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Jl. Prof. Dr. Ing. B. J. Habibie, Moutong, Tilongkabila, Kabupaten Bone Bolango, Gorontalo, Indonesia
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Jambura Journal of Mathematics
Published by Universitas Negeri Gorontalo
ISSN : 26545616     EISSN : 26561344     DOI : https://doi.org/10.34312/jjom
Core Subject : Education,
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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Articles 16 Documents
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Search results for , issue "Vol 6, No 2: August 2024" : 16 Documents clear
Perbandingan Propensity Score Stratification dan Propensity Score Matching dengan Pendekatan Multivariate Adaptive Regression Spline Akolo, Ingka Rizkyani; Ningsih, Setia; Dukalang, Hendra
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26137

Abstract

Research on complications of Diabetes Mellitus (DM) is multifactorial, where the risk factors causing DM complications are interrelated, leading to confounding bias, which results in inaccurate research findings. Confounding bias can be reduced using the propensity score method. This study aims to compare the performance of the Propensity Score Stratification (PSS) and Propensity Score Matching (PSM) methods with the Multivariate Adaptive Regression Spline (MARS) approach in estimating treatment effects on DM complication cases. The data used is the medical records of type-2 DM patients at Hospital X. The results showed that the PSS method with the MARS approach is not suitable for small data sets, as it can lead to treatment or control groups lacking members, making it impossible to calculate the p-value in balance testing or the Percent Bias Reduction (PBR). The estimated Average Treatment Effect (ATE) using the PSS method was 0.487 with a PBR of 35.1%, whereas the estimated Average Treatment for Treated (ATT) using the PSM method was 0.531 with a PBR of 99.46%. These PBR values indicate that the best method for estimating treatment effects and the one that can reduce the most bias in this case is the PSM method with MARS. The analysis also showed that serum uric acid levels significantly affect the peripheral diabetic neuropathy (PDN) status of DM patients.
Comparison of Fuzzy Grey Markov Model (1,1) and Fuzzy Grey Markov Model (2,1) in Forecasting Gold Prices in Indonesia Soraya, Arthamevia Najwa; Firdaniza, Firdaniza; Parmikanti, Kankan
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26679

Abstract

Currently, gold investment is considered promising despite the ever-changing price of gold. However, obtaining optimal profits is a challenge for investors. Therefore, a proper forecasting method is needed to forecast the gold price so investors can know the best transaction time. This study used two forecasting methods: the Fuzzy Grey Markov Model (1,1) and a new, never-before-used approach, the Fuzzy Grey Markov Model (2,1). The Fuzzy Grey Markov Model (2,1) approach is interesting because it can be considered for forecast data that shows varying increases and decreases, such as the gold price data used in this study. Both methods are combined models that utilize fuzzy logic to handle uncertainty in data; the Grey model forms a forecasting model, and the Markov chain determines the state transition probability matrix. Next, the error rates of the two methods are compared based on the Mean Absolute Percentage Error (MAPE) value to obtain the best forecasting method. As a result of this study, the Fuzzy Grey Markov Model (1,1) was chosen as the best forecasting method with a MAPE value of 0.28%.
Propensity Score Matching Pada Pemanfaatan Data Hasil Web Scraping Untuk Perbaikan Statistik Resmi Fatimah, Fatimah; Wijayanto, Hari; Afendi, Farit Mochamad
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26568

