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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 188 Documents
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Peramalan Harga Emas Berjangka Menggunakan Metode ARIMA-GARCH Hasanah, Mauizatun; Putri, Mega Ramatika; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 2 August 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i2.32723

Abstract

Gold futures price forecasting plays an important role in investment decision-making and risk management, especially in the midst of volatile commodity market dynamics. This research aims to build an accurate gold futures price forecasting model by combining Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The ARIMA model is used to capture linear patterns and historical trends in time series data, while the GARCH model is able to handle the high volatility characteristic of gold price movements, something that conventional forecasting models often fail to capture. The data used in this study is daily gold futures price data collected over the period January 3, 2023 to March 31, 2025, which covers both normal market conditions and periods of turmoil, making it relevant to describe the overall market dynamics. The forecasting results show that the ARIMA-GARCH model with components (3,1,3) (1,1) with a MAPE of 4.52% indicates a good level of accuracy in the context of forecasting gold futures prices that have high volatility. Thus, this model provides precise forecasting results with actual data so that it can be used by market participants and policy makers in managing risks and designing strategies.
Perbandingan Kriteria Kataoka Safety First dan Mean Varians dalam Pembentukan Portofolio Saham Optimal Siswanah, Emy; Abdurakhman, Abdurakhman; Maruddani, Di Asih I
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 2 August 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i2.32846

Abstract

The Markowitz Mean-Variance Portfolio and the Kataoka Safety-First criterion share similarities, as both serve as risk-control methods and suitable for risk-averse investors. This study compares these two approaches in constructing an optimal portfolio and evaluates their respective performances. The findings indicate that the portfolio weights derived from both methods are positive. Empirical evidence suggests that the expected return of the Kataoka Safety-First portfolio is consistently higher than that of the Mean-Variance method. However, this greater return is accompanied by a higher level of risk. Furthermore, the Sharpe and Treynor indices for the Kataoka Safety-First portfolio surpass those of the Mean-Variance method across both portfolio variations analyzed. These results confirm that the Kataoka Safety-First portfolio demonstrates superior performance compared to the Mean-Variance approach. Therefore, the Kataoka Safety-First criterion presents itself as a viable strategy for constructing an optimal portfolio tailored to risk-averse investors.
Implementasi Metode Bayesian untuk Menghitung Premi Produk Asuransi Kendaran Bermotor dengan Pendekatan Monte Carlo Markov Chain Situmorang, Boy Nathanael; A’la, Kevina Alal; Arvianti, Aurellia; Yusuf, Feby Indriana; Handamari, Endang Wahyu
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 2 August 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i2.32930

Abstract

Accurate premium determination is a fundamental aspect of risk management in motor vehicle insurance. This study implements the Bayesian method using a Markov Chain Monte Carlo (MCMC) approach to calculate the net premium. The aggregate claim model is constructed from a claim frequency distribution (Poisson) and a claim severity distribution (Generalized Extreme Value (GEV)), with the GEV distribution specifically chosen to model extreme claim risk. The analysis utilizes generated data for the period 2018–2024, with parameters derived from the historical data of PT Asuransi Jasa Indonesia Purwokerto (2013–2017). Parameter estimation, performed via OpenBUGS software, was validated to have achieved good convergence (MC-error   ). Based on the estimated parameters, a premium of IDR 397.502.000 was obtained, calculated using the net premium principle from the expected value of aggregate claims. These results demonstrate that the Bayesian MCMC approach is effective for producing a robust premium estimation, contributing a pricing framework that explicitly accounts for extreme value claims.
Implementasi Metode Goal Programming Untuk Optimasi Produksi Cokelat Pada UMKM Pradjaningsih, Agustina; Andora, Ela; Santoso, Kiswara Agung
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.26904

Abstract

Chocolate is a food made from cocoa beans, namely Theobroma Cacao. Cocoa beans harvested are then processed to prevent rotting, which can reduce their quality. Currently, many chocolate manufacturers produce various variants of chocolate products. Each production company tries to achieve maximum profits with minimal costs. Production optimization problems can be addressed using objective programming, which is a method used to develop mathematical models of optimization problems involving multiple objectives or constraints. In goal programming, each goal is expressed as a goal constraint. Objective programming methods involve determining decision variables, objective constraints, and objective functions. Optimization problems are solved using the objective programming method with the help of Lingo software. Optimization calculations using Lingo software show that the production of each chocolate product has reached optimality. Production after optimization reached Rp. 10,380,000 per month, whereas production costs were only Rp. 10,500,000 per month before optimization. The availability of raw materials needed after optimization reached 85 recipes per month, whereas it was 90 recipes per month before optimization. The profit obtained is also optimal, namely Rp. 4,267,000 in one month.
Implementasi Artificial Neural Network (ANN) dalam Memprediksi Nilai Tukar Rupiah terhadap Dolar Amerika Sakti, Adam Indra; Saputra, Lianda; Suhendra, Helen; Halim, Nikken; Alviari, Irfaliani; Ilham, Muhammad Rozan Nur; Putri, Marwah Hotimah Nada; Dalimunthe, Desy Yuliana
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.26654

