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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 16 Documents
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Search results for , issue "EULER: Volume 12 Issue 1 June 2024" : 16 Documents clear
Implementasi Metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam Prediksi Harga Saham X Damayanti, Adelia; Agustina, Dwi
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 12 Issue 1 June 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i1.25278

Abstract

Today, the stock market is one of the most important financial vehicles. Investors were concerned about X shares because it fluctuated significantly during Elon Musk's acquisition process. This study was aimed to predict the future price trend of X stocks. Thus, this analysis can assist investors in controlling X stocks. Data for this study were gathered from the Kaggle website. This study uses data from January 2016 to October 2022. The Adaptive Neuro Fuzzy Inference System (ANFIS) will be used to estimate the price of X stocks. The results demonstrated that the ANFIS approach accurately captured the pattern of stock price changes. Based on the accuracy test results, this method has an RMSE of 0.005. It demonstrates that the ANFIS method can accurately anticipate the price of X stock.
Meir Keeler's Fixed-Point Theorem in Complex-Valued Modular Metric Space Kiftiah, Mariatul; Yudhi, Yudhi
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 12 Issue 1 June 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i1.25126

Abstract

In this paper, we introduce the notion of Meir-Keeler contraction mapping, which is defined in complex-valued modular metric space. Some properties of sequences in this space, which are convergence, Cauchyness and completeness, are used to prove the fixed-point theorem under this mapping. Additionally, the Delta_2-type condition is also defined as the sufficient condition in order to have a unique fixed point.
Meningkatkan Kemampuan Komunikasi Matematika dengan Menggunakan Pendekatan Saintifik Pada Materi Kubus dan Balok Mahmud, Irfan; Usman, Kartin; Takaendengan, Bertu
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 12 Issue 1 June 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i1.26346

Abstract

This research aims to improve students' written mathematical communication skills through a scientific approach. Implementation of actions is carried out in two cycles. The subjects of this research were 22 students in class VIII of SMP NEGERI 6 SATAP Telaga Biru, consisting of 10 men and 12 women. The techniques used in this research were observation and tests. The data analysis techniques in this research are teacher observation, student observation, and mathematical communication tests. Research procedures include planning, implementation, observation, and reflection. The research consisted of 2 cycles. The results of the research show that there has been an increase in mathematical communication through scientific learning. This is shown by the research results that the scientific learning process has been running in accordance with the learning implementation plan and has succeeded in creating a conducive learning situation and improving mathematical communication. The results of the research showed that mathematical communication skills in cycle I were 55% poor criteria and in cycle II were 82% good criteria. The results of observations of teachers' teaching skills when managing learning has increased, in cycle I it reached 79.17\% of good criteria, while in cycle II it reached 90.97% of very good criteria. The results of observations of student learning activities have increased, from cycle I the percentage was 70.63 good criteria, whereas in cycle II it was 82.5% very good criteria.
Pengaruh Pengembangan Model Discovery Learning terhadap Kemampuan Koneksi Matematika Mahasiswa dalam Pembelajaran Online Matematika Ekonomi Listyotami, Mega Kusuma; Ferita, Rolina Amriyanti
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 12 Issue 1 June 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i1.25113

Abstract

The research aims to determine the influence of developing a discovery learning model on students' mathematical connection abilities in online mathematics and economics learning. This research was conducted at the Dwi Sakti Baturaja College of Economics. The research method used in this research is pre-experiment with a One Group Pretest-Posttest Design research design. The sample for this research consisted of 30 students. Data collection techniques use test techniques which are divided into pretest and posttest. The data processing technique uses t-test calculations with the SPSS 20 program. The research results show the influence of developing a discovery learning model on increasing students' mathematical connection abilities in online mathematics and economics courses at the Dwi Sakti Baturaja College of Economics. The average initial test score was 48.50, increasing from 37.00 to 85.50 in the final test. The test results show that the t count is 17.796 and the t-table (df 30 = 2.048). The t-count value (17.796)t-table (2.048). The results show that Ho is rejected and Ha is accepted. Thus, it can be said that the development of the discovery learning model influences students' mathematical connection abilities in online mathematics and economics learning. The results of the analysis show that the R square value of the correlation analysis between the development of the discovery learning model on students' mathematical connection abilities in online learning for mathematics economics courses is 71.50\%, which means that the contribution of developing the discovery learning model has a positive influence on students' mathematical connection abilities in online learning mathematics economy.
Perbandingan Metode Monte Carlo Antithetic Variate dan Control Variate dalam Penentuan Harga Opsi Barrier Knock-Out Murwaningtyas, Chatarina Enny; Haryono, William Saputra; Uge, Maria Andriani; Kristofel, Tedi
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 12 Issue 1 June 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i1.25128

Abstract

This study aims to examine the effectiveness of the Monte Carlo antithetic variate and control variate methods in pricing knock-out barrier options compared to the standard Monte Carlo method. The main problem in barrier option pricing is the high variance of estimates, which can reduce the accuracy and efficiency of results. The standard Monte Carlo method often requires a very large number of simulations to achieve stable results, which is computationally inefficient. To address this issue, this study employs variance reduction techniques, antithetic variate, and control variate. The findings indicate that both methods offer higher accuracy in price estimation compared to the standard Monte Carlo method. Further analysis reveals that the control variate method is more effective for pricing up and out barrier call options and down and out barrier call options, while the antithetic variate method excels in pricing up and out barrier put options and down and out barrier put options. This study underscores the importance of selecting the appropriate method according to the type of option involved to achieve accurate and efficient estimations.
Analisis Kelayakan Kredit Menggunakan Classification Tree dengan Teknik Random Oversampling Vebriyanti, Lo Mei Ly; Martha, Shantika; Andani, Wirda; Rizki, Setyo Wira
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 12 Issue 1 June 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i1.24182

Abstract

Credit is providing money or bills based on the agreement between a bank and another party. Lending is inseparable from bad credit risk, so credit analysis must be conducted on prospective debtors before approving a proposed loan. This research aims to analyze creditworthiness using a Classification Tree as a classification method with Random Oversampling to overcome imbalanced data. This study uses secondary data on the status of debtors from a bank in West Kalimantan. Research data amounted to 800 data samples consisting of collectability variables as target variables and 10 independent variables, namely limit, rate, tenor, total installments, age, salary, premium and admin, agency, type credit, and type need. The Classification Tree method with Random Oversampling is used to overcome imbalanced data. Classification begins with data preprocessing, then the data is divided into training and test data with proportions of 70:30, 80:20 and 90:10 for each treatment without Random Oversampling and with Random Oversampling. Next, a classification model is formed using training data, and the classification model is validated using test data. After that, an overall evaluation of the model is carried out to determine the best model used in the classification process. Based on the research results, the best model is the model Classification Tree with Random Oversampling in proportion 70:30, with an accuracy value of 89.17%, specificity of 75.00%, and recall of 89.66%. The model can be used to classify current and non-current debtor data. The most influential variable in classifying debtor status is the total installment variable.

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