cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Resmawan
Contact Email
resmawan@ung.ac.id
Phone
+6285255230451
Journal Mail Official
euler@ung.ac.id
Editorial Address
Department of Mathematics, 3rd Floor Faculty of Mathematics and Natural Sciences, Universitas Negeri Gorontalo Jl. Prof. Dr. Ing. B. J. Habibie, Tilongkabila, Kabupaten Bone Bolango 96119, Gorontalo, Indonesia
Location
Kota gorontalo,
Gorontalo
INDONESIA
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 11 Documents
 1   2 
Search results for , issue "Volume 13 Issue 3 December 2025" : 11 Documents clear
Peningkatan Akurasi Model Untuk Prediksi KKM Siswa Sekolah Dasar Menggunakan Supervised Machine Learning dengan Integrasi Faktor Internal dan Eksternal Rahim, Arham; Mustakim, Mustakim
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.34577

Abstract

The Minimum Mastery Criteria (KKM) is a standard used to assess students’ competency achievement in elementary schools in Indonesia and serves as an important indicator of learning success. However, many students still have difficulties meeting this standard, thus requiring a data-driven early detection strategy to support timely intervention. This study aims to develop a prediction model for students’ KKM achievement based on internal and external factors using a supervised machine learning approach. Internal data include report card scores and attendance, while external data are obtained from student responses and parental information covering environmental, economic, motivational, and family support aspects. Four machine learning algorithms were evaluated, namely Naïve Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Neural Network, using a confusion matrix. Experiments were conducted under four data preprocessing scenarios: reverse scoring, feature selection, normalization, and variable grouping. The best result was obtained in Scenario S3, which combines normalization and feature selection, using the SVM algorithm with 100% accuracy. However, to avoid potential overfitting, a more stable algorithm is recommended, namely Naïve Bayes, which achieved 93% accuracy. These results indicate that the application of machine learning with appropriate preprocessing is effective for identifying students at risk of not achieving the KKM.
Modelling the Effect of Calendar Variation in the GSTARIMAX For Predicting Nitrogen Monoxide Air Quality Khaulasari, Hani; Akbar, Jeneiro Rezkyansyah Maulana
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.33830

Abstract

Nitrogen monoxide (NO) pollution has had a devastating impact on the environment and public health in Surabaya. This study aims to determine the best prediction model and forecast nitrogen monoxide concentrations in the April 2024 period. The method used is the GSTARIMAX model, which integrates the influence of calendar variation as well as spatial weight. Calendar factors such as school holidays, Christmas, New Year, and Eid al-Fitr are included as pseudo-exogenous variables (dummy). Data was obtained from three air quality monitoring points in Surabaya, namely SPKU Wonorejo, Kebonsari, and Tandes, throughout January 2023 to March 2024. Parameter estimation in the GSTARIMAX model used the Generalized Least Squares (GLS) and Ordinary Least Squares (OLS) approaches. This study also compares three types of spatial weights and compares the performance of the GSTARIMAX model with other models that consider or ignore calendar variations. The results of the analysis show that significant parameters are derived from the AR(1) model, so that the GSTARIX-SUR(1) model with first-order spatial lag and cross-normalized correlation weight provides the best performance, indicated by the sMAPE value below 10% and the lowest RMSE value. In addition, this model also meets the assumptions of white noise and normal distribution. Fluctuations in nitrogen monoxide concentrations during April 2024 show fairly high volatility, with a significant spike occurring on April 12–14, 2024. The increase is correlated with the return flow of people from outside the city to Surabaya after the Eid al-Fitr holiday.
Hedging Strategy Analysis of GOTO Stock Using Collar, Bear Put Spread, and Long Strangle Agustiani, Nur; Wahyu, Sri; Firdawanti, Aulia Rizki; Ahmad, Hafidlotul Fatimah
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.34092

