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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 Social Media and Smartphone Use on Mathematics Learning Interest among Students at SMP Negeri 2 Singingi Intan Nurhakiki, Desti; Suripah; Istikomah, Endang; Indriati, Mefa; Wulandari, Happy
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 expansion of digital technology has transformed students’ learning experiences, yet its influence on mathematics learning interest remains inconclusive because social media and smartphone use have often been examined separately. This study investigated the individual and simultaneous relationships between social media use, smartphone use, and mathematics learning interest among junior high school students. A quantitative ex post facto cross-sectional design was employed involving all 76 eighth-grade students at SMP Negeri 2 Singingi, Indonesia, using saturated sampling, while data were collected through three validated Likert-scale questionnaires and analyzed using multiple linear regression after the required statistical assumptions were satisfied. The findings revealed that both social media use and smartphone use were significant positive predictors of mathematics learning interest, and the regression model demonstrated a statistically significant overall fit. The results suggest that digital technology contributes positively to students’ affective engagement with mathematics when it is used purposefully to access educational content, facilitate learning interaction, and support independent study rather than merely for recreational activities. Unlike previous studies that predominantly investigated digital platforms or mobile devices separately or focused primarily on academic achievement, this study integrates both dimensions of students’ digital learning behaviour within a single analytical framework while positioning mathematics learning interest as the primary educational outcome. The findings contribute to the literature on digital mathematics education by providing empirical evidence from an Indonesian junior high school context and highlighting the importance of digital literacy, self-regulation, and pedagogically guided technology integration in promoting meaningful and sustainable mathematics learning.
A Digital-Based Flipped Classroom Strategy for Enhancing Students' Mathematical Representation Ability Tanjung, Meylia Hapni; Reflina, Reflina
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

Students' mathematical representation ability remains one of the fundamental competencies that is often underdeveloped in mathematics learning, particularly in topics requiring abstract reasoning such as trigonometric functions. Although the Flipped Classroom approach has been widely implemented, empirical evidence regarding its effectiveness in improving mathematical representation ability in Indonesian secondary education is still limited. This study aimed to examine the effectiveness of a Flipped Classroom strategy in enhancing students' mathematical representation ability. A quasi-experimental design with a pretest–posttest control group was employed involving 65 eleventh-grade students from a public senior high school in Indonesia, consisting of 32 students in the experimental group and 33 students in the control group. The experimental group received instruction through the Flipped Classroom strategy, whereas the control group was taught using conventional instruction. Data were collected using a mathematical representation ability test and analyzed through descriptive statistics, normality and homogeneity tests, and an independent samples t-test. The findings revealed that both groups had comparable initial abilities; however, the experimental group achieved a significantly higher posttest mean score (81.75) than the control group (70.24). Statistical analysis confirmed a significant difference between the two groups (t = 12.67, p < .001), indicating that the Flipped Classroom strategy effectively improved students' mathematical representation ability. These findings contribute to the growing evidence supporting student-centered, technology-enhanced learning environments for mathematics education and suggest that the Flipped Classroom can be adopted as an effective instructional strategy to foster mathematical representation skills in secondary schools.
On Derivations of Pseudo BN-algebras Mahiroh; Sri Gemawati; Syamsudhuha
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

