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Data Analytics, Mathematical Modelling, and Forecasting (DMF) Research Group Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Negeri Padang Jalan Prof. Dr. Hamka, Air Tawar Padang, Sumatera Barat Web: mjomaf.ppj.unp.ac.id Email: mjomaf@ppj.unp.ac.id
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INDONESIA
Mathematical Journal of Modelling and Forecasting
Published by Universitas Negeri Padang
ISSN : -     EISSN : 29881013     DOI : https://doi.org/10.24036/mjmf.v1i2
Core Subject : Economy, Science,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance
The Mathematical Journal of Modelling and Forecasting are scientific journals in the fields of mathematics, statistics, actuarial, financial mathematics, computational mathematics, and applied mathematics. This journal is published twice a year, precisely in June and December in an online version. All publications are available in full text and free to download.
Arjuna Subject : Matematika - Matematika Terapan
Articles 38 Documents
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Enhancing Oil Field Investment Decisions Using Spiral Dynamics Optimization Humairah, Reni; Afrizal, Aidil Adrianda; Kartika, Hanum
Mathematical Journal of Modelling and Forecasting Vol. 3 No. 1 (2025): June 2025
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v3i1.38

Abstract

Investment is an activity in the present to obtain profits in the future. One method of investment assessment is calculating Net Present Value (NPV) using Discounted Cash Flow (DCF). To determine the right time to carry out an investment in order to obtain maximum profits, it is necessary to determine a time limit for when the investment should be carried out or not. Then, the threshold curve (optimal implementation time limit) for the investment will first be determined. After obtaining the threshold curve for each investment option, the NPV value for each investment option will then be determined. The best investment choice is the one that provides the highest NPV value. The metaheuristic method is an effective optimization method for determining the optimal investment implementation time limit (threshold curve). One metaheuristic method for determining threshold curves is the Spiral Dynamics Optimization algorithm developed by Kenichi Tamura and Keiichiro Yasuda, a search algorithm inspired by phenomena in nature such as water speed, air pressure speed, Nautilus Shells, and spiral galaxy shapes. The results of this research are areas that are above the threshold curve, which can implement the project, while areas that are below the curve are not recommended for implementing the project.
Risk Comparison in Optimal Portfolios: A Study of Value at Risk (VaR) and Tail Value at Risk (TVaR) Turnika Afdatul Rafni; Dina Agustina
Mathematical Journal of Modelling and Forecasting Vol. 3 No. 1 (2025): June 2025
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v3i1.40

Abstract

Considering investment risk is something that investors must do before deciding to invest; measuring risk provides an opportunity for investors to get the desired return and minimize losses. This study compares Value at Risk (VaR) and Tail Value at Risk (TVaR) methodologies for measuring portfolio risk. VaR is a commonly used method that provides the maximum loss at a certain confidence level and period. However, VaR is not an effective measure of risk because it does not satisfy one of the axioms of coherent risk measures, i.e., subadditivity. Subsequently, the TVaR measure emerged, which satisfies all the axioms of coherent risk measures, thereby providing a good and effective measure of risk. The optimal portfolio will be formed using the Single Index model, simplifying the Markowitz portfolio model. The Composite Stock Price Index will be the only factor influencing other stocks in this model. The data used data from stocks that were consistently listed on the IDX30 index from 24/10/2022 to 25/10/2024. Based on the result of the analysis of data, the optimal portfolio consists of 5 stocks, i.e., PT Bank Mandiri (BMRI.JK), PT Indofood Sukses Makmur (INDF.JK), PT Bank Central Asia (BBCA.JK), PT Bank Negara Indonesia (BBNI.JK), and PT Barito Pacific (BRPT.JK). Risk measures were compared on the optimal portfolio, using a confidence level of 1-α=95%, with a daily time period, and an initial investment capital of IDR 1 billion. The estimated VaR risk measure is IDR 15.38 million, while TVaR reaches IDR 23.25 million.
Strategy for Enhancing GRU-RNN Performance through Parameter Optimization Hermansah
Mathematical Journal of Modelling and Forecasting Vol. 3 No. 1 (2025): June 2025
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v3i1.41

