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Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Published by Universitas Negeri Gorontalo
ISSN : 20879393     EISSN : 27763706     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi is a national journal intended as a communication forum for mathematicians and other scientists from many practitioners who use mathematics in the research. Euler disseminates new research results in all areas of mathematics and their applications. Besides research articles, the journal also receives survey papers that stimulate research in mathematics and its applications. The scope of the articles published in this journal deal with a broad range of mathematics topics, including: Mathematics Applied Mathematics Statistics and Probability Applied Statistics Mathematics Education Mathematics Learning Computational Mathematics Science and Technology
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "Volume 14 Issue 1 April 2026" : 6 Documents clear
Spatial Planning of Mosque-Based Ablution Water Reuse Networks in Lombok Barat Using K-Means Clustering and Minimum Spanning Tree Sutanto Sutanto; Retno Tri Vulandari; Tyas Titah Nareswari
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.37246

Abstract

Lombok Barat Regency, located on Lombok Island, frequently experiences water scarcity due to its semi-arid climate and prolonged dry seasons. Agricultural and plantation activities in this region rely heavily on limited freshwater resources, particularly in dryland areas. Meanwhile, mosques continuously generate relatively clean greywater from daily ablution (wudu) activities. Despite its regular availability and relatively low contamination level, this resource remains largely underutilized. This study examines the spatial planning of mosque-based wudu water collection networks in West Lombok as a potential supplementary water source for plantation and dryland irrigation. A spatial analytical framework combining K-Means clustering and the Minimum Spanning Tree (MST) algorithm was applied and implemented through an interactive RShiny application. Spatial data from 940 mosques were preprocessed and analyzed. K-Means clustering at Level 1 grouped mosques into 25 local service clusters, while Level 2 clustering aggregated these clusters into five main reservoir zones. A cost-weighted MST based on Haversine distance was then used to estimate the minimum pipeline length required to connect mosques within the proposed network configuration. The results show that the modeled network connects all 940 mosques with a minimum total pipeline length of 411,757.28 meters and could potentially collect approximately 282,000 liters of reusable wudu water per day. However, the model represents a preliminary spatial planning framework and does not include hydraulic simulations, water quality validation, treatment system design, or operational feasibility assessment.
Fostering Inclusive Mathematics Learning: A Lesson Study Approach Integrating the Wordwall Digital Platform for Special Needs Students Nia Wahyu Damayanti; Ali Carli
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.37499

Abstract

This study investigated the integration of the Wordwall digital interactive learning platform into a Lesson Study framework to enhance mathematics instruction for students with special needs. Conducted at a Special Education School (SLB), the research explored digital tools and collaborative teacher development improved student engagement, conceptual understanding, and motivation in learning basic mathematical concepts. A group of mathematics teachers participated in  lesson planning, classroom implementation, observation, and reflection, during which Wordwall was used to design interactive and accessible math activities tailored to students with diverse disabilities. Despite growing interest in inclusive education, few studies had examined how structured professional development models such as Lesson Study could be effectively integrated with digital platforms like Wordwall in special education contexts. This study addressed that gap by demonstrating that the strategic integration of these tools significantly enhanced students’ active participation and conceptual comprehension. The interactive features of Wordwall supported multisensory engagement and differentiated instruction, while the Lesson Study process facilitated sustained collaboration and adaptive teaching among educators. The findings suggest that integrating technology-enhanced learning with professional learning communities not only improved students’ outcomes, such as accuracy in basic computations, mathematical reasoning, and enthusiasm but also strengthened teachers' capacities in inclusive instructional design, reflective practice, and adaptive pedagogical strategies. These results showed insights for future research and practice, especially in developing inclusive models that digital technology and collaborative teaching to promote equity and quality in special education.
Optimasi Penentuan Basis Risiko pada Jaringan Saham Keuangan Menggunakan Dimensi Metrik untuk Estimasi Value at Risk Annisa Hevita Gustina Kumalasari Saefulloh; Putri Isnaini Cahyaning Baiti; Erica Grace Simanjuntak; Rahmatika Zaqiatul Latifah
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.37004

Abstract

This study aims to optimize systemic risk monitoring in the Indonesian financial sector network by determining the minimum risk basis using the Metric Dimension concept. The high complexity of inter-asset correlations requires a dimension reduction method that maintains structural information regarding risk exposure. Daily stock price data from 10 financial issuers (banking, insurance, and financing) for the five-year period from January 1, 2021, to December 31, 2025, were used. The data were transformed into a weighted graph through a log-return correlation matrix converted into metric distances. The resolving set (W) was determined using a greedy algorithm to identify the optimal basis. Validation was performed by analyzing the correlation between the metric coordinates of each issuer and its 95% Value at Risk (VaR). The results showed that the financial network has a metric dimension of dim(G) = 1, with ADMF.JK selected as the optimal resolving set (basis). Actuarial validation revealed a significant negative correlation (−0.5495) between the metric distance and VaR. This implies that the metric distance from the basis can linearly map the magnitude of market risk, offering an efficient strategy for investment managers to monitor portfolio stability through a single reference entity.
Evaluasi Perbandingan Model XGBoost, Random Forest, LightGBM, dan Artificial Neural Network dalam Klasifikasi Kerawanan Pangan Mardatunnisa Isnaini; Dela Gustiara; Rizqi Annafi Muhadi; Shalshabilla Shafa; Bagus Sartono; Aulia Rizki Firdawanti; Budi Susetyo; Gerry Alfa Dito
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.36227

