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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 403 Documents
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PERBANDINGAN PERFORMA METODE INTERPOLASI POLINOMIAL NEWTON-GREGORY MAJU DAN NEWTON-GREGORY MUNDUR DALAM MENGESTIMASI JUMLAH PENDUDUK DI PROVINSI PAPUA Agus Firanto; Darsih Idayani
Jurnal Matematika Sains dan Teknologi Vol. 23 No. 2 (2022)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v23i2.5147.2022

Abstract

Population data is one of the absolute requirements that must be fulfilled by the Central Statistics Agency (Badan Pusat Statistik) to determine the grand design of development in the Province of Papua. However, the most complete and accurate source of population data comes from the results of a population census carried out every ten years. With long intervals and requiring costs, time, and effort, it will be more efficient and save time and effort if the number of residents can be estimated. Estimating the population required the proper method. Therefore, in this article, a comparison of Forward Newton-Gregory Polynomial Interpolation and Backward Newton-Gregory Polynomial Interpolation techniques is carried out to estimate the population of Papua Province. The estimated results of the two methods are compared by comparing the relative amount of error. The comparison results show that the relative error average of the Forward Newton-Gregory Polynomial Interpolation 0,014200679 is smaller than the relative error average of the Backward Newton-Gregory Polynomial Interpolation 0,047163677. So, it can be concluded that the Forward Newton-Gregory Polynomial Interpolation method is better than Backwards Newton-Gregory Polynomial Interpolation in predicting the population of Papua Province.
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.
The Application of Dicrete Wavelet Transform for Digital Image Compression Ahmad Khairul Umam; Pukky Tetralian Bantining Ngastiti; Aris Alfan; Zaqiyatus Shahadah; Amanda Fatma Muamalah
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 1 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i1.3955.2024

Abstract

This article explains Discrete Wavelet Transform (DWT) in image compression. Wavelet transform is a generalization of Fourier transform, consisting of discrete and continuous wavelet transform. DWT has many uses, including image compression, fingerprint recognition, and image denoising. This research aims to know the steps of digital image compression using DWT and compare the original and resulting images. Efforts of DWT in digital image compression go by DWT's process, determining the threshold, sorting the absolute value of the image whether it is minor or more significant (equal to) threshold value, then is processed, Inverse Discrete Wavelet Transform (IDWT). This research explains the Peak Signal-to-Noise Ratio (PSNR), computing time, and compression ratio for three examples: the image of the cameraman, Lena, and a cat. The results determine that the highest PSNR values are wavelet of coiflets 3 for the cameraman, biorthogonal 3.5 for Lena, and coiflets 3 for the cat. The fastest computation times are wavelet of symlets 4 for the cameraman, symlets 4, coiflets 3 for Lena, and Daubechies 4 for the cat. Then, the highest compression ratios are wavelet of symlets 4, biorthogonal 3.5, coiflets 3 for the cameraman, Haar for Lena, and symlets 4, biorthogonal 3.5 for the cat. The results of this research are we get steps of the discrete wavelet transform for digital image compression. Also, we obtain types of wavelets with the highest PSNR values, the fastest computation times, and the highest compression ratios.
Implementation of Random Oversampling Technique in the K-Nearest Neighbor Method for Creditworthiness Analysis Ayu Dhita Putri Wulandari; Shantika Martha; Wirda Andani
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 1 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i1.4909.2024

Abstract

Banks are financial institutions, one of whose main activities is providing credit to their customers. The existence of credit granting activities requires the bank to know the feasibility of prospective debtors in receiving credit. Because in practice, credit granting activities still often have bad credit problems. The problem of bad credit can be overcome by analyzing the feasibility of granting credit to prospective debtors. The data used in this study consists of 10 independent variables and 1 dependent variable is collectibility (kol). The collectibility (col) data consists of 500 data for the current debtor class and 26 data for the non-current debtor class, this indicates an imbalance class. So in this study, the application of the random oversampling (ROS) technique is used to overcome the imbalance class with the K-Nearest Neighbor (KNN) method in classifying current and non-current debtor data. ROS was chosen because it can generally provide better results and does not eliminate information from existing data. The analysis results obtained show that the use of the KNN method with the application of ROS is better than the KNN model without ROS, with an accuracy of 84.91% at data testing. The KNN model with ROS can improve the model's ability to classify noncurrent debtor data or the specificity value of the model increases by 25%. In the KNN model without ROS the model cannot classify non-current debtor data correctly at all, this can endanger the bank in making decisions.
Forecasting Number of Train Passengers Using Time Series Regression Integrated Calendar Variation and Covid 19 Intervention Mega Silfiani; Farida Nur Hayati
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 1 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i1.4941.2024

Abstract

The purpose of this study is to obtain a forecasting model for the number of train passengers using time series regression integrated with variations in the Islamic calendar and the effects of COVID 19. This study uses the number of train passengers in Jabodetabek, Java (Non-Jabodetabek), and Sumatra from January 2006 to December 2022 as the data source. Time series regression with variations of the Islamic calendar and the effects of COVID 19 for Jabodetabek, Java (non-Jabodetabek), and Sumatra has an RMSE value for each category of 7657,821; 2453.827 and 275.901. In general, the number of train passengers for all categories (Jabodetabek, Java, Sumatra) has a seasonality. In Jabodetabek and Sumatra, Eid al-Fitr has a big impact on the number of train passengers. Meanwhile, one month before Eid al-Fitr has a big impact on the number of train passengers in Java (Non Jabodetabek). In addition, the impact of COVID 19 significantly affected the number of train passengers for all categories.
Analysis of the Impact of Energy Consumption and Economic Performance on Carbon Dioxide Emissions in Indonesia Using Error Correction Mechanism Abigail Brenda Padhang Pasorong Randa; Arlita Dwina Firlana Sari; Emily Azizaida Budikusuma; Yuniar Yudhi Tirana; Nasrudin Nasrudin
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 2 (2024): September (in Progress)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i2.6879.2024

Abstract

Lately, Indonesia has been intensively developing its domestic economy. Industrial development began to be started, which attracted investors to invest. However, this massive economic development is causing an increase in CO2 emissions. This study intends to capture the effects of primary energy consumption per capita and economic performance represented by Gross Domestic Product (GDP), Foreign Direct Investment (FDI), and International Trade Openness on CO2 emissions in Indonesia from 1990–2022, in the short-run and long-run, using Error Correction Mechanisms (ECMs) analysis. In the long-run, energy consumption and GDP significantly affect CO2 emissions in Indonesia. Meanwhile, in the short-run, only energy consumption and Error Correction Term (ECT) have a significant effect on CO2 emissions. Moreover, from the ECT coefficient, it is known that the speed of adjustment to return to equilibrium is 95.08% in the first year after the shock.

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