Jambura Journal of Mathematics
Jambura Journal of Mathematics (JJoM) is a peer-reviewed journal published by Department of Mathematics, State University of Gorontalo. This journal is available in print and online and highly respects the publication ethic and avoids any type of plagiarism. JJoM is intended as a communication forum for mathematicians and other scientists from many practitioners who use mathematics in research. The scope of the articles published in this journal deal with a broad range of topics, including: Mathematics; Applied Mathematics; Statistics; Applied Statistics.
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<![CDATA[Karakteristik Modul Endoprima Lemah ]]>
<![CDATA[Ainurrofiqoh, Dewi Ika]]>
<![CDATA[ Jambura Journal of Mathematics Vol 6, No 2: August 2024 ]]>
Publisher : <![CDATA[Department of Mathematics, Universitas Negeri Gorontalo ]]>
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DOI: 10.37905/jjom.v6i2.26450
<![CDATA[In this research, we studied weakly endoprime module, which is a development of weakly prime module by reviewing its fully invariant submodule. This is similar to the development of endoprime module from prime module. This primary objective of this research are to determine the relationship between weakly endoprime module and endoprime module and examine the fully invariant submodules of weakly endoprime module. The method employed in this research involves studying the relationship between weakly prime modules and prime modules, endoprime modules and prime modules, as well as weakly endoprime modules and weakly prime modules. The results obtained are that each endoprime module is a weakly endoprime module, but the converse is not necessarily true. Moreover, a fully invariant submodule of a weakly endoprime module is not necessarily a weakly endoprime module.]]>
<![CDATA[Koreksi pada artikel Hayati dkk.: Teori Titik Tetap untuk Tipe Kannan yang Diperumum dalam Ruang b-Metrik Modular Lengkap ]]>
<![CDATA[Hayati, Afifah]]>;
<![CDATA[Sofiyati, Noor]]>
<![CDATA[ Jambura Journal of Mathematics Vol 6, No 2: August 2024 ]]>
Publisher : <![CDATA[Department of Mathematics, Universitas Negeri Gorontalo ]]>
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DOI: 10.37905/jjom.v6i2.26441
<![CDATA[This article is a commentary on research that is conducted by Hayati et al. which was published in Jambura Journal of Mathematics volume 5 number 2 in 2023. It was found that there is an error occurred in the process of proving the fixed point theorems for generalized Kannan type mappings in modular b-metric spaces. This error makes the existence of the fixed point in the theorem is not valid. By adding the convex property to the modular b-metric, the fixed point theorems still holds.]]>
<![CDATA[Perbandingan Metode ARIMA dan SARIMA Dalam Peramalan Jumlah Penumpang Bandara Provinsi Kepulauan Bangka Belitung ]]>
<![CDATA[Febiola, Aulia]]>;
<![CDATA[Dewi, Amelia]]>;
<![CDATA[Fazarin, Fatia Maura]]>;
<![CDATA[Ramadhani, Fitri]]>;
<![CDATA[Khaffi, Muhammad Akbar]]>;
<![CDATA[Akbar, Ridho]]>;
<![CDATA[Dalimunthe, Desy Yuliana]]>
<![CDATA[ Jambura Journal of Mathematics Vol 6, No 2: August 2024 ]]>
Publisher : <![CDATA[Department of Mathematics, Universitas Negeri Gorontalo ]]>
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DOI: 10.37905/jjom.v6i2.25081
<![CDATA[One of the transportation that is often used by people in Bangka Belitung to travel outside the area is airplanes. Judging from the data on the number of passengers at two airports in the Bangka Belitung Islands Province, the highest number of airplane passengers departing outside the region in April 2023 was 88,511 people. If there isan excessive increase in the number of passengers, it will have an impact on the quality of airport service levels and disrupt the stability of flight traffic. The purpose of this study is to compare the results of forecasting the number of passengers at the airport of Bangka Belitung Islands Province for the future period using the ARIMA and SARIMA methods. Based on the results of the analysis, the best method to forecast the number of passengers at the Bangka Belitung Islands Provincial Airport is the ARIMA method with the best model, namely ARIMA (0,1,1). In general, the results of forecasting the number of passengers at the Bangka Belitung Islands Provincial Airport from May 2024 to April 2026 will increase the number of passengers continuously until April 2026.]]>
