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Devni Prima Sari
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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 33 Documents
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Application of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model in Forecasting the Volatility of Optimal Portfolio Stock Returns of the MNC36 Index Deswita, Siska; Sari, Devni Prima
Mathematical Journal of Modelling and Forecasting Vol. 2 No. 2 (2024): December 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v2i2.24

Abstract

Investment is a capital investment made by investors through the purchase of several stocks that are usually long-term with the hope that investors will benefit from increased stock prices. The most commonly used risk indicator in investing is volatility. Therefore, it is necessary to carry out modeling that can overcome the effects of heteroscedasticity to predict future volatility. Efforts are made to overcome the effects of heteroscedasticity by applying the Generalized Autoregressive Conditional Heterossexicity (GARCH) Model in Forecasting the Volatility of Optimal Portfolio Stock Returns on the MNC36 Index. This type of research is applied research that begins with reviewing the problem, analyzing relevant theories, and reviewing the problem and its application. Based on the results of data analysis using the residual normality test through the Jarque-Bera test, it was obtained that the GARCH model has a normal residual and is not heteroscedasticity so that it can be used as a forecasting model. BNGA shares obtained the most stable forecast results with almost constant volatility, indicating that this stock has the lowest risk compared to BBCA and BMRI stocks.
Application of Principal Component Analysis in Identifying Factors Affecting the Human Development Index Faisal, Muhammad; Fitri, Fadhilah; Zilrahmi
Mathematical Journal of Modelling and Forecasting Vol. 2 No. 2 (2024): December 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v2i2.26

Abstract

This study examines the Human Development Index (HDI) in West Sumatra Province in 2023. The HDI is an essential indicator for measuring the success of efforts to improve the quality of human life. This research aims to identify the key factors that influence the HDI. The HDI is constructed from three fundamental dimensions that indicate human quality of life: health, education, and economy. The factors within each dimension tend to be strongly correlated, as they mutually influence one another, potentially leading to multicollinearity issues. Therefore, an analysis is conducted to reduce the number of original variables into new orthogonal variables while preserving the total variance of the original variables using Principal Component Analysis (PCA). Based on this background, the study applies PCA to address multicollinearity and to identify new, more representative variables. The study findings indicate that the factors influencing the HDI are the education and economic and health welfare indexes.
Modelling the Number of Stunting Cases in Indonesia in 2022 Using Negative Binomial Regression to Address Overdispersion Oktarina, Cinta Rizki; Pahlepi, Reza
Mathematical Journal of Modelling and Forecasting Vol. 2 No. 2 (2024): December 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v2i2.27

Abstract

This study models the incidence of stunting in toddlers in Indonesia in 2022 using negative binomial regression to address the overdispersion issue often present in count data. The Poisson regression model, typically used for count data, showed less accurate results due to the variance exceeding the mean, indicating overdispersion. By adopting a negative binomial regression approach, this study accommodates higher variability in the data, leading to more accurate estimates. The results reveal that the percentage of pneumonia cases and low birth weight are significant factors in stunting incidence. In contrast, other variables, such as complete basic immunization and poverty levels, are insignificant in the final model. The final negative binomial model yielded a lower AIC value than the initial model, indicating an improved model fit, with an R-squared (Nagelkerke's R²) of 50.50%. This study offers enhanced insights into the factors influencing stunting, supporting more targeted health policy decisions to reduce stunting rates in Indonesia.
Application of the K-Means Clustering Algorithm to the Case of Stunting Risk Families in Districts/Cities of West Sumatra Province in 2023 Widiyanti; Fitri, Fadhilah
Mathematical Journal of Modelling and Forecasting Vol. 2 No. 2 (2024): December 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v2i2.29