Abstract

Food insecurity remains a serious household-level issue, particularly in densely populated regions such as West Java, highlighting the need for analytical approaches capable of accurately identifying vulnerable groups. Machine learning algorithms offer the potential to improve the accuracy and precision of food insecurity classification based on survey data. This study aims to compare the predictive performance and variable importance identification of four machine learning algorithms—Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)—in predicting household food insecurity status. The analysis employs SUSENAS 2023 data covering 26,012 households with 14 predictor variables, and food insecurity is classified using the Food Insecurity Experience Scale (FIES). Class imbalance is addressed using the Synthetic Minority Over-sampling Technique (SMOTE) within a 10-fold cross-validation framework. The results show that XGBoost achieves the highest accuracy of 71%, while Random Forest provides the best balanced accuracy under the SMOTE scenario. Moreover, all algorithms consistently identify the Wealth Index as the most influential predictor based on their respective Variable Importance measures, followed by variables related to water access and food assistance. Accordingly, XGBoost is recommended in terms of accuracy, whereas Random Forest demonstrates superior balanced accuracy and prediction stability.
Modeling the Health Service Queuing System Using Petri Net and Max-Plus Algebra at Integrated Health Service Post (Posyandu) Syarif Abdullah; Himmatul Mursyidah; Ferdian Bangkit Wijaya; Miftahul Huda; Sri Istiyarti Uswatun Chasanah; Nadia Eka Nursafitri; Dinda Dwi Anugrah Pertiwi
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.35889

Abstract

This study models the health service queue system at the Integrated Health Service Post (Posyandu) in Cilegon City, Banten, using the Petri Net and Max-Plus Algebra approaches to analyze the flow of participant arrivals and completion times. The data used are observational data from Posyandu activities simulated through a discrete event model, which includes several types of participants, namely: babies not standing yet, babies standing, pregnant mothers, and family planning programs. Petri Net modeling is used to represent the relationship between service transitions, while Max-Plus Algebra is used to calculate the process cycle time based on the critical path. The results of the study showed that the categories of non-standing babies, standing babies, and pregnant women/participants in the family planning program had identical service time patterns, namely a total duration of 17 minutes 19 seconds, with the main stages including measurement, midwife intervention, and provision of additional food. Max-Plus analysis confirms that the measurement and midwife intervention stages are the critical path that determines the length of service time. This study concludes that the combination of Petri Net and Max-Plus Algebra is effective in describing the dynamics of Posyandu queues and is able to provide quantitative information needed to identify bottleneck points and the basis for improving the service flow.
Analisis Komparatif Random Forest dan Support Vector Machine untuk Klasifikasi Tingkat Keparahan Serangan Siber Reyhanssan Islamey; Sri Winiarti; Imam Riadi
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.36558

Abstract

The escalating volume and sophistication of cyberattacks on network infrastructures processing massive daily traffic have overwhelmed security teams in prioritizing incident responses rapidly and accurately, a phenomenon known as alert fatigue. This study aims to analyze and compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms for classifying cyberattack severity levels (Low, Medium, and High). The study uses the public Cyber Security Attacks dataset, consisting of 40,000 network traffic records reduced to 20,000 clean entries through preprocessing and feature engineering. The methodology includes data cleaning, selecting 10 significant features using SelectKBest, standardizing numerical features, and evaluating models across three data split scenarios (70:30, 80:20, and 90:10) using a stratified splitting approach. Experimental results show that SVM consistently outperforms RF across all scenarios, with the best performance in the 80:20 split, achieving 98.92% accuracy and a weighted average F1-Score of 0.99 using hyperparameter configurations of C = 100 and gamma = 0.01. The superiority of SVM lies in its ability to model non-linear relationships and complex feature interactions in data with overlapping class boundaries. In contrast, RF exhibits an over-prediction bias toward the minority class (’Low’) due to the class_weight=’balanced’ mechanism and limitations of axis-based separation. These findings confirm that SVM with a Radial Basis Function (RBF) kernel is more suitable for cyberattack severity classification, particularly in automated incident detection systems requiring balanced precision and recall as well as reliable decision-making.

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