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Desimal: Jurnal Matematika
Published by Universitas Islam Negeri Raden Intan Lampung
ISSN : 26139073     EISSN : 26139081     DOI : -
Core Subject : Education, Social,
Education Mathematics Social Sciences
Desimal: Jurnal Matematika, particularly focuses on the main issues in the development of the sciences of mathematics education, mathematics education, and applied mathematics. Desimal: Jurnal Matematika published three times a year, the period from January to April, May to Augustus, and September to December. This publication is available online via open access.
Arjuna Subject : -
Articles 388 Documents
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The Effect of the Discovery Learning Model Using PowerPoint Media on Students' Mathematical Critical Thinking Skills Regarding Systems of Linear Equations in Two Variables (SLPDV) Romadona, Sahrul; Karo-karo, IsranRasyid
Desimal: Jurnal Matematika Vol. 9 No. 2 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/

Abstract

Mathematical critical thinking is increasingly recognized as a fundamental competency in mathematics education because it enables students to analyze information, evaluate solution strategies, and justify mathematical conclusions. Despite the growing adoption of inquiry-oriented learning, limited empirical evidence explains how commonly available classroom technology can effectively support the cognitive processes underlying Discovery Learning to improve students' mathematical critical thinking. This study investigated the effect of PowerPoint-assisted Discovery Learning on eighth-grade students’ mathematical critical thinking in Systems of Linear Equations in Two Variables. A quantitative quasi-experimental approach employing a nonequivalent control group design was conducted with 50 students, consisting of 25 students in the experimental group and 25 in the control group. Data were collected using a validated essay-based mathematical critical thinking test comprising nine valid items with excellent reliability (Cronbach’s α = .9669) and analyzed using descriptive statistics and an independent-samples t-test after normality and homogeneity assumptions were satisfied. Students in the experimental group significantly outperformed those in the control group (87.40 vs. 58.08), t(48)=16.1489, p<.001., and the difference was statistically significant, t (48) = 16.1489, p < .001. These findings demonstrate that PowerPoint functions as an instructional scaffold supporting exploration, verification, and mathematical reasoning within Discovery Learning. The study contributes empirical evidence that integrating structured visual scaffolding with inquiry-oriented pedagogy provides an accessible and effective approach for strengthening mathematical critical thinking in lower-secondary mathematics education.
Rainfall Prediction in Medan City Using Support Vector Machine–Based Machine Learning Models M. Hafizh Ramadhan; Cipta, Hendra
Desimal: Jurnal Matematika Vol. 9 No. 2 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/jm.v9i2.32670

Abstract

Accurate rainfall classification remains a persistent challenge because precipitation is governed by highly nonlinear atmospheric processes, while routine meteorological observations are often limited, particularly in humid tropical environments. Although recent advances in machine learning have substantially improved rainfall prediction, relatively few studies have examined the capability of interpretable models using compact monthly meteorological datasets. This study developed and evaluated a linear Support Vector Machine (SVM) for classifying monthly rainfall in Medan City using air temperature, relative humidity, sunshine duration, and wind speed as predictor variables. The analysis was conducted using 23 monthly observations collected by BBMKG Region I Medan from July 2024 to May 2026. Rainfall was categorized using the sample mean threshold of 274.7 mm, producing seven higher-rainfall and sixteen lower-rainfall observations. Predictor variables were normalized using the Min–Max method, and the linear SVM parameters were estimated through Sequential Minimal Optimization. The resulting model produced an accuracy of 70%, precision of 50%, recall of 43%, and an F1-score of 46%, with optimization identifying two support vectors and a decision boundary characterized by positive coefficients for relative humidity and wind speed and negative coefficients for air temperature and sunshine duration. These findings indicate that the proposed model successfully identified meaningful rainfall-classification patterns while exhibiting greater capability in recognizing lower-rainfall than higher-rainfall conditions. The study contributes an interpretable machine-learning framework for rainfall classification under limited observational conditions and demonstrates the importance of balancing predictive performance with model transparency in tropical hydrometeorological applications.
The Effect of a Deep Learning Instructional Approach Supported by Animated PowerPoint Media on Junior High School Students' Numeracy Literacy Skills Yasri, Bayhaqi; Siregar, Tanti Jumaisyaroh
Desimal: Jurnal Matematika Vol. 9 No. 2 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/

