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Pattimura International Journal of Mathematics (PIJMath)
Published by Universitas Pattimura
ISSN : -     EISSN : 28306791     DOI : https://doi.org/10.30598/pijmathvol1iss2year2022
Core Subject : Education,
Mathematics
Pattimura International Journal of Mathematics (PIJMath) is provided for writers, teachers, students, professors, and researchers, who will publish their research reports about mathematics and its is applications. Start from June 2022, this journal publishes two times a year, in May and November
Arjuna Subject : Matematika - Matematika Terapan
Articles 5 Documents
 1 
Search results for , issue "Vol 1 No 1 (2022): Pattimura International Journal of Mathematics (PIJMath)" : 5 Documents clear
Value at Risk Prediction for the GJR-GARCH Aggregation Model Nurhayati, Nurhayati; Apriani, Wiwin; Bustan, Ariestha Widyastuty
Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 1 (2022): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (470.401 KB) | DOI: 10.30598/pijmathvol1iss1pp01-06

Abstract

Volatility is the level of risk faced due to price fluctuations. The greater the volatility brings, the greater the risk. We need a measure such as Value at Risk (VaR) and volatility modeling to overcome this. The most frequently used volatility model in the financial sector is GARCH. However, this model is still unable to accommodate the asymmetric nature, so the GJR-GARCH model was developed. In addition, this study also used aggregation returns with two assets in them. This study aimed to determine the VaR prediction for the GJR-GARCH(1.1) aggregation model and its comparison with the GARCH(1.1) aggregation model. The results obtained indicate that the prediction of volatility using the GJR-GARCH(1.1) aggregation model is more accurate than the GACRH(1.1) aggregation model because it has a correct VaR value that is close to the given confidence level.
Modeling Biodegradation of Polyethylene Terephthalate Involving the Growth of Factor Escherichia Coli Bacteria Talib, Taufan
Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 1 (2022): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (604.926 KB) | DOI: 10.30598/pijmathvol1iss1pp07-16

Abstract

A design of a Polyethylene Terephthalate (PET) waste biodegradation system using Escherichia Coli (E-Coli) bacteria in a whole-cell biocatalyst system. E-Coli bacteria will produce LC-Cutinase enzyme on the cell surface so PET can be broken down into Ethylene glycol and Terephthalate acid. With the help of Reductase and Dehydrogenase enzymes, a chemical reaction occurs that converts Ethylene glycol into Malate. Through the chemical reaction process, it is guaranteed that Ethylene glycol does not explain the environment but can be used as an energy source for the growth of E-Coli bacteria. Thus E-Coli can grow faster, so the more bacteria, the more PET that can be broken down quickly.
Analysis of Online Learning During the Covid-19 Period using the Ordinary Least Square (OLS) Method Matdoan, Muhammad Yahya; Irianto, Wahyu
Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 1 (2022): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (423.106 KB) | DOI: 10.30598/pijmathvol1iss1pp17-26

Abstract

Education is the main capital to create quality human resources to face the challenges of the times that continue to develop rapidly. At the end of 2019, Indonesia experienced an outbreak of the COVID-19 pandemic. It makes learning activities carried out from home or online. Online learning aims to meet educational standards by utilizing information technology using a computer or mobile devices that are interconnected between students and teachers via an internet connection. However, there are several problems in the online learning process, both among teachers and students, as well as the facilities used in learning. The method of least squares (Ordinary Least Square) is one of the estimation methods used to analyze the relationship between the predictor variable (independent variable) and the response variable (dependent variable). This study aimed to identify and analyze online learning problems during the covid-19 pandemic. The data used in this study were sourced from Public Senior High School 11 Ambon, with the variables used being learning motivation (X1), teacher's role (X2), and student learning outcomes (Y). This study concluded that student learning outcomes in online learning at Public Senior High School 11 Ambon were in a low category, student motivation in learning is in the moderate category, and the role of the teacher is in the moderate category. In addition, the variables of learning motivation and the role of the teacher together have a positive effect on student learning outcomes. The amount of the contribution of the influence of learning motivation and the role of the teacher to student learning outcomes at Public Senior High School 11 Ambon is 61.9%, while other factors outside the study influence the remaining 38.1%.
Analysis of the Increase in Covid-19 Patients in Maluku Province Using Markov Chain Method Rumata, Umi Sari; Lesnussa, Yopi Andry; Noya van Delzen, Marlon Stivo
Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 1 (2022): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (374.276 KB) | DOI: 10.30598/pijmathvol1iss1pp27-32

Abstract

Since the beginning of 2020, most of the world has been hit by the covid-19 pandemic, patients who are confirmed to be covid-19 are increasing day by day until the end of 2020, this increase in covid-19 patients has also occurred in the province of Maluku. In this study using the Markov chain method to analyze the increase in COVID-19 patients in the Maluku province, the results of the study were obtained that there were 5 ranges of adding positive COVID-19 patients with the opportunity value of increasing in each range as follows In the range of 0-20 people, the opportunity value is 0.103, the range of 21-40 people, is the opportunity value 0.098, the range 41-60 people is the opportunity value is 0.093, the range 61-80 people is the opportunity value is 0.1, the range of more than 81 people the opportunity value is 0.74.
Comparison of Support Vector Machine and K-Nearest Neighbors in Breast Cancer Classification Desiani, Anita; Lestari, Adinda Ayu; Al-Ariq, M; Amran, Ali; Andriani, Yuli
Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 1 (2022): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (470.745 KB) | DOI: 10.30598/pijmathvol1iss1pp33-42

Abstract

Cancer is one of the leading causes of death, and breast cancer is the second leading cause of cancer death in women. One method to realize the level of malignancy of breast cancer from an early age is by classifying the cancer malignancy using data mining. One of the widely used data mining methods with a good level of accuracy is the Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). Evaluation techniques of percentage split and cross-validation were used to evaluate and compare the SVM and KNN classification models. The result was that the accuracy level of the SVM classification method was better than the KNN classification method when using the cross-validation technique, which is 95,7081%. Meanwhile, the KNN classification method was better than the SVM classification method when using the percentage split technique, which is 95,4220%. From the comparison results, it can be seen that the KNN and SVM methods work well in the classification of breast cancer.

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