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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 
Forecasting Palm Oil Production in North Sumatera Using the Adaptive Neuro Fuzzy Inference System Method Sari, Riezky Purnama; Hidayati, Adinda Tri; Fairus, Fairus
Pattimura International Journal of Mathematics (PIJMath) Vol 4 No 1 (2025): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol4iss1pp1-6

Abstract

Indonesia is an agricultural and maritime country because it is the country that has the largest agriculture and plantations in ASEAN. One of them is palm oil production, because palm oil is believed to not only be able to produce various types of butter, cooking oil or soap, but can also be a substitute for fuel oil. In the province of North Sumatra itself, oil palm is a crop that has potential and produces very high profits. Therefore, forecasting is used to determine future palm oil production results using the ANFIS method in order to increase or catalyze palm fruit. The data source used in this research comes from the Central Statistics Agency (BPS) of North Sumatra. The aim of this research is to determine the results of forecasting palm oil production in North Sumatra using the ANFIS model. So we got results from forecasting palm oil production in North Sumatra which experienced fluctuations throughout the period January 2023 to December 2024 with a forecasting accuracy level of 92% and a MAPE value of 12.778179% with MAPE criteria of 10% - 20% which was considered 'Good '. So it can be concluded that the forecasting results were carried out well and can be used for future forecasting.
Monte Carlo-Expected Tail Loss for Analyzing Risk of Commodity Futures Based on Holt-Winters Model Saputra, Wisnowan Hendy
Pattimura International Journal of Mathematics (PIJMath) Vol 4 No 1 (2025): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol4iss1pp7-16

Abstract

Future, an agreement to buy or sell an asset at a certain price and a certain time in the future, is one of the market derivatives because the underlying assets influence the price of futures. In general, futures divide into financial futures and commodity futures. Each of the futures has different risks, so risk measures are needed to improve the effectiveness and efficiency of investment management. For example, we have the London Metal Exchange (LME) in the metal scope of commodity futures. Therefore, we propose the Holt-Winters Model for estimating commodity prices in this study. Hereafter, The Expected Tail Loss (ETL) with Monte Carlo process will use to analyze risk measures. We took six commodity futures in LME to implement the method as a sample, such as Zinc, Lead, Aluminum, Copper, Nickel, and Tin. Based on the analysis, each commodity has a different mean ETL value, where Nickel has the most significant risk with an ETL value of 0.036. This value means that the possibility of the expected loss to be borne by investors is 3.6%.
Classification of Poverty in Maluku Province using SMOTE-Random Forest Algorithm Damamain, Ferina L; Sinay, Lexy Janzen; Latupeirissa, Sanlly J; Bakarbessy, Lusye
Pattimura International Journal of Mathematics (PIJMath) Vol 4 No 1 (2025): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol4iss1pp17-28

Abstract

Poverty is a complex issue. According to BPS publications, in 2023, the poverty line in Indonesia has reached 9.57%. Maluku is one of the provinces with a high poverty rate, reaching 16.23%. This research aims to classify poverty status in Maluku Province using the SMOTE-random forest algorithm. This research uses SUSENAS 2022 data, where the data is not balanced. SMOTE is used to overcome this problem. The best model obtained has an accuracy rate of 85.8%. The model is based on a training data proportion of 75% and testing 25%, with parameters m=4 and r=100. The critical factor that influences poverty status in Maluku Province is the number of households.
Damped Trend Exponential Smoothing and Holt-Winters in Forecasting the Number of Airplane Passengers at Kualanamu Airport Binoto, Rustham Michael; Sudarwanto, Sudarwanto; Santi, Vera Maya
Pattimura International Journal of Mathematics (PIJMath) Vol 4 No 1 (2025): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol4iss1pp29-40

Abstract

Airplanes are one of the most frequently chosen modes of transportation by Indonesians today. Kualanamu Airport is one of the busiest airports in terms of the number of passengers. The number of airplane passengers often fluctuates, increasing and decreasing, so an analysis method is required to predict the number of passengers. This study uses the Double Exponential Smoothing Damped Trend and Multiplicative Holt-Winters models. The number of Kualanamu Airport domestic airplane passengers from January 2006 to December 2023 was used as research data. The best model is then used to forecast the number of Kualanamu Airport domestic airplane passengers for 12 periods from the last data used. The results showed that the Multiplicative Holt-Winters model with smoothing parameters and obtained smaller (Mean Absolute Error) MAE and (Mean Square Error) MSE values of 21415.556 and 961525264.508, compared to the Double Exponential Smoothing Damped Trend model with smoothing parameters,, and which obtained MAE and MSE values of 23612.461 and 1061042411.507 in predicting the number of domestic aircraft passengers at Kualanamu Airport. Forecasting accuracy for the next 12 periods using Holt-Winters Exponential Smoothing produces a MAPE value of 9.2%. It shows the accuracy of forecasting in the very good category.
Factor Analysis on Poverty in Kalimantan Island with Geographically Weighted Negative Binomial Regression Halim, Alvin Octavianus; Satyahadewi, Neva; Preatin, Preatin
Pattimura International Journal of Mathematics (PIJMath) Vol 4 No 1 (2025): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol4iss1pp41-52

Abstract

Poverty is one of the problems still faced by Indonesia. The problem of poverty is a development priority because poverty is a complex and multidimensional problem. Therefore, to reduce poverty, it is necessary to know the factors that influence the number of people living in poverty. The influencing factors in each region are different due to the effects of spatial heterogeneity between regions such as geographical, economic, and socio-cultural conditions. This research considers spatial factors by using the Geographically Weighted Negative Binomial Regression (GWNBR) method on poverty-based regions in Kalimantan Island. This research uses eleven independent variables. The weighting function used is the Adaptive gaussian kernel because the adaptive kernel can produce the number of weights that adjust to the distribution of observations. The stage starts with descriptive statistics and checking multicollinearity. Then proceed with the formation of Poisson Regression, because the data used is enumerated data. Then check for overdispersion. If overdispersion is detected where the variance is bigger than the mean, then Negative Binomial Regression is continued. After that, it is tested for the presence or absence of spatial heterogeneity. If there is, proceed to find the bandwidth and Euclidean distance. After that, the graphical weighting matrix is searched. Then proceed with GWNBR modeling. The results of the analysis show that there are seven significant variables, including the percentage of households with the main source of lighting is non-state electricity company (PLN), average monthly net income of informal workers, population density for every square kilometer, monthly per capita expense on food and non-food essentials, percentage of people who have a health complaint and do not treat it because there is no money and percentage of population 15 years and above who do not have a diploma. Based on the categories of significant variables, six groups were formed in 56 districts/cities in Kalimantan Island.

Page 4 of 4 | Total Record : 35

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