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INDONESIA
Jurnal Matematika Sains dan Teknologi
Published by Universitas Terbuka
ISSN : 14111934     EISSN : 24429147     DOI : -
Core Subject : Education, Agriculture,
Agriculture, Biological Sciences & Forestry Mathematics Other
Merupakan media informasi dan komunikasi para praktisi, peneliti, dan akademisi yang berkecimpung dan menaruh minat serta perhatian pada pengembangan Matematika, ilmu pengetahuan dan teknologi. Diterbitkan oleh Lembaga Penelitian dan Pengabdian kepada Masyarakat, Universitas Terbuka.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol. 24 No. 2 (2023)" : 5 Documents clear
Forecasting Consumer Price Index Expenditure Inflation for Food Ingredients Using Singular Spectrum Analysis Nur Aziza S; Aswi Aswi; Muhammad Fahmuddin S; Asrirawan
Jurnal Matematika Sains dan Teknologi Vol. 24 No. 2 (2023)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v24i2.4868.2023

Abstract

Inflation is an economic problem that significantly impacts the macro economy and people's real income if it occurs continuously. South Sulawesi Province often experienced significant inflation fluctuations during 2005-2019. In 2015, inflation in South Sulawesi reached 3.32%, ranking the highest in Eastern Indonesia. Ten food ingredients played an essential role in influencing inflation that year. However, until now, research on forecasting Consumer Price Index expenditure inflation for food ingredients in South Sulawesi using the Singular Spectrum Analysis method has never been carried out. The novelty in this research lies in using the Singular Spectrum Analysis method, which provides a new contribution to forecasting inflation trends in South Sulawesi and deepens understanding of regional inflation problems. This research aims to forecast consumer price index expenditure inflation for food ingredients in South Sulawesi using the Singular Spectrum Analysis method. This research used CPI expenditure inflation data for food ingredients from the official website of the Central Statistics Agency of South Sulawesi for the monthly period from January 2014 - June 2022. The forecasting results show that the lowest inflation rate is predicted to occur in December 2022 at -0,12%, while the highest level is expected to be reached in May 2023 at 0.43%. Furthermore, the mean absolute percentage error value of 3.54% indicates that the forecasting model has a very good level of accuracy. The results of this forecasting have the potential to be used by economic policymakers in South Sulawesi in designing more effective policies to overcome the problem of inflation, especially in the food ingredients and its impact on society. The practical implications of this research can help improve regional economic stability and community welfare.
The Fifth Coefficient Approximation of The Inverse Strongly Convex Function Krisna Adilia Daniswara; Aris Alfan; Ahmad Khairul Umam
Jurnal Matematika Sains dan Teknologi Vol. 24 No. 2 (2023)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v24i2.4977.2023

Abstract

This paper discusses the fifth coefficient approximation of the inverse strongly convex function. Strongly convex function is a subclass of convex function. Those functions are included as univalent functions. Using corresponding lemmas, we give sharp limits for the fifth coefficient of the inverse strongly convex function. The limit is sharp if the value of the approximation has the same value as the limit. We verify that the limit of the fifth coefficient of the inverse strongly convex function differs from that of the strongly convex function in some interval but still have the same value in a point. Besides, we also explain that the sharp limit of the fifth inverse coefficient is less than or equal to one.
Hybrid Autoregressive Integrated Moving Average-Support Vector Regression for Stock Price Forecasting Hanan Albarr; Rosita Kusumawati
Jurnal Matematika Sains dan Teknologi Vol. 24 No. 2 (2023)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v24i2.4983.2023

Abstract

Stock investment provides high-profit opportunities but also has a high risk of loss. Investors use various decision-making methods to minimize this risk, such as stock price forecasting. This research aims to predict daily closing stock prices using a hybrid Autoregressive Integrated Moving Average (ARIMA)-Support Vector Regression (SVR) model and compare it with the single model of ARIMA and SVR, as well as compiling the R-shiny web for the hybrid ARIMA-SVR model which makes it easier for investors to use the model to support investment decision making. The hybrid ARIMA-SVR model is composed of two components: the linear component from the results of stock price forecasting using the Autoregressive Integrated Moving Average (ARIMA) model and the nonlinear components from the residual forecasting results of the ARIMA model using the Support Vector Regression (SVR) model. The data used was closing stock price data from April 1, 2019, to April 1, 2021, from PT Unilever Indonesia Tbk (UNVR.JK), PT Perusahaan Gas Negara Tbk (PGAS.JK), and PT Telekomunikasi Indonesia Tbk (TLKM.JK), from the Yahoo Finance website. The research results conclude that the hybrid ARIMA-SVR model has excellent capabilities in forecasting stock prices with the MAPE values ​​for UNVR, PGAS, and TLKM stocks, respectively of 0.797%, 2.213%, and 0.993%, which are lower than the MAPE values of ARIMA-GARCH and SVR models. The hybrid model can be an alternative model with excellent capabilities in forecasting stock prices.
Forecasting Daily Maximum and Minimum Air Temperatures in The Cilacap District Using Arima and Exponential Smoothing Hana Yulia Anggraeni; Riski Aspriyani; Mizan Ahmad
Jurnal Matematika Sains dan Teknologi Vol. 24 No. 2 (2023)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v24i2.5078.2023

Abstract

This research aims to predict daily maximum and minimum air temperatures in Cilacap Regency using ARIMA and Exponential Smoothing. Data was obtained from recordings carried out by BMKG Cilacap using maximum and minimum thermometers taken from January 1, 2016, to December 31, 2021. The results show that the best forecasting model uses the ARIMA (2,1,2) model for maximum temperature and the ARIMA (1,1,1) model for minimum temperature, with the MAPE value of 2.09% for the maximum temperature and 2.44% for the minimum temperature, while the RMSE value obtained is 0.9177 for the maximum temperature and 0.8001 for the minimum temperature. Based on the ARIMA model, Cilacap's daily maximum temperature in 2022 was predicted to be around 30.6ᵒC, with a 95% confidence interval between 28ᵒC - 35ᵒC, while the minimum temperature was predicted to be around 25.1ᵒC, with a 95% confidence interval between 23ᵒC - 28ᵒC.
Mapping Indonesia's Covid-19 Death Case with Comorbidities Using Correspondence Analysis Melinda Putri Utami; Rosita Kusumawati
Jurnal Matematika Sains dan Teknologi Vol. 24 No. 2 (2023)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v24i2.5142.2023

Abstract

This research aims to map and identify COVID-19 deaths with comorbidities in Indonesia using correspondence analysis. The data collection technique involved the analysis of 6231 samples of COVID-19 death cases with comorbidities in Indonesia from the official website of the COVID‑19 Response Acceleration Task Force. The variables used were the number of COVID-19 deaths with comorbid hypertension, diabetes mellitus, cardiovascular disease, chronic obstructive pulmonary disease, kidney disease, immune disorders, liver disease, cancer, asthma, pregnancy, tuberculosis, and other respiratory disorders. The findings from this study divide four groups of provinces with characteristics: Group One with the characteristics of COVID-19 death cases with comorbid hypertension, diabetes mellitus, heart disease, kidney disease, lung disease, immune disorders, and cancer; Group Two with the characteristics of COVID-19 death cases with comorbid pregnancy, liver disease, and tuberculosis; Group Three with the characteristics of COVID-19 death cases with comorbid asthma; and Group Four with the characteristics of COVID-19 death cases with other comorbid respiratory disorders.

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