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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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Forecasting Fluctuating Salt Product Demand Using ARIMAX with Price-Based Exogenous Information Lutfiyah, Lufi; Rinaldi, Achi; JL, Ana Risqa
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

Accurate demand forecasting is essential for salt-processing enterprises because fluctuating demand can affect production planning, inventory control, and distribution efficiency. This study examines the demand for table salt products at CV Restu Ibu by applying the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model using product price as an external predictor. Monthly sales and price data for 1 kg, 2 kg, and 4 kg packaging sizes from January 2018 to December 2025 were analyzed through the Box–Jenkins procedure, including stationarity testing, model identification, parameter estimation, diagnostic checking, forecast evaluation, and projection for 2026–2028. The results show that all demand series became stationary after first-order differencing. The best models were ARIMAX (2,1,1) for the 1 kg product, ARIMAX (1,1,1) for the 2 kg product, and ARIMAX (0,1,1) for the 4 kg product. Diagnostic testing using the Ljung–Box test confirmed that all selected models satisfied the white-noise residual assumption. The forecasting accuracy was excellent, with Mean Absolute Percentage Error values of 2.363%, 2.824%, and 1.559%, respectively. The forecast results indicate a declining demand trend for the 1 kg product, an increasing trend for the 2 kg product, and a fluctuating pattern for the 4 kg product. These findings demonstrate that incorporating price information improves the representation of demand dynamics and supports product-specific forecasting for price-sensitive salt products. The study also confirms that packaging size produces heterogeneous demand behavior, making differentiated forecasting more appropriate than a single aggregate demand model in practice.
Meta-Analysis: Heuristic Vee Learning Model and Mathematical Concept Understanding Ability Suherni, Herni; Supriadi, Nanang; Dinda Pratiwi, Dona
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 conceptual understanding is essential for enabling students to interpret mathematical ideas, connect concepts, and justify problem-solving procedures. However, many students still experience difficulty developing conceptual understanding because mathematics instruction often emphasizes procedural fluency rather than meaningful conceptual construction. The Heuristic Vee model has been proposed as an instructional approach that connects conceptual and methodological components of learning, yet empirical findings regarding its effectiveness remain fragmented across studies. This study conducted a group-contrast meta-analysis to examine the effect of the Heuristic Vee model on students’ mathematical conceptual understanding. Data were obtained from seven eligible empirical studies published between 2013 and 2023, selected through Publish or Perish from Google Scholar, Semantic Scholar, and Scopus. The included studies reported complete statistical information from experimental and control groups, including sample size, mean, and standard deviation. Effect sizes were calculated using standardized mean differences and analyzed with OpenMEE and JASP. The random-effects model produced a significant large pooled effect size of 2.529, indicating that Heuristic Vee-based instruction positively influenced students’ mathematical conceptual understanding. Subgroup analysis showed a significant large effect at the junior secondary level and in the Indonesian context, whereas higher education, Malaysia, and Turkey showed large numerical effects but limited statistical certainty due to small study representation. Heterogeneity was very high, and Egger’s test indicated possible funnel plot asymmetry, although fail-safe N and trim-and-fill analysis suggested relatively robust results. These findings indicate that Heuristic Vee is a promising instructional model for strengthening mathematical conceptual understanding when supported by appropriate scaffolding and contextual implementation.