Abstract

This study compares the performance of three hedging strategies, Collar, Bear Put Spread, and Long Strangle, in a case study of PT GoTo Gojek Tokopedia Tbk (GOTO) stock. The analysis focuses on the risk management effectiveness and profit potential of these strategies within an emerging market context. The research utilizes weekly stock price data from July 2023 to June 2024 (54 observations). The methodological procedures include calculating returns and volatility, testing return normality using the Shapiro-Wilk test, determining European option prices using the Black-Scholes model with a 6% risk-free interest rate, and conducting profit simulations. The findings indicate that the Collar strategy provides maximum protection against stock price declines, albeit with limited profit potential. The Bear Put Spread strategy proves effective in generating returns during moderate price decreases while offering lower risk and cost. Conversely, the Long Strangle strategy possesses high profit potential during significant price volatility but carries the risk of total loss if stock prices remain stagnant. As a comprehensive comparison of these three option strategies applied to GOTO stock, this study recommends the Collar strategy as the optimal choice for risk-averse investors during bearish trends.
Identifying Digital Literacy Profiles in Distance Education: A K-Prototypes Clustering Approach Zili, Arman Haqqi Anna; Martinasari, Made Diyah Putri; Kharis, Selly Anastassia Amellia
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.34568

Abstract

Education quality is one of the main focuses of Indonesia’s Sustainable Development Goals (SDGs), particularly in the goal that emphasizes equitable access and lifelong learning. Universitas Terbuka (UT) is a higher education institution that implements an open and distance learning system. This setting creates a diverse student body in terms of age, occupation, and digital literacy levels. Segmenting students based on their digital literacy is both essential and challenging, as it involves combining demographic data with daily digital behavior. This study aims to identify the digital literacy profiles of UT students using cluster analysis with the K-Prototypes algorithm. Data were obtained from a survey of 10,396 students with 42 variables. The Elbow Method analysis revealed three distinct clusters, each reflecting unique engagement profiles. The first cluster, the Engaged Evening Digital User, is active during the evening and balances work with social activities. The second cluster, the Hyper Connected Communicator, relies heavily on messaging applications for social interaction. The third cluster, the Balanced Digital Citizen, shows a more even distribution of digital use across academic, entertainment, and communication activities. These clusters predominantly comprise Generation Z individuals, many of whom are actively engaged in the private sector. The profound implications of these findings lie in their capacity to forge highly targeted strategies for digital learning, communication, and student support, thereby enhancing educational outcomes. Furthermore, this research significantly advances methodological literature by demonstrating a powerful, integrated approach to clustering mixed-type attributes, offering a more nuanced understanding of learner profiles in distance education.
Evaluating Kernel Weighting Functions in Geographically Weighted Logistic Regression for Spatial Modelling of Stunting in East Lombok Hastuti, Siti Hariati; Chintyana, Alissa; Sastriana, Hanipar Mahyulis
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.33951

Abstract

Stunting remains a major public health concern in Indonesia, with East Lombok Regency recording the highest prevalence in West Nusa Tenggara Province in 2022. This study aims to identify factors influencing stunting while accounting for spatial heterogeneity across regions. The Geographically Weighted Logistic Regression (GWLR) method was applied, comparing three kernel weighting functions: Fixed Gaussian, Adaptive Gaussian, and Adaptive Bisquare, to determine the best-fitting model. Parameter estimation was conducted using Maximum Likelihood Estimation with the Newton–Raphson iterative procedure. The results show that the Adaptive Gaussian kernel provided the best model performance, indicated by the lowest Corrected Akaike Information Criterion (AICc) value of 28.346. Spatial mapping identified two regional clusters: one where vitamin A supplementation significantly affected stunting, and another where no explanatory variables were significant. These findings emphasize the importance of incorporating spatial effects in public health modeling to support more targeted and context-specific interventions for stunting reduction at the local level.
A Study on Prediction Intervals Produced Using Quantile Regression Forest With and Without Variable Selection Megawati, Megawati; Sartono, Bagus; Oktarina, Sachnaz Desta
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.34392