Derivation theory plays a fundamental role in implication-based algebra by providing operator frameworks that reveal the structural behavior of algebraic systems. Although numerous derivation concepts have been established for BN-algebras and related implication algebras, an appropriate derivation framework for pseudo BN-algebras has remained unavailable because their dual-operation structure cannot be accommodated by existing derivation formulations. This study aims to establish a unified derivation framework for pseudo BN-algebras through the introduction of algebraically compatible operator mappings. Using a deductive mathematical approach, two auxiliary binary operations, ⊛ and , are constructed to define Type 1 and Type 2 (l,r)-derivations, (r,l)-derivations, and left derivations. Their fundamental properties are then investigated through formal definitions, propositions, and rigorous mathematical proofs. The obtained results show that both derivation systems satisfy regularity conditions, preserve essential identities involving the distinguished zero element, and maintain structural consistency with the defining axioms of pseudo BN-algebras. More importantly, the proposed framework demonstrates that derivation theory for pseudo BN-algebras cannot be obtained by directly extending existing derivation concepts but instead requires new algebraic constructions that are intrinsically determined by the interaction of their two binary operations. Consequently, this study establishes the first derivation framework for pseudo BN-algebras, broadens the scope of derivation theory within implication-based algebra, and provides a rigorous theoretical foundation for future investigations of generalized derivations, derivation-induced ideals, homomorphisms, congruence relations, fuzzy derivations, and other operator structures on generalized implication algebras.
PCA-Enhanced Machine Learning Framework for Child Stunting Prediction Using Household and Socioeconomic Factors Ria Indah Sari; Adnan, Arisman; Syamsudhuha, Syamsudhuha
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

Childhood stunting continues to pose a major public health concern because its underlying determinants arise from complex household and socioeconomic interactions that are difficult to capture using conventional analytical approaches. Although machine-learning techniques have shown considerable potential for health prediction, limited attention has been given to understanding how different levels of dimensionality reduction influence classifier performance when analysing high-dimensional survey data. Addressing this gap, this study developed a Principal Component Analysis (PCA)-enhanced machine-learning framework for childhood stunting prediction using secondary data from the 2023 Indonesian Ministry of Health survey in Riau Province. Following preprocessing, 2,976 valid observations with 16 predictor variables were transformed into 117 numerical features, after which PCA generated three feature representations retaining 89.30%, 94.28%, and 98.44% of the total variance. Twelve supervised machine-learning algorithms were subsequently evaluated using precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The empirical results demonstrated that preserving a greater proportion of variance improved predictive performance across most classifiers. Among all evaluated models, K-Nearest Neighbours combined with 45 principal components achieved the strongest overall performance, yielding a precision of 0.710, recall of 0.771, F1-score of 0.739, and AUC of 0.809. These findings provide empirical evidence that integrating PCA with machine-learning algorithms offers a reproducible and computationally efficient framework for supporting evidence-based nutritional surveillance and advancing data-driven childhood stunting prediction.
Beyond Algorithmic Coloniality: Toward a Decolonial Framework for AI-Mediated International Education in Global South Contexts Age, Terungwa James
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 expansion of artificial intelligence (AI) in international education has transformed the processes through which knowledge is produced, distributed, and governed, while simultaneously creating new challenges related to inequality, representation, and epistemic justice. This study examines algorithmic coloniality as a critical issue in AI-mediated international education, particularly within Global South contexts where technological systems are frequently shaped by dominant epistemological assumptions and unequal structures of knowledge production. Employing a qualitative conceptual research design through critical conceptual synthesis, this study integrates perspectives from artificial intelligence, international education, decolonial theory, epistemic justice, and Global South epistemologies to develop a new theoretical framework. The analysis identifies five interconnected dimensions of algorithmic coloniality: epistemic exclusion, linguistic dominance, data colonialism, digital dependency, and algorithmic governance. To address these challenges, this study develops the Decolonial Ayatutu–Ubuntu Framework, which integrates epistemic reclamation, relational ethics, algorithmic accountability, critical AI literacy, and participatory governance as foundations for more equitable AI-mediated education. The framework extends existing approaches to AI governance by shifting attention beyond algorithmic efficiency and procedural fairness toward knowledge plurality, cultural recognition, and contextual responsibility. This study contributes to global debates on responsible artificial intelligence and international education by positioning Global South epistemologies as sources of theoretical innovation rather than merely contexts affected by technological transformation, providing a conceptual foundation for rethinking AI-mediated international education beyond algorithmic efficiency toward a more inclusive, culturally responsive, and epistemically just future.
Comparing Data Splitting Ratios in Student Sentiment Analysis Using the Naïve Bayes Algorithm Rofiatus Sabila, Annisa Zulfi; Asyhari, Ardian; Pitri, Rizka
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