Abstract

This study examines the selection of optimal parameters in the Gated Recurrent Unit-Recurrent Neural Network (GRU-RNN) model for forecasting inflation in Indonesia. Accurate forecasting requires precise model parameter adjustments, especially for time-series data, which can be either linear or non-linear. The study evaluates several parameters, including learning rate, number of epochs, optimization methods (Stochastic Gradient Descent (SGD) and Adaptive Gradient (AdaGrad)), and activation functions (Logistic, Gompertz, and Tanh). The results show that the best combination consists of the SGD optimization method, logistic activation function, a learning rate of 0.05, and 450 epochs, which delivers the best performance by minimizing errors and achieving high prediction accuracy. When compared to other forecasting models such as Exponential Smoothing (ETS), Autoregressive Integrated Moving Average (ARIMA), Feedforward Neural Network (FFNN), and Recurrent Neural Network (RNN), the GRU-RNN model shows significant superiority. Additionally, the Logistic activation function proves to be more effective in maintaining stability and prediction accuracy, while the use of the Adaptive Gradient (AdaGrad) method results in lower performance. These findings underscore the GRU-RNN model's ability to handle non-linear time-series data and provide insights for developing more accurate and efficient forecasting models in the future.
Implementation of Elgamal Elliptic Curve-Based Cryptographic Systems (ECEG) in Text Encryption Oktariza, Kenanga Dylla; Dewi, Meira Parma
Mathematical Journal of Modelling and Forecasting Vol. 3 No. 2 (2025): December 2025
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v3i2.42

Abstract

Modern cryptography continues to evolve to meet the challenges of information security in the digital age. One of the most widely used algorithms is ElGamal's Elliptic Curve (ECEG). This public-key cryptography-based encryption method offers a high level of security with better computational efficiency than classical algorithms. This study implements the ECEG algorithm in the text encoding process to ensure data confidentiality and integrity. The encryption process is performed using the recipient's public key, while decryption is performed using the corresponding private key. The entire encryption and decryption process in this algorithm can be understood mathematically through operations on elliptical curves. The purpose of this study is to determine the peculiarities of each elliptical curve selected on an ECEG. This study uses a curves y^2=(x^3+8x+25) mod 37 generates all the points on the curve that can be used as a generator that will represent all the letters in the alphabet and numbers from 0 to 9.
Survival Analysis of Patients with Chronic Kidney Disease at Dr Achmad Darwis Regional General Hospital Using the Kaplan-Meier Method Anis, Mei Syarah; Sari, Devni Prima
Mathematical Journal of Modelling and Forecasting Vol. 3 No. 2 (2025): December 2025
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v3i2.43

Abstract

Abstract. Chronic kidney disease (CKD) is a global health problem with a steadily increasing incidence. In West Sumatra, its prevalence is recorded at 0.2%, with the highest proportion occurring in individuals aged 45–54 years. Although relatively low, CKD still has a substantial impact on patients' quality of life. To date, data on the survival probability of CKD patients at the regional level, particularly in the district where this study was conducted, remain limited and require further investigation. This study aims to determine the overall survival probability of CKD patients and to examine the factors that influence survival, such as age, sex, disease stage, history of diabetes mellitus, hypertension, anaemia, heart disease, and smoking. This study employs survival analysis using the Kaplan-Meier method and the log-rank test to compare differences between groups. The results show that the overall survival probability of the 140 patients declined significantly over the four-year observation period. The Kaplan-Meier analysis revealed a sharp decline in survival probability within the first 12 days of treatment, with only 20.9% of patients remaining alive by day 12. Based on the log-rank test, the factors significantly associated with survival were age, history of diabetes mellitus, hypertension, and heart disease. These findings underscore the importance of early detection and integrated management of comorbidities in clinical practice, as they may help improve survival outcomes and guide healthcare planning for CKD patients in regional settings.
Digital Signature Using the SHA-512 Hash Function and the ElGamal Cryptographic Algorithm for Document Security Bokari, Adib Yusron; Dewi, Meira Parma
Mathematical Journal of Modelling and Forecasting Vol. 3 No. 2 (2025): December 2025
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v3i2.44