Abstract

The Central Statistics Agency (BPS) welcomes the challenge of utilizing big data. One of the BPS publications that can be supported using big data is the inflation figure collected from the consumer price survey. One part of the consumer price survey is the HK-4 Survey, which contains house contract rates. So far, the house contract rates produced by BPS have been underestimated or lower than the actual situation. Improvements to house contract rates are carried out by matching BPS data and web scraping of house rental sites using Propensity Score Matching (PSM). The data used in this study includes DKI Jakarta, Bandung, and Semarang from September to October 2023. This study aims to find the best matching model using PSM to improve official statistics (house contract rates) by combining several propensity score value estimation methods and matching algorithms. Furthermore, the results matching the best model will be used to calculate the corrected house contract rates. The study results show that the best matching model generally uses logistic regression propensity score value estimation, the nearest neighbor matching algorithm with returns and uses a 1:1 ratio. The corrected contract rates are far above the official ones (DKI Jakarta corrected 87.27%, Bandung 316.15%, and Semarang 60.04%). Web Scraping allows it to improve official statistics because it is cost and time-saving, enhances the quality of official statistical data, and supports better decision-making in various sectors.
Comparative Analysis of ARIMA and LSTM for Forecasting Maximum Wind Speed in Kupang City, East Nusa Tenggara Magfirrah, Indah; Ilma, Meisyatul; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.25834

Abstract

This study compares the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models for predicting maximum wind speed based on accuracy measured by Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Based on the results of the research, the LSTM model is better than the ARIMA model in predicting maximum wind speed in Kupang City, East Nusa Tenggara Province. The best LSTM model has hyperparameters of 200 epochs; batch size of 32; learning rate of 0,001; and 8 neurons. Based on the evaluation results of predicted data against actual data, the MAPE value of the LSTM model is 19,40%. The benefit of this research is that it can contribute to the literature on the development of wind utilization as a basis for building power plants on small islands as a renewable resource, particularly in Kupang City, East Nusa Tenggara.
Analisis Pembentukan Portofolio Optimal pada Indeks Saham LQ-45 dengan Metode Safety First Criterion Amalia, Disya Recita; Sulistianingsih, Evy; Imro'ah, Nurfitri
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.24438

Abstract

An optimal portfolio of stocks is a combination of various stock investment assets chosen to provide the maximum level of return for a specified level of risk or provide a minimal level of risk for a specified level of return. Investors form an optimal stock portfolio with the aim of minimizing the risk of investment activities. This research discusses the formation of the optimal portfolio on LQ-45 index stocks with the Safety First Criterion method. There are three criteria in the Safety First method, namely Roy Safety First, Kataoka Safety First, and Telser Safety First. The three criteria of Safety First have the main similarity in focusing on investment risk and have different objectives. The optimal portfolio with Roy Safety First criteria aims to reduce the possibility of a high level of risk. Then, the optimal portfolio with Kataoka Safety First criteria, has the goal of maximizing returns, with a level of risk determined by investors. While the optimal portfolio of Telser Safety First criteria aims to achieve the highest expected return within a predetermined risk level. The data in this study are secondary data on the weekly closing price of the LQ-45 index for the period February 2021 to January 2023, which is 105 weeks. Based on the results of the analysis, the optimal portfolio formation for risk-loving investors is the Telser criteria portfolio. This portfolio consists of ADRO, BBNI, BMRI, ITMG, and MEDC stocks. Then, the optimal portfolio for risk-averse investors is the Roy criteria portfolio consisting of ADRO, BBNI, BMRI, MDKA, and MEDC stocks.
Penerapan Model Geographically Weighted Logistic Regression dengan Fungsi Pembobot Adaptive Gaussian Kernel pada Data Kemiskinan Nurhasanah, Nunung; Widiarti, Widiarti; Nurvazly, Dina Eka; Usman, Mustofa
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26504

Abstract

Regression analysis is one statistical method used to determine the relationship between a dependent variable and one or more independent variables. Dependent variables that are categorical are analyzed using logistic regression analysis. Geographically Weighted Logistic Regression (GWLR) is a method that is a local version of logistic regression, where location factors are considered. This method assumes that the dependent variable data are distributed binomially. In this study, the GWLR method is used to determine the factors influencing the poverty percentage in West Java Province in 2022 using an adaptive Gaussian kernel weighting function. The variables used are per capita expenditure, average length of schooling, Gross Regional Domestic Product (GRDP) per capita, and population density. The results of this study indicate that the variables of per capita expenditure, Gross Regional Domestic Product (GRDP) per capita, and population density significantly influence the poverty percentage in West Java Province in 2022.

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