<![CDATA[Using Real Options and Geometric Brownian Motion Methods to Evaluate Petroleum Projects in Indonesia ]]>
<![CDATA[Jalaludin, Paiz]]>;
<![CDATA[Nuraini, Ani]]>;
<![CDATA[Rahman, Alrafiful]]>
<![CDATA[ Jambura Journal of Mathematics Vol 6, No 2: August 2024 ]]>
Publisher : <![CDATA[Department of Mathematics, Universitas Negeri Gorontalo ]]>
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DOI: 10.37905/jjom.v6i2.26718
<![CDATA[There are several methods for evaluating the value of a project. The most commonly used method is the Discounted Cash Flows (DCF) method which is more practical in its use. However, the DCF method still has several weaknesses, including not paying attention to the flexibility of the manager's decision-making when the project is carried out. The Real Options method enhances this by offering more flexible and varied models. This study uses Benninga's version of the binomial method to evaluate the value of petroleum projects with the characteristics of existing companies in Indonesia. In this study, oil prices are assumed to move following the Geometric Brownian Motion (GBM) model which is commonly used in modeling the movement of a fluctuating price. In addition, the author also modifies the binomial model by including expansion options, divestment options and a combination of both. The results of this study show that the more options that managers can choose in decision-making, the greater the opportunity for the company to optimize profits and minimize losses.]]>
<![CDATA[Comparison of Seasonal ARIMA and Support Vector Machine Forecasting Method for International Arrival in Lombok ]]>
<![CDATA[MY, Hadyanti Utami]]>;
<![CDATA[Setyowati, Silfiana Lis]]>;
<![CDATA[Notodiputro, Khairil Anwar]]>;
<![CDATA[Angraini, Yenni]]>;
<![CDATA[Mualifah, Laily Nissa Atul]]>
<![CDATA[ Jambura Journal of Mathematics Vol 6, No 2: August 2024 ]]>
Publisher : <![CDATA[Department of Mathematics, Universitas Negeri Gorontalo ]]>
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DOI: 10.37905/jjom.v6i2.26478
<![CDATA[Seasonal Autoregressive Integrated Moving Average is a statistical model designed to analyze and forecast data with that shows seasonal patterns and trends. Support Vector Machine (SVM) is a machine learning-based technique that can be used to forecast time series data. SVM uses the kernel tricks to overcome non-linearity problems, whereas The SARIMA model is well-suited for data that exhibit seasonal fluctuations that repeat over time. Lombok International Airport is the main gateway to West Nusa Tenggara and has become a symbol of tourism growth in the region. Time series analysis is a very useful tool in determining patterns and forecasting the number of international arrivals at Lombok International Airport within a certain period. This study aims to compare the SARIMA model and SVM which can read non-linear patterns in the number of international arrivals at Lombok International Airport. After obtaining the SARIMA and SVM models, the two models are evaluated using test data based on the smallest RMSE value. The SVM model with a linear kernel trick provides the smallest RMSE when compared to SARIMA with SVM RMSE is 238,655. While the best model in Seasonal ARIMA is SARIMA (3,1,0)(1,0,0)12, the forecasting results show SARIMA is better in the forecasting process for the next 10 months.]]>
<![CDATA[Perbandingan Value at Risk dan Expected Shortfall pada Portofolio Optimal menggunakan Metode Downside Deviation ]]>
<![CDATA[Nugrahaeni, Indah]]>;
<![CDATA[Perdana, Hendra]]>;
<![CDATA[Satyahadewi, Neva]]>
<![CDATA[ Jambura Journal of Mathematics Vol 6, No 2: August 2024 ]]>
Publisher : <![CDATA[Department of Mathematics, Universitas Negeri Gorontalo ]]>
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DOI: 10.37905/jjom.v6i2.24326