Abstract

This study proposes a secure digital signature scheme using the SHA-512 hash function and the ElGamal cryptographic algorithm to ensure the authenticity and integrity of digital documents. The increasing reliance on digital communication has made document security a critical concern, as digital documents are vulnerable to unauthorised access, tampering, and fraud. To address this issue, a digital signature approach is implemented, leveraging the SHA-512 hash function to generate a unique message digest and the ElGamal algorithm to encrypt and decrypt the signature. The proposed system ensures that any alteration or modification of the document will result in a mismatch between the expected and actual hash values, thereby detecting potential security breaches. The combined use of SHA-512 and ElGamal algorithms provides a robust and secure digital signature scheme, guaranteeing the confidentiality, integrity, and authenticity of digital documents. This study demonstrates the effectiveness of the proposed scheme in preventing fraud and protecting digital documents from various security threats, making it a reliable solution for secure digital communication.
Statistical Modelling of Rainfall Data Using Robust Kriging with Gaussian Semivariogram in Bengkulu Province Rizki Oktarina, Cinta; Pahlepi, Reza
Mathematical Journal of Modelling and Forecasting Vol. 3 No. 2 (2025): December 2025
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v3i2.46

Abstract

This study aims to predict rainfall in Bengkulu Province for January 2024 using the Robust Kriging method, an advanced geostatistical approach designed to handle outliers and non-ideal spatial characteristics. The novelty of this study lies in integrating Robust Kriging with a Gaussian semivariogram for short-term rainfall prediction in Bengkulu Province. This combination has not been explored in previous hydrometeorological studies. Rainfall data were obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) and analysed to identify spatial dependency and variation. The analysis began with descriptive statistics, assumption testing, and outlier detection, followed by the construction of robust empirical and theoretical semivariogram models. Three semivariogram models, Spherical, Exponential, and Gaussian, were compared to determine the most suitable model based on Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values. The results indicate that the Gaussian model produced the smallest MSE and MAPE values, showing the best fit to the empirical semivariogram. The Robust Kriging interpolation generated spatial predictions of rainfall intensity across Bengkulu, showing higher rainfall in the north and lower rainfall in the south. The findings demonstrate that Robust Kriging effectively improves prediction accuracy by minimizing the influence of outliers and optimizing spatial weighting. These results provide valuable insights for water resource management, agricultural planning, and hydrometeorological disaster mitigation in Bengkulu Province.
Application of Algorithm Learning Vector Quantization for Air Quality Classification Roufsaldiaz Nawfal; Dina Fitria; Chairina Wirdiastuti
Mathematical Journal of Modelling and Forecasting Vol. 3 No. 2 (2025): December 2025
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v3i2.48

Abstract

This study aims to classify air quality using the Learning Vector Quantization (LVQ) algorithm based on the Air Quality and Pollution Assessment dataset obtained from Kaggle. The dataset comprises 5,000 observations, of which 4,000 were used for training and 1,000 for testing. The analytical process includes data preprocessing (normalization), the construction and training of the LVQ model, and performance evaluation using a confusion matrix. The experimental results demonstrate that the LVQ model successfully classified 903 of 1,000 test samples, yielding an overall accuracy of 90.3%. This level of accuracy indicates that the LVQ algorithm can capture relevant patterns in air quality variables and perform reliable classification across different air quality categories. The findings suggest that LVQ can serve as a potential foundation for developing automated air quality monitoring and decision-support systems. Future studies are encouraged to compare LVQ with other machine learning classification techniques to build a more optimal model and to gain deeper analytical insights.

Page 4 of 4 | Total Record : 38

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