Hybrid learning has become an integral component of higher education, making student feedback an important source of information for evaluating learning quality and improving instructional practices. Although sentiment analysis has been widely applied in educational research, limited attention has been given to how different training–testing data-splitting ratios influence classification performance. This study aimed to compare the performance of the Naïve Bayes algorithm using three data-splitting ratios (60:40, 70:30, and 80:20) for classifying students' sentiments toward hybrid learning at UIN Raden Intan Lampung during the second semester of the 2024–2025 academic year. A quantitative approach was employed to analyze 4,728 student opinions collected from 394 respondents. The analytical procedure consisted of text preprocessing, sentiment labeling using the InSet Lexicon, TF-IDF feature extraction, and Naïve Bayes classification, followed by performance evaluation using accuracy, precision, recall, and F1-score. The findings revealed that 70.3% of the opinions expressed positive sentiment, while 29.7% were negative, indicating that students generally perceived hybrid learning favorably despite challenges related to instructional delivery and internet connectivity. The comparison of data partitions showed similar overall performance, with accuracy values of 79% for the 60:40 split, 78% for the 70:30 split, and 79% for the 80:20 split. However, the 80:20 configuration achieved the strongest class-level performance, producing an F1-score of 87% for positive sentiment and 51% for negative sentiment. These findings demonstrate that Naïve Bayes provides stable performance across different data partitions, while the 80:20 ratio offers the most balanced configuration for sentiment classification within the scope of this dataset and analytical framework.
A Comparative Analysis of Support Vector Machine (Svm) and Svm–Principal Component Analysis (Pca) in Breast Cancer Diagnosis Sari, Okvia Metha Permata; Awawin Mustana Rohmah; M.Syaiful Pradana
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

Although dimensionality reduction is widely incorporated into machine-learning pipelines for breast cancer diagnosis, its independent contribution remains unclear when classifiers are optimized under identical experimental conditions. This study evaluates Support Vector Machine (SVM) and Principal Component Analysis–Support Vector Machine (PCA-SVM), both optimized through Random Search, to determine whether PCA provides a measurable classification benefit. The Wisconsin Diagnostic Breast Cancer dataset, consisting of 569 samples, 30 numerical features, and benign and malignant classes, was analyzed. Preprocessing included Interquartile Range-based outlier removal, label encoding, Min-Max normalization, and Synthetic Minority Over-sampling Technique applied to the training data. PCA retained 95.30% of cumulative explained variance and reduced the feature space from 30 variables to 10 principal components. Both pipelines used identical preprocessing, optimization, evaluation metrics, and train-test partitions of 60:40, 70:30, and 80:20. Performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. The optimized SVM achieved its best performance at the 70:30 split, with 98.33% accuracy, 98% precision, 98% recall, 98% F1-score, and a ROC-AUC of 0.9998. Under the same split, PCA-SVM reached 97.22% accuracy and remained below SVM across the reported metrics. Despite reducing dimensionality by 66.7%, PCA did not improve predictive performance, indicating that original features retained important class-discriminative information. These findings show that preserving the original feature representation was more effective than PCA-based reduction and that dimensionality reduction should be justified empirically rather than adopted as a routine preprocessing step.
The Influence of The Joyful Learning Model Assisted by Wordwall Type Open the Box on Mathematical Concept Understanding Pebriani, Sarah Rizki; Karo-Karo S, Isran Rasyid
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