Forecasting the Consumer Price Index in Bima City Using Random Forest Regression Naafia, Suci Faaza; Fathir; mustafidah, Hilyatul
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

Accurate forecasting of the Consumer Price Index (CPI) is essential for supporting regional inflation monitoring and evidence-based economic decision-making. However, city-level CPI forecasting remains limited, particularly in smaller urban areas where local expenditure patterns may differ from national trends. This study aimed to develop a predictive model for the General CPI of Bima City, Indonesia, using Random Forest Regression based on monthly expenditure-group CPI data. A quantitative predictive research design was employed using secondary data published by the Central Statistics Agency (BPS) of Bima City from January 2013 to December 2025, comprising 156 monthly observations. The dataset was divided into training and testing subsets using an 80:20 ratio. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²), while feature-importance analysis was performed to identify the most influential expenditure groups. The model achieved an MAE of 0.7626, an MSE of 1.4501, an RMSE of 1.2042, and an R² of 0.994, indicating strong predictive performance on the testing dataset. The actual–predicted comparison and scatter plot demonstrated close agreement between observed and predicted CPI values. Feature-importance analysis identified Finished Food, Health, and Foodstuffs as the dominant predictors contributing to model performance. These findings demonstrate that Random Forest Regression can effectively model the relationship between expenditure-group CPI variables and General CPI within the observed dataset while providing interpretable information regarding variable contribution. This study contributes to the growing application of interpretable machine learning in regional economic forecasting and offers a practical framework for supporting localized CPI monitoring and data-informed inflation analysis.
Modeling Stunting Cases in North Sumatra Using Generalized Poisson Regression with Maximum Likelihood Estimation Zam, Elma Dwi Ariana Aprilia; Nasution, Fakhrur Rozi; Lubis, Riri Syafitri
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

Stunting remains a major public health challenge in Indonesia, particularly in regions where case distribution varies substantially across administrative areas. This study aimed to model the number of stunting cases in 33 regencies and municipalities of North Sumatra Province in 2023 using Generalized Poisson Regression estimated through Maximum Likelihood Estimation. A quantitative explanatory design was applied using secondary aggregate data obtained from the North Sumatra Provincial Health Office. The response variable was the number of stunting cases, while the explanatory variables included the number of low birth weight infants, pregnant women consuming iron supplementation tablets, pregnant women experiencing chronic energy deficiency, and infants receiving vitamin A supplementation. The analysis involved descriptive statistics, multicollinearity testing, Poisson regression, overdispersion assessment, Generalized Poisson Regression, parameter significance testing, incidence rate ratio interpretation, and model comparison using the Akaike Information Criterion. The Poisson model showed severe overdispersion, with a deviance-to-degree-of-freedom ratio of 525.357, indicating that the equidispersion assumption was violated. Generalized Poisson Regression provided a substantially better model fit, with an AIC of 505.8721 compared with 14985.5643 for Poisson regression. The final model identified chronic energy deficiency among pregnant women as the only statistically significant predictor of stunting cases (β = 0.42770, p = 0.0004; IRR = 1.5337; 95% CI: 1.2099–1.9442). These findings demonstrate that Generalized Poisson Regression is appropriate for overdispersed stunting count data and highlight maternal nutritional vulnerability as an important factor associated with regional variation in stunting cases in North Sumatra.
Implementation of Risk-Based Business Licensing Policy at the Investment and One-Door Integrated Services Department in Sintang Regency Lestari, Reni Puji; Herawati , Netty; Arifin; Martoyo; Sudirman
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 implementation of risk-based business licensing through the Online Single Submission Risk-Based Approach (OSS-RBA) has become a key component of Indonesia’s public-service reform to improve regulatory efficiency, investment certainty, and digital governance. However, empirical evidence explaining how organizational capacity, policy interpretation, and policy application interact during local implementation remains limited. This study analyzes the implementation of OSS-RBA at the Investment and One-Stop Integrated Services Office (DPMPTSP) of Sintang Regency using Jones and Baird’s policy implementation framework. A qualitative single-case study was employed. Data were collected from seven purposively selected participants through semi-structured interviews, non-participant observation, and document analysis, and analyzed using theory-informed thematic analysis with source and method triangulation. The findings indicate that OSS-RBA has strengthened administrative transparency, clarified licensing requirements based on business-risk classifications, and improved institutional arrangements for licensing services. Nevertheless, implementation remains constrained by limited human resources, uneven technical competence, interagency coordination challenges, unstable digital infrastructure, and varying digital capabilities among business actors. These conditions collectively influence organizational performance, policy interpretation, and service delivery. The study demonstrates that effective implementation depends on the interdependent relationship among organizational capacity, policy interpretation, and policy application rather than technological adoption alone. It refines the empirical application of Jones and Baird’s framework by illustrating how these dimensions interact within digitally mediated licensing services and provides practical insights for strengthening institutional capacity, adaptive coordination, and user-oriented digital services.
