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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics LEMMA: Letters Of Mathematics Education MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Eksakta : Berkala Ilmiah Bidang MIPA Pelita Eksakta Majalah Ilmiah Matematika dan Statistika (MIMS) Journal of Applied Data Sciences Journal of Science and Technology MATHunesa: Jurnal Ilmiah Matematika Journal of Mathematics UNP Mathematical Journal of Modelling and Forecasting Journal of Statistics and Data Science
Devni Prima Sari
Universitas Negeri Padang
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation Other

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Journal : Mathematical Journal of Modelling and Forecasting

 1 
University Election Analysis Logistic Regression Approach with Dummy and Ordinal Variables Miftha Delinda; Devni Prima Sari
Mathematical Journal of Modelling and Forecasting Vol. 1 No. 2 (2023): December 2023
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v1i2.13

Abstract

Education has a very important role to advance the development of a country. One of them is the university. Thus, if you continue your studies at university, it is hoped that you will have the knowledge and skills in accordance with the study program you are taking, which will later become the basic capital to be more competent in the world of work. Logistic regression is a statistical method that can be used to determine the factors that influence the choice of university for class XII Phase F students. The dependent variable consists of two categories. This research aims to determine the factors that influence the choice of university for class XII Phase F students. This type of research is applied research and uses primary data obtained from filling out questionnaires. This research was carried out at SMAN 3 Padang. The population in this study were all students in class XII Phase F at SMA Negeri 3 Padang with a sample of 78 students obtained using the Purposive Sampling method. The results of the research show that the factors that influence the choice of university for class XII Phase F students at SMA Negeri 3 Padang are father's work based on educated and trained labor, father's work based on educated labor, father's work based on trained labor, father's work based on uneducated and unskilled labor, convenient university location, easy access to transportation, rent, food and daily living costs -affordable days according to budget, and information from social media and websites.
Optimal Portfolio Risk Estimation Using Expected Shortfall of Jakarta Islamic Index (JII) Shares Lestari, Adika Risky; Sari, Devni Prima
Mathematical Journal of Modelling and Forecasting Vol. 2 No. 1 (2024): June 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v2i1.19

Abstract

Forming an optimal portfolio using the Mean-Variance method with Downside Deviation as a measure of risk produces a good combination of assets. Before investing, estimating risk as a worst-case scenario is very important. Expected shortfall (ES) serves as a risk measure that takes into account the possibility of losses that exceed Value at Risk (VaR). This study aims to determine the optimal portfolio and compare ES and VaR at the 90%, 95%, and 99% confidence levels. This research data involves 3 stocks namely ACES, WIFI, and TLKM. Based on the results of the analysis conducted, the optimal combination of weights is ACES (19%), WIFI (10%), and TLKM (71%). Comparison of ES and VaR shows that the higher the level of confidence, the higher the VaR and ES values generated, so the greater the risk that will be borne by investors and the capital allocation used to cover these losses.
Analysis of Product Quality Control Using the Taguchi Method and Principal Component Analysis (PCA) at the Pabrik Tahu Alami Gulo, Trimodesman Hardinsyah; Sari, Devni Prima
Mathematical Journal of Modelling and Forecasting Vol. 2 No. 1 (2024): June 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v2i1.22

Abstract

Indonesia's rapid economic growth in the global business sector has intensified competition among entrepreneurs, necessitating stringent control over product quality for companies to sustain their market position. This study utilizes the Taguchi method and Principal Component Analysis (PCA) to enhance quality control processes. The Taguchi method focuses on offline quality control with a single response, while PCA is employed for multiple responses. Experiments were conducted at Pabrik Tahu Alami, examining four factors: soybean rate, soaking time, boiling time, and whey water rate, each at three levels. The optimal combination determined was 3 kg of soybeans (level 1), 4 hours of soaking time (level 1), 20 minutes of boiling time (level 2), and 5 litres of whey water (level 2). These results provide a robust framework for optimizing product quality in similar production settings.
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.
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.
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.
Co-Authors 1004, Anisa Adellia, Clara Febby Adhitya Ronnie Effendie, Adhitya Ronnie Afifah Humayrah Afifah, Wardah Hasna Anggraini, Fira arsilla uswatunnisa Danardono Danardono Dedi Rosadi Deswita, Siska Dina Agustina Dina Agustina, Dina Dina Fakhriyana Emmelia Safani Fadhilah Fitri Febri, Fera Malianis Filfiqri, Hamimatul Gulo, Trimodesman Hardinsyah Gusvira Widuri irwandi irwandi Jayawarsa, A.A. Ketut Jeri Jefrianto Lestari, Adika Risky Media Rosha Medika, Amelia Nurul Miftha Delinda Muhammad Rashif Hilmi Pepi Novianti Prastika, Ifa Pratama, Tasya Putri Puja Ermiati Putri, Elisa Ananda Putri, Muthiara Hazimah Putri, Rahma Dana Putriyana Nursal Rara Sandhy Winanda Riri Indriani Rohadatul Aisy Sadli, Wanda Hamidah Soleha, Annisa Sumita, Nurmaya Tessy Octavia Mukhti Tri Rahmayani Ulfasari Rafflesia Wardana, Yuni Yolandafitri Zulvia