Abstract

Numeracy literacy has become a fundamental competency for enabling students to interpret quantitative information, construct mathematical representations, and make informed decisions in authentic contexts. Despite its importance, many junior high school students continue to experience difficulty applying mathematical concepts beyond routine procedural tasks, indicating the need for instructional approaches that promote deeper conceptual understanding. This study investigated the effect of a Deep Learning instructional approach supported by animated PowerPoint media on students’ numeracy literacy skills while examining how progressive visual scaffolding enhances meaningful mathematical learning. A quantitative quasi-experimental study employing a pretest–posttest control group design was conducted with 61 eighth-grade students at SMP Negeri 2 Namorambe, Indonesia, selected through cluster random sampling and assigned to an experimental group (n = 32) and a control group (n = 29). Data were collected using a validated essay-based numeracy literacy test and analyzed through descriptive statistics, the Shapiro–Wilk normality test, Levene’s homogeneity test, an independent-samples t-test, and Cohen’s d. Students receiving the Deep Learning instructional approach significantly outperformed those receiving conventional instruction, achieving a posttest mean of 74.09 compared with 51.07 in the control group, t(59) = 5.514, p < .001, with a large effect size (d = 1.414). The findings contribute empirical evidence that pedagogical integration, rather than technological sophistication alone, is the principal mechanism through which technology enhances numeracy-oriented mathematics learning in lower-secondary education.
Differences in Mathematical Problem-Solving Ability of Grade XI Senior High School Students on Matrix Material Between Educational Card Game-Assisted Strategy and Expository Learning Strategy Dewi, Nurika Kartika; Yahfizham
Desimal: Jurnal Matematika Vol. 9 No. 2 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/

Abstract

Mathematical problem-solving ability is a fundamental competency in mathematics education because it enables students to interpret problems, develop solution strategies, implement mathematical procedures, and evaluate the validity of their answers. Although game-based learning has been increasingly incorporated into mathematics instruction, empirical evidence comparing card-assisted educational games with conventional expository instruction for improving students’ mathematical problem-solving ability on matrix material remains limited. This study aimed to examine the difference in mathematical problem-solving ability between Grade XI students taught using a card-assisted educational game strategy and those taught using an expository learning strategy. A quantitative quasi-experimental study employing a pretest-posttest comparison group design was conducted at SMA Swasta IT Ummi Ayuni Perbaungan, Serdang Bedagai Regency, Indonesia, involving 40 students selected through total sampling. Data were collected using a validated mathematical problem-solving test and analyzed using appropriate parametric statistical procedures. The findings revealed that students in the experimental group achieved a higher posttest mean score (80.55) than those in the expository group (67.90), with a statistically significant difference (t(38) = −5.347, p < .001). These findings indicate that the card-assisted educational game strategy provided a more effective learning environment for developing students’ mathematical problem-solving ability on matrix material than the expository learning strategy. The study contributes empirical evidence supporting the integration of structured, non-digital educational games as an effective instructional alternative for secondary mathematics learning.
Comparison of K-Means and K-Median Algorithms for Clustering Poverty Indicator Data in North Sumatra Province Hasibuan, Mey Rani; M.Si, Ismail Husein
Desimal: Jurnal Matematika Vol. 9 No. 2 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/

Abstract

Poverty in North Sumatra exhibits considerable spatial heterogeneity, yet comparative evidence on the performance of clustering algorithms for identifying multidimensional regional poverty patterns remains limited. This study aimed to compare K-Means and K-Median in clustering 33 regencies and municipalities using five poverty-related indicators averaged over 2022–2025: percentage of poor population, poverty line, poverty gap index, poverty severity index, and adjusted per capita expenditure. A quantitative descriptive-comparative design was applied using secondary data from the Central Statistics Agency of North Sumatra Province. All variables were transformed through Min–Max normalization, the Elbow Method was used to determine the optimal number of clusters, and clustering performance was evaluated using the Silhouette Coefficient. The analysis identified three clusters for both algorithms. K-Means produced cluster sizes of 28, 2, and 3 regions, whereas K-Median generated 21, 2, and 10 regions. A notable finding was that both methods consistently placed South Nias and North Nias in the same distinct cluster, characterized by comparatively high poverty gap and poverty severity values and low adjusted per capita expenditure. K-Means achieved a higher Silhouette Coefficient than K-Median, with values of 0.2953 and 0.1515, respectively, indicating comparatively better overall clustering performance. These findings show that algorithm selection influences both cluster structure and the substantive interpretation of multidimensional poverty patterns. The study provides empirical support for targeted and context-sensitive regional poverty alleviation policies across the diverse administrative areas of North Sumatra.
Students' Creative Thinking Skills in Solving Three-Variable Linear Equation System (SPLTV) Problems: A Gender Perspective Azizah, Lailatul Nur; Putri, Anggita Oktaviana
Desimal: Jurnal Matematika Vol. 9 No. 2 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/

