in West Kalimantan using Ordinary Kriging Fitriyana, Gita; Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul; Zuleha, Zuleha
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.v9i4.32643

Abstract

Forest fires are recurring environmental disasters with severe ecological and economic impacts, particularly in regions like West Kalimantan. One of the key indicators used to measure fire intensity is Fire Radiative Power (FRP). Accurate spatial prediction of FRP is essential to support early warning systems and mitigation strategies. This study is a quantitative descriptive research that applies a geostatistical spatial analysis technique, namely Ordinary Kriging interpolation, to predict FRP values in West Kalimantan for July, August, and September 2024. The data were obtained from satellite imagery (VIIRS NOAA-20), including latitude, longitude, and FRP values. Prior to modeling, data were tested for normality and found to follow a normal distribution. The spherical semivariogram model yielded the best fit for July and August with RMSE values of 0.046 and 0.011, respectively, while the Gaussian model was optimal for September (RMSE = 0.007). The results show spatial variation in FRP distribution across different regencies each month, with the highest estimated FRP values recorded in Kapuas Hulu (July: 63.56), Melawi (August: 69.00), and Ketapang (September: 55.27). Most areas demonstrated low fire intensity, as shown by the dominance of green zones on the prediction maps. However, localized red-yellow zones indicate areas with high fire potential, which shifted monthly. This study contributes by demonstrating the application of Ordinary Kriging in forest fire intensity mapping and highlights the importance of choosing an appropriate semivariogram model to enhance predictive accuracy. The resulting FRP prediction maps can serve as a valuable tool for policy planning and targeted fire prevention efforts.
Markov Chain Analysis of Bank Customer Migration: Implication for Financial Inclusion in Maritime Economies Hayati, Nahrul; Sulistyono, Eko; Gusrita, Rani
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.v9i4.32121

Abstract

Objectives: This study analyzes customer migration patterns among five major banks (BCA, BNI, BRI, BSI, and Bank Mandiri) in Batam’s strategic maritime economic zone using a Markov Chain model to assess long-term market dynamics and financial inclusion implications. The research aims to quantify interbank transition probabilities, to identify key switching drivers, and to develop targeted policy recommendations. Methods: Using a quantitative descriptive-analytical approach, we collected structured questionnaires from 250 Batam Institute of Technology academic members, capturing historical bank transitions and 5-point Likert-scale evaluations of eight switching factors. These factors included ATM/branch proximity, administrative fees, mobile/internet banking service, salary/ scholarship payment linkages, promotions/rewards, interest rates, family/friend recommendations, and Sharia compliance. Data were analyzed via Markov Chain modeling to project steady-state distributions. Results: The transition matrix revealed BCA’s superior retention (85.1%) compared to peers, with steady-state projections showing market dominance (32.44%), followed by Bank Mandiri (26.51%) and BSI (26.39%). Salary linkages (mean score: 3.45) and ATM accessibility (3.16) emerged as primary retention drivers, while BCA’s digital services (3.40) and low fee perception (3.67) explained its competitive edge. Paradoxically, BSI capitalizes on institutional salary systems (4.27) despite moderate Sharia compliance ratings (2.87). Implications: Three key policy directions emerge: hybrid digital-physical banking for coastal communities, Islamic financial ecosystem development, and fee transparency regulations. The study advances Markov Chain applications in behavioral finance while providing SEZ-specific insights for inclusive banking strategies.

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