<![CDATA[Portfolio formation is one of the strategies that investors can do to get the best results Portfolio formation can use the Downside Deviation method. The optimal portfolio with this method uses downside deviation and sets the return below the benchmark as a measure of risk. Every optimal portfolio certainly cannot be separated from risk. To measure risk, you can use the Value at Risk (VaR) and Expected Shortfall values. This study aims to form an optimal portfolio using the Downside Deviation method and continued by comparing the possible losses that occur from the formed portfolio using the VaR and Expected Shortfall values. The data used in this study is the daily closing price data of LQ-45 Index stocks in the banking sector in the period February-June 2023. From the stock data, data selection is carried out by selecting stocks that have a positive expected return and are normally distributed. Then, the optimal portfolio formation stage is continued using the Downside Deviation method and comparing the possible risks formed with the VaR and Expected Shortfall values. The results of this study show that the optimal portfolio with the Downside Deviation method consists of four stocks, namely with the stock codes BRIS.JK, BBRI.JK, BBNI.JK, and BBCA.JK. This study uses a case example by investing capital of Rp100,000,000 with a one-day time period and three levels of confidence, namely 90%, 95%, and 99%. Based on the comparison of the risk value of the portfolio formed using VaR and Expected Shortfall, it is shown that the possible risk with the Expected Shortfall method is greater than the VaR value. Therefore, Expected Shortfall is better in estimating the maximum risk.]]>
<![CDATA[Comparative Study in Controlling Outliers and Multicollinearity Using Robust Performance Jackknife Ridge Regression Estimator Based on Generalized-M and Least Trimmed Square Estimator ]]>
<![CDATA[Saputri, Gustina]]>;
<![CDATA[Herawati, Netti]]>;
<![CDATA[Ruby, Tiryono]]>;
<![CDATA[Nisa, Khoirin]]>
<![CDATA[ Jambura Journal of Mathematics Vol 6, No 2: August 2024 ]]>
Publisher : <![CDATA[Department of Mathematics, Universitas Negeri Gorontalo ]]>
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DOI: 10.37905/jjom.v6i2.24828
<![CDATA[Regression analysis is one of the statistical methods used to determine the causal relationship between one or more explanatory variables to the affected variable. The problem that often occurs in regression analysis is that there are multicollonity and outliers. To deal with such problems can be solved using ridge regression analysis and robust regression. Ridge regression can solve the problem of multicollinearas by assigning a constant k to the matrix Z′Z. But in this method the resulting bias value is still high, so to overcome this problem, the jackknife ridge regression method is used. Meanwhile, to overcome outliers in the data using robust regression methods which have several estimation methods, two of which are the Generalized-M (GM) estimator and the Least Trimmed Square (LTS) estimator. The aim of the study is to solve the problem of multicollinearity and outliers simultaneously using robust jackknife ridge regression method with GM estimators and LTS estimators. The results showed that the robust ridge jackknife regression method with LTS estimator can control multicollinearity and outliers simultaneously better based on MSE, AIC and BIC values compared to the robust ridge jackknife regression method with GM estimators. This is indicated by the value MSE = -6.60371, AIC = 75.823 and BIC = 81.642 on LTS estimators that are of lower value than GM estimators.]]>
<![CDATA[Implementasi CNN-BiLSTM untuk Prediksi Harga Saham Bank Syariah di Indonesia ]]>
<![CDATA[Mushliha, Mushliha]]>
<![CDATA[ Jambura Journal of Mathematics Vol 6, No 2: August 2024 ]]>
Publisher : <![CDATA[Department of Mathematics, Universitas Negeri Gorontalo ]]>
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DOI: 10.37905/jjom.v6i2.26509
<![CDATA[Stock price forecasting plays a crucial role in stock investment. Accuracy in predicting stock prices can provide significant financial benefits and help reduce investment risks. Stock price data are time series with high-frequency characteristics, non-linearity, and long memory, which makes stock price prediction a complex challenge. This research proposes a method for predicting the stock prices of Islamic banks in Indonesia using CNN-BiLSTM. This method aims to improve prediction accuracy by utilizing the feature extraction capabilities of CNN and the ability of BiLSTM to understand the temporal sequences of stock data. The data used in this research are the closing stock prices of Bank Syariah Indonesia (BSI), Bank Tabungan Pensiunan Negara Syariah (BTPN Syariah), and Bank Panin Dubai Syariah (PDSB) from January 2, 2020, to July 4, 2024. Testing these three stocks yielded MAPE values of 2.376%, 2.092%, and 0.629%, respectively. The study results show that the CNN-BiLSTM prediction model produced has very good accuracy in predicting stock prices.]]>