Abstract

Creative thinking is essential in mathematics because it enables students to generate varied, flexible, and original solutions to non-routine problems. This study investigated students’ creative thinking skills in solving Three-Variable Linear Equation System (SPLTV) problems from a gender perspective. A qualitative descriptive design involved 15 eleventh-grade students from a State Islamic Senior High School in Malang Regency, Indonesia. Data were collected through two open-ended SPLTV tasks and interviews. Five focal participants were selected purposively based on gender and creative-thinking ability categories. Their written responses and interview explanations were analyzed using the indicators of fluency, flexibility, and novelty, supported by data reduction, data display, conclusion drawing, and technique triangulation. The findings showed that all participants demonstrated fluency by producing several correct alternatives. However, flexibility and contextual novelty were identified only in the high-ability female participant, who used both elimination and matrix determinant methods. Male and female participants in the moderate and low categories showed similar patterns, as they could generate multiple answers but relied on one familiar strategy and did not produce a distinct approach. These results indicate that creative-thinking dimensions do not necessarily develop simultaneously and that ability level, strategic knowledge, and experience may be more influential than gender alone. The study provides a contextualized account of mathematical creativity in open-ended SPLTV problem solving and highlights the value of instruction that promotes comparison, justification, and evaluation of multiple solution methods.
The Effect of Generative Artificial Intelligence-Assisted Project-Based Learning on Vocational High School Students' Statistical Literacy and Critical Thinking Skills Lestari, Siti; Setyawati, Astri
Desimal: Jurnal Matematika Vol. 9 No. 2 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/

Abstract

The rapid advancement of Generative Artificial Intelligence (GenAI) has created new opportunities to enhance mathematics instruction by supporting authentic, inquiry-based learning. However, empirical evidence regarding the integration of GenAI into Project-Based Learning (PjBL) to improve statistical literacy and critical thinking among vocational high school students remains limited. This study aimed to examine the effect of GenAI-assisted Project-Based Learning on students' statistical literacy and critical thinking skills. A quasi-experimental research design employing a non-equivalent control group design was conducted with 61 eleventh-grade vocational high school students, comprising 32 students in the experimental group and 29 students in the control group. The experimental group received Project-Based Learning integrated with Generative Artificial Intelligence, while the control group experienced conventional Project-Based Learning. Data were collected using validated essay-based tests of statistical literacy and critical thinking and analyzed using descriptive statistics, Shapiro–Wilk normality tests, Levene's homogeneity tests, independent-samples t-tests, normalized gain (N-gain), and Cohen's d effect size. The results revealed that students in the experimental group achieved significantly higher posttest scores and learning gains than those in the control group for both statistical literacy (t = 5.87, p < .001; d = 1.52) and critical thinking (t = 5.12, p < .001; d = 1.31). These findings indicate that integrating Generative Artificial Intelligence into Project-Based Learning provides substantial educational benefits by promoting higher-order thinking and data interpretation skills. The study offers an evidence-based instructional framework for integrating artificial intelligence into vocational mathematics education to better prepare students for data-driven and technology-rich learning environments.
Mathematical Resilience as a Predictor of Academic Burnout Among Prospective Mathematics Teachers: Evidence from Partial Least Squares Structural Equation Modeling widyawati, Santi; Rosyidah, Ummi; Iskandar, Iskandar; Nurra, Reva Aisyah
Desimal: Jurnal Matematika Vol. 9 No. 2 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/

Abstract

Academic burnout has become an increasingly prevalent issue among prospective mathematics teachers due to escalating academic demands, intensive coursework, and persistent performance expectations. Within the framework of the Job Demands–Resources Theory, mathematical resilience is considered an important personal resource that may help students cope with academic challenges and reduce burnout. This study aimed to examine the effect of mathematical resilience on academic burnout among prospective mathematics teachers. A quantitative explanatory survey with a cross-sectional design was employed involving all 57 undergraduate students enrolled in the Mathematics Education Program through a census sampling technique. Data were collected using validated self-report questionnaires measuring mathematical resilience and academic burnout. The proposed structural model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The measurement model demonstrated satisfactory psychometric properties, including adequate indicator reliability, convergent validity, discriminant validity, and internal consistency reliability. The structural model revealed that mathematical resilience had a significant negative effect on academic burnout (β = −0.834, t = 11.223, p < 0.001), explaining 69.6% of the variance in academic burnout (R² = 0.696). These findings indicate that students with higher levels of mathematical resilience are less likely to experience academic burnout despite demanding academic environments. The study extends the application of the Job Demands–Resources Theory in mathematics teacher education by highlighting mathematical resilience as a key protective factor for students' psychological well-being. The findings also provide practical implications for designing resilience-based educational interventions to promote healthier and more sustainable learning experiences

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