Despite the growing adoption of digital game-based learning in mathematics classrooms, empirical evidence explaining how technology-supported pedagogical models foster students’ mathematical conceptual understanding remains limited. This study investigated the effect of the Joyful Learning model assisted by the Wordwall Open the Box template on students’ mathematical conceptual understanding. A quasi-experimental pretest–posttest control group design was implemented at MAS Alwashliyah 22 Tembung, Indonesia, from 29 April to 19 May 2026. Eighty eleventh-grade students selected through purposive sampling were assigned to an experimental class (n = 40) and a control class (n = 40). Data were collected using an essay-based conceptual understanding test developed from 15 items, of which eight met the validity criteria, with a reliability coefficient of 0.7951. Data analysis included the Liliefors normality test, the F-test for homogeneity, and an independent-samples t-test. The experimental class improved from a mean pretest score of 46.975 to 86.525, whereas the control class increased from 47.150 to 57.525, with respective mean gains of 39.550 and 10.375. The difference between the two groups was statistically significant (t = 18.038), indicating that students experiencing Joyful Learning supported by Wordwall achieved substantially stronger conceptual development than those receiving conventional instruction. These findings suggest that the educational value of digital game-based learning lies not in technological novelty itself but in its pedagogically structured integration with active participation, collaborative knowledge construction, and formative feedback. This study contributes to the literature by providing empirical evidence that constructivist pedagogy and purposeful technology integration operate synergistically to promote meaningful mathematical conceptual understanding.
The Effect of the Think-Pair-Share Model Assisted by Animated PowerPoint on the Mathematical Representation Ability of Generation Alpha Students Iramadhania, Fahira; Hasanah, Rusi Ulfa
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 representation ability is an essential competency that enables students to represent, organize, and communicate mathematical ideas through visual, symbolic, and verbal forms. However, many students still experience difficulties in transforming mathematical problems into different forms of representation and explaining solution processes accurately, indicating that this competency remains insufficiently developed. Therefore, this study aimed to determine the effect and the magnitude of the effect of the Think-Pair-Share (TPS) learning model assisted by animated PowerPoint on the mathematical representation ability of Generation Alpha students in learning cubes and rectangular prisms. This study employed a quantitative approach using a quasi-experimental design with a Posttest-Only Control Group Design. The sample was selected through cluster random sampling, with Class VII-4 serving as the experimental group and Class VII-2 as the control group, each consisting of 31 students. Data were collected using a validated and reliable six-item open-ended mathematical representation test and were analyzed using descriptive and inferential statistics, including normality, homogeneity, t-test, and effect size analyses. The findings showed that the experimental group achieved a higher mean posttest score (82.890) than the control group (56.142). The t-test results indicated a t value of 7.559 with a significance value of 0.000 < 0.05, indicating a significant effect of the TPS model assisted by animated PowerPoint on students' mathematical representation ability. Furthermore, the effect size was 1.92, indicating a high effect. Therefore, the animated PowerPoint-assisted TPS model is effective in improving students' mathematical representation ability and can serve as an alternative instructional approach for mathematics learning.
Development and Effectiveness Test of a Gamification-Based Mathematics Learning Media Integrated With Vocational Problems on Numeracy Literacy Ability Solehudin, Seva Zidan; Maharani, Anggita; Nasir, H.Fuad
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

Despite the increasing adoption of digital learning platforms in vocational education, mathematics instruction often remains procedural and provides limited opportunities for students to develop contextual numeracy skills relevant to workplace situations. This study aimed to develop and examine the preliminary effectiveness of a gamification-based mathematics learning medium using Quizizz that integrates vocational problems with the Assessment of Minimum Competency (AKM) numeracy processes of formulating, employing, and interpreting. The study employed a Research and Development approach using the ADDIE model. A needs analysis involved 48 Grade X vocational students in the Office Management and Business Services program, while the implementation stage involved 29 students. The developed product was validated by two mathematics education lecturers and two mathematics teachers. Data were analyzed using percentage analysis, normalized gain, and a paired-samples t-test procedure. The validation results categorized the learning medium as highly valid after revisions to instructional clarity, language, illustrations, and visual presentation. Students' mean numeracy score increased from 46.36 on the pretest to 61.69 on the posttest, with a mean N-Gain of 0.23 (minimum = −0.38, maximum = 0.78, SD = 0.32), indicating modest overall improvement accompanied by considerable variation in individual learning gains. These findings provide preliminary evidence that integrating vocational contexts with AKM-oriented gamification offers a valid supplementary environment for contextual numeracy practice. The study contributes to the growing literature on context-sensitive digital mathematics learning by demonstrating how vocational problem contexts and AKM numeracy processes can be integrated within a gamified instructional design while highlighting the need for sustained implementation to achieve stronger learning outcomes.

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