<![CDATA[Implementasi Metode New Jersey dalam Perhitungan Cadangan Premi dengan Suku Bunga Stokastik dan Konstan ]]>
<![CDATA[Sulistyawati, Yuni]]>;
<![CDATA[Kartikasari, Mujiati Dwi]]>
<![CDATA[ Jambura Journal of Mathematics Vol 6, No 2: August 2024 ]]>
Publisher : <![CDATA[Department of Mathematics, Universitas Negeri Gorontalo ]]>
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DOI: 10.37905/jjom.v6i2.24668
<![CDATA[Premium reserve allocation represents an obligation undertaken by insurance companies to set aside funds for future claims payment to policyholders. Some insurance companies have faced operational challenges, leading to their closure, primarily due to inaccurate premium reserve computations. This research aims to calculate premium reserve in lifelong insurance using the New Jersey method, an improvement upon the Illionis method. The New Jersey method initiates the premium reserve at the beginning or end of the first year at zero dollars. The majority of premium reserve calculations still rely on constant interest rates. However, in reality, this approach inadequately reflects future fluctuations in interest rates, which are crucial for long-term life insurance products. Therefore, this study implements a more realistic approach using stochastic elements, using the Vasicek stochastic interest rate model to determine premium reserve values. From this research, it was found that there was quite a significant difference between the New Jersey method premium reserve value and the two interest rates. The calculation graph shows that the premium reserve value using the Vasicek model of stochastic interest rates tends to be lower than when using constant interest rates. This can be caused by the results of non-constan variations in interest rates in the Vasicek model which ultimately results in fluctuations in interest rates which wffect the calculation of premium reserve.]]>
<![CDATA[The Comparison A-Optimal and I-Optimal Design in Non-Linear Models to Increase Purity Levels Silicon Dioxide ]]>
<![CDATA[Aliu, Muftih Alwi]]>;
<![CDATA[Syafitri, Utami Dyah]]>;
<![CDATA[Fitrianto, Anwar]]>;
<![CDATA[Irzaman, Irzaman]]>
<![CDATA[ Jambura Journal of Mathematics Vol 6, No 2: August 2024 ]]>
Publisher : <![CDATA[Department of Mathematics, Universitas Negeri Gorontalo ]]>
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DOI: 10.37905/jjom.v6i2.26253
<![CDATA[One of the obstacles that arise in optimal design is the non-linear model. The relationship between temperature factors and the temperature increase rates with the purity of silicon dioxide (SiO2) forms a non-linear pattern. Determining the optimal design for a non-linear model is relatively more complex than a linear model because it requires additional information in its information matrix. Therefore, this issue necessitates further research on optimal design in non-linear models. This study uses the polynomial Taylor approach to approximate the non-linear equation through a linear equation using the appropriate optimal design methods, namely A-Optimal and I-Optimal criterion. The point search algorithm used was variable neighborhood search, this algorithm searches for design points by exploring several different neighborhood structures. These two methods were chosen to compare the characteristics and performance of the designs produced, aiming to obtain an optimal design to improve SiO2 purity (non-linear case) using the same algorithm, VNS. The research results showed that the design pattern produced by the A-Optimal design formed three temperature groups, namely the minimum temperature of 800°C - 820°C, the middle temperature of 850°C, 860°C, and the maximum temperature of 900°C, with varying temperature increase rates in the design area. The design pattern produced by the I-Optimal design formed a full quadratic pattern, namely the minimum temperature of 800°C and the maximum temperature of 900°C, with varying temperature increase rates in the design area. The I-Optimal design demonstrated the best performance (most optimal) in the aspect of prediction variance compared to the A-Optimal design across all alternative points in this study to improve SiO2 purity.]]>