An MSI-Transformed Multiple Linear Regression Model for Early Warning of Seafarers' Safety Compliance in a Maritime IDSS Prototype Fachrudin, Achmad Dhany; Novitasari, Novitasari; Zainuddin, Mochamad; Azhar, Fieranda Firdaus; Santoso, Agus Dwi; Basuki, Nanang
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

This study developed an MSI-transformed multiple linear regression model to estimate seafarers’ safety compliance from fatigue and occupational stress and implemented the resulting equation in a Python-based Maritime Intelligent Decision Support System prototype. Data were collected from 49 professional seafarers using a five-point Likert questionnaire measuring fatigue, occupational stress, and safety compliance. The ordinal responses were transformed into interval-based scores through the Method of Successive Intervals and analysed using ordinary least-squares regression. The overall model was statistically significant, F(2,46)=4.139, p=0.0222, with R2=0.153. Fatigue produced a negative but non-significant coefficient (β=−0.1020, p=0.489), while occupational stress also showed a negative coefficient (β=−0.2366, p=0.092). Thus, the predictors jointly accounted for a modest proportion of compliance variation, although neither showed an independently significant association at the 5% level. The estimated equation was embedded in the prototype to generate compliance scores and assign preliminary Safe, Alert, and Critical categories. Generative AI was restricted to translating deterministic outputs into concise mitigation narratives and did not calculate scores or determine categories. The study provides a transparent and reproducible workflow integrating ordinal-score transformation, interpretable regression, and computational decision support. The resulting architecture preserves traceability, supports human oversight, and demonstrates an exploratory early-warning framework for maritime safety compliance rather than a validated predictor of accidents or individual unsafe behaviour under current operational assessment conditions.
The effectiveness of problem-based learning on critical thinking skill: A meta-analysis study reviewed by implementation type, cluster, and geography Shofa, Gharitza Zahira; Meiliasari; Rahayu, Wardani
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 Problem-Based Learning (PBL) has been implemented to foster critical thinking, evidence regarding the consistency of its effectiveness across instructional formats, subject domains, and geographical contexts in Indonesia remains fragmented. This meta-analysis quantitatively synthesized the effects of PBL on students’ critical thinking while examining implementation type, subject cluster, and geographical region as moderators. A systematic search of Google Scholar was conducted in May–June 2026 for peer-reviewed studies published between 2019 and 2026. Following PRISMA 2020 procedures, 100 records were identified and 22 studies involving 1,513 participants were included. Effect sizes were calculated using Hedges’ g and synthesized with a random-effects model in Comprehensive Meta-Analysis Version 3. The pooled effect was large and statistically significant (g = 1.487, 95% CI [1.018, 1.955], p < .001). However, heterogeneity was very high (I² = 92.90%), and the prediction interval crossed zero (−0.616 to 3.589), indicating contextual variation. Subject cluster and geography did not significantly moderate the effect, suggesting that the observed benefits were not systematically confined to particular disciplines or regions. In contrast, implementation type was a significant moderator, with Integrated PBL producing a larger pooled estimate than Pure PBL. The Assisted PBL estimate was imprecise, non-significant, and based on only two studies, precluding claims of superiority. The funnel plot appeared approximately symmetrical, although visual inspection alone could not rule out publication bias. Overall, implementation quality, scaffolding, facilitation, and contextual alignment appear more decisive than subject area or geography in shaping PBL effectiveness.