Abstract

Stunting is one of the indicators of chronic nutritional status that has a long-term effect on child growth; the main contributing factors are households that do not have access to clean drinking water, proper sanitation facilities, and other factors. The adverse effects experienced by stunted children are reduced cognitive ability, learning ability, decreased endurance, and can lead to new diseases such as diabetes, heart disease, and many other diseases. This study uses the K-Means Cluster method to group the Regency / City of West Sumatra Province in 2023 regarding cases of stunting risk families. K-Means Cluster analysis is an analysis used to group data based on similar features or characteristics. From the results of the study, it can be concluded that the clustering of 19 regencies/cities in West Sumatra Province resulted in 2 groups (clusters): cluster 1 consists of 12 regency/city members, and cluster 2 consists of 7 regency/city members. The characteristic results obtained from each cluster formed are cluster 2 shows families with better conditions than cluster 1.
Variance and Semi-Variance with a Multi-Objective Approach Using the Spiral Optimization Method Sri Zulfa, Femilya; Agustina, Dina
Mathematical Journal of Modelling and Forecasting Vol. 2 No. 2 (2024): December 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v2i2.30

Abstract

In minimizing the risk faced by investors while maximizing returns, it is necessary to study different risk measures for portfolio optimization, namely mean-variance, and semi-variance, so that it can provide a deeper understanding of how each approach works in various market conditions. The mean-variance approach measures risk based on the total variance of portfolio returns. While the semi-variance approach only focuses on downside risk, which is the risk of loss that is more relevant to investors who tend to be conservative. By comparing these two risk measures, investors can understand the trade-offs in choosing a portfolio management strategy. To conduct a study on portfolio optimization, the author uses a multi-objective optimization approach on the mean-variance and semi-variance models, which will be solved with a spiral model. The results of this study are that the spiral model with a simple case that does not involve high dimensions can be solved quickly. However, for high dimensions with significant maximum spread points and iterations, the algorithm in this Matlab programming runs slowly, so it is ineffective in computation. This spiral method is suspected of having several solutions trapped in local minima, or the results obtained have not converged, so the resulting Pareto front is not optimal.
Alternative Strategies to Eradicate Corruption in Indonesia with Numerical Simulation of 4th Order Runge Kutta Method on Mathematical Models Rahmadi, Deddy; Rahayu, Pipit Pratiwi
Mathematical Journal of Modelling and Forecasting Vol. 2 No. 2 (2024): December 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v2i2.31

Abstract

Corruption remains a critical issue hindering Indonesia's development across various sectors, necessitating innovative approaches to combat it. This study explores alternative strategies to eradicate corruption by leveraging mathematical modeling and numerical simulations. A dynamic system representing corruption propagation is formulated, considering key variables such as enforcement intensity, public awareness, and policy effectiveness. The 4th-order Runge Kutta method simulates the model and analyzes the impact of various strategic interventions over time. The results show that the difference in initial conditions significantly affects the level of corruption, which increases or decreases in a specific time. These findings provide valuable insights for policymakers and stakeholders in designing effective, data-driven anti-corruption strategies, emphasizing the integration of rigorous mathematical tools with socio-political frameworks. The study highlights the potential of numerical simulations as a complementary approach to traditional qualitative analyses in addressing complex societal challenges like corruption.
Forecasting the Saudi Riyal to Indonesian Rupiah Exchange Rate Using ARIMA Friska, Dina
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.v3i1.32

Abstract

A Currency exchange rate is an essential indicator in a country's economy. The exchange rate of a country's currency constantly fluctuates against another country's currency at any time, such as the riyal exchange rate against the rupiah. There are several methods to determine the movement of the currency exchange rate and to forecast time series data, such as Autoregressive Integrated Moving Average (ARIMA). ARIMA is a time series data forecasting method that can handle data that is not stationary to the mean and variance, such as the riyal exchange rate against the rupiah, which fluctuates irregularly. This study will forecast the riyal exchange rate against the rupiah at Bank Indonesia. The data used is daily data. The R Studio program studies the minimum AIC value to select the best model. The ARIMA (2,1,0) model is the best in forecasting the Saudi Arabian Riyal exchange rate (SAR) against the Indonesian rupiah (IDR) with an estimated forecast error of 0.26%.
Earthquake Point Clustering in Sumatra Island using Spatio-Temporal Density-Based Spatial Clustering Application with Noise (ST-DBSCAN) Algorithm Putri, Muthiara Hazimah; Sari, Devni Prima
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.v3i1.33

