Van Delsen, Marlon Stivo Noya
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Modelling Negative Binomial Regression to Resolve Overdispersion (Case Studi: The Number of Families at Risk of Stunting in Maluku Province in 2021) Salenussa, Rosalinda A.; Van Delsen, Marlon Stivo Noya; Haumahu, Gabriella
Tensor: Pure and Applied Mathematics Journal Vol 4 No 2 (2023): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol4iss2pp63-72

Abstract

Stunting is a condition of stunted growth in children due to some chronic malnutrition and is a serious problem that affects the health and development of children around the world. Maluku Province is one of the regions in Indonesia that also experiences significant stunting problems. Statistical methods that can be used to see the relationship between response variables and predictor variables are Regression analysis, one of which is Poisson regression. However, Poisson regression is not often able to meet the equidispersion assumption, so to overcome this problem, another alternative method is used, namely Negative Binomial regression. The research conducted was to produce the best Negative Binomial Regression model and identify factors that significantly affect stunting families in Maluku Province. This study produced the best Negative Binomial model, namely: with the smallest AIC value of 208.5 and able to correct overdispersion in the data. A significant influential factor in the Negative Binomial model is the age of the wife who is too old ( ) with a significance level of 5%.
Application of the Seasonal Autoregressive Integrated Moving Average (SARIMA) Method to Forecast the Number of Vessel Passenger Departures tt Yos Soedarso Ambon Port Butarbutar, Winda; Van Delsen, Marlon Stivo Noya; Djami, Ronald John
Tensor: Pure and Applied Mathematics Journal Vol 4 No 2 (2023): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol4iss2pp105-118

Abstract

Maluku Province is a fairly large archipelago in Indonesia. The large number of islands which are the administrative areas of Maluku Province, encourages the creation of a supporting transportation system. Yos Soedarso Ambon Port is the largest port in Ambon. Based on the statistics of Indonesia, the number of ship passengers at Yos Soedarso Port in Ambon during the April 2023 period experienced an increase of 44.97 percent, while in the March 2023 period, it only experienced an increase of 42.94 percent. Because the data used is time series data and has a seasonal pattern, the most appropriate method for predicting the number of passengers is the Seasonal Autoregressive Moving Average (SARIMA) method. The SARIMA method is an approach model developed from the Autoregressive Moving Average (ARIMA) model used on time series or data with a seasonal pattern. This research produced the best model for forecasting the number of departures of ship passengers at Yos Soedarso Port, Ambon. With an MSE value of 26.44. with the shape of the model with MAPE 10.23%.