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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 2 (2022): Pattimura International Journal of Mathematics (PIJMath)" : 5 Documents clear
On Local-Strong Rainbow Connection Numbers On Generalized Prism Graphs And Generalized Antiprism Graphs Nugroho, Eri; Sugeng, Kiki Ariyanti
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 Santi, Vera Maya; Ambarwati, Defina; Sumargo, Bagus
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; Maiyanti, Sri Indra; Febriyanti, Indrike
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 Katias, Puspandam; Susanto, Ismanto Hadi; Herlambang, Teguh; Anshori, Mohamad Yusak
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 Jaariyah, Muhidin; Sinay, Lexy Janzen; Lewaherilla, Norisca; Lesnussa, Yopi Andry
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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