Implementing an Ethnomathematics Approach to Enhance Mathematical Connection Skills of Junior High School Students Marbun, Trivena; Kamid, Kamid; Rohati, Rohati
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

Ethnomathematics offers a culturally responsive pathway for connecting formal mathematics with students’ lived experiences, yet empirical evidence regarding its effect on mathematical connection skills remains limited. This study examined the effectiveness of an ethnomathematics approach based on Jambi local culture in improving junior high school students’ mathematical connection skills in solid geometry. A quasi-experimental nonequivalent control group design was employed with 64 eighth-grade students from SMP Negeri 1 Muaro Jambi, divided equally into experimental and control groups. The experimental group received culturally contextualized instruction using Jambi cultural objects, whereas the control group followed conventional mathematics teaching. Data were collected through a mathematical connection skills test covering connections among mathematical concepts, between mathematics and other disciplines, and between mathematics and everyday life. Because the posttest data were not normally distributed, group differences were analyzed using the Mann–Whitney U test, followed by effect-size estimation. The experimental group achieved a higher mean posttest score than the control group, 66.88 compared with 33.75, and showed a more favorable distribution of achievement levels. The difference was statistically significant at p < .001, with a large effect size of r = .657. These findings indicate that integrating Jambi local culture into mathematics instruction can substantially strengthen students’ ability to construct, transfer, and apply mathematical relationships. The study positions ethnomathematics as an evidence-based instructional strategy that advances mathematical understanding while reinforcing cultural relevance in secondary mathematics education. 
Radec Model Based on Google Form Decision Tree on Mathematical Concept Understanding Harahap, Alya Rahmadani; Br Ginting, Siti Salamah
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’ limited mathematical concept understanding remains a persistent challenge in mathematics education, particularly when classroom practices emphasize procedural performance rather than active knowledge construction and conceptual reasoning. This study aimed to examine the improvement of students’ mathematical concept understanding through the integration of the RADEC (Read, Answer, Discuss, Explain, Create) learning model with an adaptive digital pathway using Google Form Decision-Tree. A quantitative approach with a quasi-experimental pretest-posttest control group design was employed involving Grade X students at SMAN 1 Percut Sei Tuan, Indonesia. The participants were divided into an experimental class that received RADEC-based instruction assisted by Google Form Decision-Tree and a control class that received conventional mathematics instruction. Data were collected using a mathematical concept understanding test and analyzed through descriptive statistics, normalized gain analysis, prerequisite testing, and independent sample t-test. The results showed that students in the experimental class achieved higher conceptual improvement, with an average N-Gain score of 0.7153 categorized as high, compared with 0.5138 in the control class categorized as moderate. The independent sample t-test revealed a significant difference between the two groups (t = 4.185; p < 0.001). These findings provide empirical evidence that integrating active learning models with adaptive digital support can strengthen students’ conceptual development by facilitating structured knowledge construction and differentiated learning experiences. This study contributes to mathematics education research by extending the RADEC framework beyond conventional active learning implementation toward an adaptive technology-enhanced learning environment.
Analysis of Student Learning Outcomes on Algebraic Operations Material Using Deep Learning Based on Mathematical Recreation Irawan, Hendra; Abdurrahman; Suripah; Sri Rezeki
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 implementation of Deep Learning-oriented instruction in mathematics education, empirical evidence remains limited regarding how its integration with mathematical recreation is reflected in students' achievement profiles and mathematical understanding in algebraic operations. This study aimed to examine students' achievement profiles following the implementation of a Deep Learning-based instructional module integrated with mathematical recreation. A descriptive quantitative design was employed involving 38 seventh-grade students at a junior secondary school. Data were collected through a post-instruction achievement test and analyses of students' written responses. The findings showed that students achieved a mean score of 81.00, with 81.6% meeting the Minimum Mastery Criterion, while achievement was predominantly concentrated in the medium-ability category. High-achieving students demonstrated stronger conceptual understanding and procedural fluency, whereas medium- and low-achieving students continued to experience difficulties in symbolic manipulation, identifying like terms, and applying operations involving negative signs. These findings indicate that the integration of Deep Learning principles with mathematical recreation was associated with generally satisfactory learning outcomes while revealing meaningful variation in students' mathematical understanding. Beyond reporting average achievement, this study demonstrates that achievement profiling combined with analyses of students' written responses provides richer pedagogical evidence for evaluating mathematics instruction and contributes to the growing literature on Deep Learning by offering a more comprehensive framework for understanding students' learning outcomes in algebra.

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