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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI
Drajat Indra Purnama
Badan Pusat Statistik Kabupaten Parigi Moutong, Sulawesi Tengah
Mathematics

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Support vector regression (SVR) model for forecasting number of passengers on domestic flights at Sultan Hasanudin airport Makassar Drajat Indra Purnama; Siti Setianingsih
Jurnal Matematika, Statistika dan Komputasi Vol. 16 No. 3 (2020): JMSK, MAY, 2020
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v16i3.9176

Abstract

Sultan Hasanudin Airport is one of the largest airports in Indonesia, located in Makassar City. Its strategic location is the entrance of eastern Indonesia because it is a transit airport to other eastern regions of Indonesia. The number of airplane passengers at Sultan Hasanudin Airport has increased and decreased each time depending on certain moments. The increase in the number of passengers is closely related to the moments of religious holidays or year-end holidays. Whereas the decrease in the number of passengers was greatly influenced by the policy of rising plane ticket prices some time ago. Estimated number of passengers every month is needed in planning and making appropriate decisions from the government relating to fluctuations in the number of domestic flight passengers at Sultan Hasanudin Airport. Therefore, accurate forecasting techniques are needed to predict the number of passengers in the future. Because the data pattern of domestic flight passengers at Sultan Hasanudin Airport is not stationary, the ARIMA model can be used. However, the data on the number of passengers has a nonlinear data pattern, so we need a method that can overcome these problems. In this study the SVR model is used to overcome nonlinear patterns in the data. Compared to the ARIMA model, SVR has the advantage because it does not require stationary data assumptions as in ARIMA. The results of forecasting data on the number of domestic flight passengers at Sultan Hasanudin Airport using SVR show better accuracy or accuracy compared to the ARIMA model because it has a smaller MAPE value.
Comparison of Zero Inflated Poisson (ZIP) Regression, Zero Inflated Negative Binomial Regression (ZINB) and Binomial Negative Hurdle Regression (HNB) to Model Daily Cigarette Consumption Data for Adult Population in Indonesia Drajat Indra Purnama
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 3 (2021): May, 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v17i3.12278

Abstract

Smoking is a habit that is not good for health. Smoking habits are generally practiced by adults but it is possible for teenagers to do so.The Report of Southeast Asia Tobacco Control Alliance (SEATCA) entitled The Tobacco Control Atlas, ASEAN Region shows that Indonesia is the country with the highest number of smokers in ASEAN, namely 65.19 million people. This figure is equivalent to 34 percent of the total population of Indonesia in 2016. Based on these data, the authors are interested in modeling the daily cigarette consumption data for adults in Indonesia obtained from the 2015 Indonesia Family Life Survey. The variables used include the variable amount of cigarette consumption, education, level of welfare and income per month. The author wants to compare the best model that can be used to model the daily cigarette consumption of adults in Indonesia. The models being compared are Zero Inflated Poisson Regression (ZIP), Zero Inflated Negative Binomial Regression (ZINB) and Binomial Negative Hurdle Regression (HNB). The comparison results of the three models obtained that the best model is the Zero Inflated Negative Binomial (ZINB) Regression model because it has the smallest Akaike's Information Criterion (AIC) value.
Co-Authors Siti Setianingsih