Abstract

Quantile Regression Forest (QRF) is a method that utilizes the random forest algorithm to estimate the conditional distribution of response variables and form quantile prediction intervals. However, when there is a high correlation between covariates, QRF performance may decrease due to the multicollinearity effect, thereby reducing the accuracy of the prediction interval for the target variable. In linear models, multicollinearity must be addressed because it can cause large variances. This study contributes to enhancing the reliability of prediction intervals in correlated data through the integration of adaptive-LASSO with QRF. Specifically, it examines the role of variable selection by the adaptive LASSO method on the performance of the QRF prediction interval in the simulated data, and the best model obtained in the study is then applied to predict the interval in the productivity data of oil palm fresh fruit bunches. The results of the study show that variable selection is proven to produce coverage close to the target prediction interval. In addition, the QRF model with variable selection applied to the productivity data of oil palm fresh fruit bunches produces a good prediction interval.
Enhancing the Reliability of the Flood Early Warning System in Samarinda City Through the Hybrid SARIMA-RBFNN Model Rahmah, Syifa Mutia; Dani, Andrea Tri Rian; Wahyuningsih, Sri
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.34268

Abstract

Rainfall data in Samarinda City exhibit seasonal patterns that play a crucial role in increasing flood risk during certain periods. To enhance the effectiveness of the early warning system, this study developed a hybrid SARIMA-RBFNN model. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to capture linear seasonal patterns, while the Radial Basis Function Neural Network (RBFNN) was employed to model nonlinear residuals from SARIMA. Model performance was assessed using the Symmetric Mean Absolute Percentage Error (SMAPE) and Root Mean Squared Error Prediction (RMSEP). Compared to the single SARIMA model (SMAPE = 34.699%, RMSEP = 82.255), the hybrid SARIMA–RBFNN achieved lower in-sample errors (SMAPE = 34.175%, RMSEP = 78.577) and demonstrated more stable performance for out-of-sample data. This indicates that the hybrid model provides a more balanced and reliable prediction by capturing nonlinear rainfall fluctuations that SARIMA alone could not model effectively. Forecasts for 2024 revealed a consistent seasonal trend, peaking mid-year. These findings indicate that the hybrid model can improve the reliability of the flood early warning system in Samarinda by providing more accurate rainfall predictions.
Mathematical Model of COVID-19 with the Influence of Vaccination Purnami, Ndaru Atmi; Prawadika, Luqman Nuradi; Pasangka, Irvandi Gorby; Findasari, Findasari; Kusumawati, Eka; Pratama, Ilham Yoga; Suharis, Ridho; Maturbongs, Nadhira Hasna
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The COVID-19 pandemic, which first emerged at the end of 2019, has had a significant impact on people's lives around the world. In Indonesia, the outbreak began to develop in February 2020. Although the pandemic has now passed and people have started to resume their normal activities, some individuals are still being infected with COVID-19, even though the number of cases is now under control. One of the key factors in controlling COVID-19 is vaccination. The extent to which vaccination affects COVID-19 transmission will be discussed in this study. Furthermore, a numerical simulation will be conducted on this mathematical model to observe the impact of vaccination on COVID-19. The mathematical model of COVID-19 with vaccination influence will describe the interaction between six population classes, namely: the class of susceptible individuals who can be infected (Susceptible – S), the class of exposed individuals (Exposed – E), the class of vaccinated individuals who have never been infected (Vaccinated – V), the class of infected individuals (Infected – I), the class of individuals who have recovered (Recovered – R), and the class of infected individuals who have died (Death – D). It is important to note that COVID-19 is a disease caused by infection with the coronavirus. A person who has not yet been infected with the virus has the potential to be exposed. One way to prevent exposure is through vaccination. In Indonesia, vaccination has been made mandatory three times: the first dose, the second dose, and the booster. However, because the coronavirus has an incubation period, there is no guarantee that a vaccinated person has not already been exposed to the virus. Exposed individuals will become infected with COVID-19 once the incubation period ends. Infected individuals may show symptoms or be asymptomatic. An infected individual has two possible outcomes: recovery or death. The modeling is based on the SEVIRD model, with its parameters estimated using data. This study produces a mathematical model of COVID-19 with vaccination influence, showing that vaccination plays a role in controlling the spread of COVID-19.
Estimasi Aggregate Loss Menggunakan Pendekatan Bayesian Metode MCMC Algoritma Gibbs-Sampling dengan Software OpenBUGS Nurdanita, Melati Sinta; Azizah, Azizah
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.33769