Abstract

Abstract. Earthquakes are one of the natural disasters that often occur in Indonesia, especially on the island of Sumatra. Earthquakes become a frightening spectre because they cannot be predicted when they will come, where they will be located, and how strong the vibrations are, so they often cause damage and casualties. To minimise losses due to earthquakes, it is necessary to divide areas easily affected by earthquakes. One method that can be used to divide these areas is clustering techniques. This study uses a clustering method, namely Spatio Temporal-Density Based Spatial Clustering Application with Noise (ST-DBSCAN), on the dataset of earthquake points on the island of Sumatra in 1917-2023. This method uses a spatial distance parameter (ε_1= 0.28), temporal distance parameter (= 180), and minimum number of cluster members (MinPts = 7) with a silhouette coefficient of 0.0991, resulting in 145 clusters with 15 large clusters and 4922 noises. The epicentres are primarily located in Siberut Island, Tanah Bala Island and its surroundings, the Indian Sea opposite Nias Island, the Sea around the Mentawai Islands, Enggano Island and its environs, Simaulue Regency, and Enggano Island and the Sea around it. The most common type of spatio-temporal pattern found is the occasional pattern type.
The Introduction of Strassen's Algorithm and Application to 2^n Matrix Multiplication Anjelia, Davina; Dewi, Meira Parma
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.v3i1.34

Abstract

Abstract. In matrix calculation operations, especially the process of square matrix multiplication, as the order of the matrix increases, the level of accuracy required also increases. Manual calculation is prone to errors and takes a long time, especially for large order matrices. These problems can be overcome by using the Strassen algorithm. Strassen's algorithm views a matrix as a 2×2 matrix because it has four elements. Square matrix multiplication using the Strassen algorithm can be an alternative solution because the Strassen algorithm only contains seven multiplication processes. So, applying the Strassen algorithm to square matrix multiplication will be an alternative in accelerating the multiplication process, especially for matrices of a large order. This research discusses how the Strassen algorithm is formed and its application to the square matrix multiplication of order . Strassen's algorithm is obtained by transforming the elements of the product matrix C. Algebraic identity transformation is done by applying the properties that apply to the calculation operation without changing the original value. Using Strassen's Algorithm in the square matrix multiplication process can be an alternative in accelerating the multiplication process because Strassen's algorithm summarises the multiplication process into seven steps, compared to multiplication in general, which requires eight steps.
Forecasting Rainfall in Padang Panjang City Using Fuzzy Time Series Cheng Pratama, Tasya Putri; Sari, Devni Prima
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.v3i1.35

Abstract

Rainfall is essential in many areas of life, including agriculture, water resource management, and disaster mitigation.  Padang Panjang is one of the cities with high rainfall. Rainfall varies throughout the year, affecting agriculture and people's livelihoods. Therefore, accurate rainfall estimation is required to support effective planning and management. This study aims to forecast the amount of rainfall in Padang Panjang City from January 2020 to November 2024 using the fuzzy time series method of the Cheng model. The data is on the monthly rainfall amount from January 2020 to November 2024, obtained from the BMKG Padang Pariaman Climatology Station. The stages in the fuzzy time series Cheng model are forming the universe set, forming intervals, fuzzifying the data, analyzing Fuzzy Logical Relationship (FLR) and Fuzzy Logical Relationship Group (FLRG), determining the weight of the relationship, forecasting, and measuring the accuracy of predicting using Mean Absolute Percentage Error (MAPE). The forecasting results were validated using MAPE, with a value of 41%, which indicates that the model is feasible. The forecasting results for the following three periods are December 2024 high rainfall, January 2025 medium rainfall, and February 2025 high rainfall. This research shows that the fuzzy time series method of the Cheng model can be used as an alternative means of forecasting time series data.

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