Abstract

The exchange rate of one country's currency against other countries takes an important role in the development and economic activities for a nation. This condition of the Indonesian currency exchange rate, namely the rupiah, is now continuously increasing, meaning that the exchange rate is weakening and experiencing depreciation. Apart from that, the rupiah exchange rate also experiences fluctuations, so forecasting is needed to find solutions to problems that will arise if the currency exchange rate increases. This research purpose is to find the best of network archictecture and to predict the selling rate of the rupiah (Rp) per 1USD for one year. The forecasting method used in this research is using an Artificial Neural Network (ANN) with Backpropagation algorithm. This method is suitable for use in time series analysis because the algorithm is able to adjust the data and has a relatively small error. The data used is the rupiah exchange rate against the USD in the form of time series data, which from March 1, 2019 to February 28, 2024. The data scenario of 90% training and 10% testing at the training stage obtained the best architecture 4-20-1 with MSE is 0.0010385. The data scenario is 80% training and 20% testing where in the training the best architecture is 4-25-1 with an MSE of 0.00089412. The data scenario is 70% training 30% testing where in the training the best architecture is 4-25-1 with an MSE of 0.00099221. Thus, the prediction prices used are predictions for the 80% training data scenario and 20% testing data, because the accuracy results (MSE) are better than the other two scenarios.
Support Vector Machine-Radial Basis Function Kernel and K-Nearest Neighbor Differences for Classification Superior Varieties of Rice in Indonesia Chintyana, Alissa; Kertanah, Kertanah; Hastuti, Siti Hariati; Khotimah, Husnul
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.27605

Abstract

Rice is the primary food source for the Indonesian population, making it a priority commodity in Indonesia. Rice production plays a significant role in Indonesia's economic development, with high-yield rice varieties being crucial for enhancing national rice output. Ensuring food security requires the selection of superior rice varieties with optimal quality. This study evaluates various high-yield rice varieties, including INPARA, INPARI, INPAGO, and HIPA, based on characteristic data collected in 2023. Machine learning algorithms, increasingly central to data analysis, were applied, leveraging labeled data suitable for supervised learning methods. During the pre-processing stage, it was determined that the data did not meet the linearity assumption. Thus the Support Vector Machine (SVM) algorithm was modified with the Radial Basis Function (RBF) kernel to better handle non-linear data. Additionally, the K-Nearest Neighbor (KNN) algorithm, a traditional method, was used for comparison. The results indicate that SVM with the RBF kernel achieved faster processing times and the accuracy value reaches 96%, nearly 10% higher than the KNN algorithm.
Model Geographically Weighted Regression Menggunakan Adaptive Gaussian Kernel untuk Pemetaan Faktor Penyebab Stunting Vianti, Febi; Khaulasari, Hani; Farida, Yuniar; Swantika, Cicik; Efendi, Havid
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.28072

Abstract

Stunting is a child growth disorder that is evident from a lack of height for age. Jember Regency has a stunting prevalence rate of 34.90% in 2022, making it the region with the highest stunting cases in East Java. The purpose of this research is to map the factors that influence stunting in Jember Regency with a spatial analysis approach. The method applied in this study is Geographically Weighted Regression (GWR) to analyze the spatial relationship between predictors and responses. GWR uses an optimal kernel to determine the spatial weights based on distance accurately, as well as the AIC and  goodness criteria to calculate the goodness of the model. The research variables include the number of stunting cases in Jember Regency as the response variable (Y), and the predictor variables (X) are chronic energy deficiency pregnant women (), anemic pregnant women (), exclusive breastfeeding (), proper sanitation (), pregnant women consuming TTD at least 90 days (), complete basic immunization (), and wasting (). The results of the study using the adaptive gaussian kernel with the minimum CV compared to other kernels can improve accuracy, so it can be applied to data analysis.  The GWR model obtained an accuracy of 80.59% and AIC 360.  indicates the ability to explain 80.59% of the variability of the response data, and the AIC value is 360, which reflects the efficiency and suitability of the model to spatial data. From the GWR parameters, 14 groups were formed where there are several different factors in each area in the sub-districts in Jember Regency.
Pemodelan Statistik Total Klaim BPJS Kesehatan Berbasis Distribusi Pareto dan Weibull: Pendekatan Non-Homogeneous Poisson Process Fauziah, Irma; Mahmudi, Mahmudi; Safitri, Nur Izzati
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 2 August 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i2.33562

Abstract

BPJS Kesehatan must be prepared with adequate financial reserves to pay participant claims, which requires careful financial analysis and management. One aspect of this analysis is estimating claim inter-arrival times and claim amounts using data patterns from various hospital types (A, B, C, and D). Given the time-varying intensity of claims, the Non-Homogeneous Poisson Process is the suitable method for this study. The best distribution models were selected based on the smallest Kolmogorov-Smirnov value. The findings indicate the best model for inter-arrival time data is a Pareto distribution, with different parameters for each hospital type. For claim amounts, the analysis shows claims from type A and D hospitals follow a three-parameter Weibull distribution, while claims from type B and C hospitals follow a two-parameter Weibull. Based on these results, BPJS Kesehatan needs to prepare average monthly reserve funds of IDR 10–11 trillion, with extreme scenarios requiring up to IDR 11–12 trillion per month.
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.

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