Abstract

This study aims to estimate aggregate loss (total loss) in private passenger car insurance data using the Bayesian approach of the Markov Chain Monte Carlo (MCMC) algorithm Gibbs-Sampling with the help of OpenBUGS software. The approach was carried out by modeling claim frequency data using Geometric and Negative Binomial distributions, and claim severity using Gamma and Lognormal distributions. Next, the prior for each model was determined, along with calculations for the likelihood function, joint distribution, marginal distribution, and posterior distribution. Since the resulting posterior distribution could not be calculated analytically, simulation was performed using OpenBUGS software to calculate it. Simulation was also used in predictive posterior calculations to estimate future aggregate losses. The results show that the Bayesian approach with the Markov Chain Monte Carlo method using the Gibbs-Sampling algorithm and its implementation through OpenBUGS software can be used to estimate aggregate loss. From the simulations used, it was found that the estimation of aggregate loss for private passenger car insurance is influenced by the selection of the frequency and severity of claims models. The Negative-Gamma Binomial model produced the highest posterior predictive estimate of aggregate loss at $75270.0, while the Geometric-Lognormal model provided the lowest estimate at $70500.0. Meanwhile, the model with the smallest standard deviation is the Negative Binomial-Lognormal model, which is $62720.0. This study contributes to insurance risk modeling, particularly in determining reserve funds and setting insurance premiums tailored to the target market of insurance companies.
Penentuan Spektrum pada Variasi Graf Barbel Putri, Neli Septiana; Rosyida, Isnaini
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.33968

Abstract

This study aims to analyze the determination of the spectrum of barbell graph variations, where the variations are made by modifying the number of nodes on the bridge between complete graphs in a barbell structure. The spectrum contains the eigenvalues of the adjacency matrix of the barbell graph variations along with their multiplicities. The analysis is conducted manually using linear algebra approaches such as cofactor expansion, characteristic polynomial factorization, the rational root theorem, and Horner’s scheme. The results are then validated using Python programming. The findings of this study show that the longer and more complex the bridge connecting the two complete graphs, the greater the diversity of eigenvalues produced. The spectrum of the barbell graph B(n,1)B(n, 1)B(n,1) consists of the eigenvalues λ1,n−1,λ2,−1,λ3\lambda_1, n - 1, \lambda_2, -1, \lambda_3λ1,n−1,λ2,−1,λ3 with multiplicities 1,1,1,2n−3,11, 1, 1, 2n - 3, 11,1,1,2n−3,1. Furthermore, the spectrum of the barbell graph B(n,2)B(n, 2)B(n,2) consists of the eigenvalues λ1,λ2,λ3,λ4,−1,λ5,λ6\lambda_1, \lambda_2, \lambda_3, \lambda_4, -1, \lambda_5, \lambda_6λ1,λ2,λ3,λ4,−1,λ5,λ6 with multiplicities 1,1,1,1,2n−4,11, 1, 1, 1, 2n - 4, 11,1,1,1,2n−4,1, respectively. This research provides theoretical contributions regarding the relationship between complex graph structures and their spectral representations.

Page 1 of 2 | Total Record : 11

 1   2 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Volume 13 Issue 3 December 2025 Volume 13 Issue 2 August 2025 Volume 13 Issue 1 April 2025 Volume 12 Issue 2 December 2024 EULER: Volume 12 Issue 1 June 2024 EULER: Volume 11 Issue 2 December 2023 EULER: Volume 11 Issue 1 June 2023 EULER: Volume 10 Issue 2 December 2022 EULER: Volume 10 Issue 1 June 2022 EULER: Volume 9 Issue 2 December 2021 EULER: Volume 9 Issue 1 June 2021 EULER: Volume 8 Issue 2 December 2020 EULER: Volume 8 Issue 1 June 2020 EULER: Volume 7 Issue 2 December 2019 EULER: Volume 7 Issue 1 June 2019 More Issue