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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 35 Documents
 1   2   3   4 
Value at Risk Prediction for the GJR-GARCH Aggregation Model Nurhayati Nurhayati; Wiwin Apriani; Ariestha Widyastuty Bustan
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 Taufan Talib
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 Muhammad Yahya Matdoan; Wahyu Irianto
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 Umi Sari Rumata; Yopi Andry Lesnussa; Marlon Stivo Noya van Delzen
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 Anita Desiani; Adinda Ayu Lestari; M Al-Ariq; Ali Amran; Yuli Andriani
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.
On Local-Strong Rainbow Connection Numbers On Generalized Prism Graphs And Generalized Antiprism Graphs Eri Nugroho; Kiki Ariyanti Sugeng
Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 2 (2022): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (489.934 KB) | DOI: 10.30598/pijmathvol1iss2pp43-58

Abstract

Rainbow geodesic is the shortest path that connects two different vertices in graph such that every edge of the path has different colors. The strong rainbow connection number of a graph G, denoted by src(G), is the smallest number of colors required to color the edges of G such that there is a rainbow geodesic for each pair of vertices. The d-local strong rainbow connection number, denoted by lrscd, is the smallest number of colors required to color the edges of G such that any pair of vertices with a maximum distance d is connected by a rainbow geodesic. This paper contains some results of lrscd of generalized prism graphs (PmxCn) and generalized antiprism graphs for values of d=2, d=3, and d=4.
Zero Inflated Poisson Regression Analysis in Maternal Death Cases on Java Island Vera Maya Santi; Defina Ambarwati; Bagus Sumargo
Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 2 (2022): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (442.104 KB) | DOI: 10.30598/pijmathvol1iss2pp59-68

Abstract

The basic regression model used to analyze the count data is the Poisson regression.. However, applying the Poisson regression model is unsuitable for excess zero data because it can cause overdispersion where the variance data is greater than its mean. One of the developments of the Poisson regression model can overcome this condition, Zero Inflated Poisson Regression (ZIP). In the health sector, the death of pregnant women on the Java island is an event that still rarely occurs and forms an excess zero data structure. However, the analysis of cases of maternal mortality using ZIP regression has never been studied in more depth. In this article, the maternal mortality cases in Java were modelled using ZIP regression to specify the variables that had a significant effect. The initial analysis results indicated the occurrence of overdispersion due to excess zero where there are 52% zero values in the data. The ZIP regression applied in this research provides enhancements to the Poisson regression based on the Vuong test. The results showed that the variables that had a significant effect on the maternal death cases in Java in the count model are the percentage of maternal health service coverage and the percentage of coverage of postpartum visit coverage, while in the zero-inflation model, the percentage of deliveries in health facilities and the percentage of obstetric complications treatment
Correspondence Analysis to Know Factors Related to the Use of Reducant Herbicide on Pagaralam Coffee Farmers Irmeilyana Irmeilyana; Ngudiantoro Ngudiantoro; Sri Indra Maiyanti; Indrike Febriyanti
Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 2 (2022): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (593.936 KB) | DOI: 10.30598/pijmathvol1iss2pp69-80

Abstract

Weed control is an attempt to care for agricultural land that can affect coffee production. This study aims to analyze the factors that have a relationship with the use of reductant herbicide in Pagaralam coffee farmers by using simple correspondence analysis. The research data included 19 variables and 3 categories of respondents based on the use of reductant herbicide, namely non-users, new users, and users. At the initial stage, each variable was carried out a mean difference test between 2 categories of respondents. Furthermore, each variable is divided into several categories. Then, by using the independence test, the categories of each variable are associated with the category of reductant use. There are 7 factors that have a relationship with the use of reductants, namely education of respondents, age of trees, length of harvest, frequency of herbicide use, frequency of chemical fertilizers used, frequency of organic fertilizers used, and number of labour outside the family (TL). The results of the correspondence analysis plot can show differences in the characteristics of the respondent's categories according to the use of reductant herbicide. The user category is dominantly characterized by having junior high school education, tree age more than 25 years, tend not to use organic fertilizer, and the harvest period can reach 3 months.
Unscented Kalman Filter and H-Infinity for Travel Company Stock Price Estimation Puspandam Katias; Ismanto Hadi Susanto; Teguh Herlambang; Mohamad Yusak Anshori
Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 2 (2022): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (414.728 KB) | DOI: 10.30598/pijmathvol1iss2pp81-88

Abstract

The travel and hotel industry is one of the industries experiencing rapid growth. As the population grows, the need for travel and accommodation services gets higher. This is one of the factors contributing a rapid increase in such service industry. Competition in the economy and business world is getting tougher from year to year both within a country and abroad. Considering that Indonesia is a country comprised of many islands with a variety of natural beauty, it has the very potential for tourist resort attraction. This kind of thing leads to the growth of the Hotel and Travel industry to support tourism development. With such rapid service industry development, supported by promising business opportunities, investors for such sector are encouraged. The right way to reduce risk for investors interested is to develop a system for estimating the stock prices. Therefore, in this study, the stock price estimation method applied for travel companies adopted Advanced Kalman Filter, a comparison of H-Infinify and Unscented Kalman Filter (UKF) as a chart for investors to take into consideration in their investment decision making. The simulation results showed that the UKF method had higher accuracy than the H-Infinify method with an error by the UKF of 3.2% and that by the H-Infinify of 9.6%.
Forecasting The Composite Stock Price Index Using Autoregressive Integrated Moving Average Hybrid Model Artificial Neural Network Muhidin Jaariyah; Lexy Janzen Sinay; Norisca Lewaherilla; Yopi Andry Lesnussa
Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 2 (2022): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (491.178 KB) | DOI: 10.30598/pijmathvol1iss2pp89-100

Abstract

A stock index is a statistical measure that reflects the overall price movement of a group of stocks selected based on certain criteria and methodologies and evaluated regularly. JCI is included in the composite index, which is the Headline index. The Headline Index is an index that is used as the main reference to describe the performance of the capital market. The JCI is very important in describing the current condition of the capital market because the JCI measures the price performance of all stocks listed on the Main Board and Development Board of the IDX. This study aims to predict JCI data using the time series method. The hybrid Autoregressive Integrated Moving Average–Artificial Neural Network (ARIMA-ANN) model combines the linear ARIMA model and the non-linear ANN model. The best models are the ARIMA model (2,1,1) and the ANN Backpropagation model with one input layer, one hidden layer with 20 neurons, and one output. The ARIMA-ANN hybrid model accurately predicts JCI data because it produces a MAPE value of less than 1%, with the level of forecasting accuracy from testing results being smaller than the level of accuracy during training. In addition, the forecast for the next five days is very accurate because it produces a very small RMSE and a MAPE below 1%, respectively, namely 56.99 and 0.72%.

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