Hidayat, Rani Aulia (2018) Prediksi Jumlah Penumpang di Bandar Udara Juanda Menggunakan Metode Support Vector Regression dengan Particle Swarm Optimization. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Jumlah penerbangan yang ada di suatu bandar udara terus meningkat. Hal tersebut mendukung pertumbuhan jumlah penumpang yang menjadi salah satu parameter bagi pihak manajemen bandar udara untuk menentukan kapan dan apa saja yang harus disiapkan. Dengan mengetahui jumlah penumpang setiap bulannya, pihak manajemen bandar udara akan lebih mudah untuk melakukan perencanaan jangka pendek. Tujuan dari tugas akhir ini adalah membangun program yang dapat memprediksi jumlah penumpang di bandar udara setiap bulan menggunakan data historis penumpang pada bulan-bulan sebelumnya, Dalam penelitian ini, data terbagi ke dalam empat skenario data yaitu data jumlah penumpang pada keberangkatan, kedatangan, transit, dan total. Data-data tersebut diprediksi menggunakan metode Support Vector Regression dengan Particle Swarm Optimization (SVR-PSO) di mana hasilnya dibandingkan dengan metode Support Vector Regression (SVR), Support Vector Regression dengan Genetic Algorithm (SVR-GA), serta Moving Average. Saat menggunakan SVR-PSO, MAPE yang didapatkan dari hasil prediksi data keberangkatan, kedatangan, transit, dan total secara berurutan adalah 6,6696%; 7,3784%; 11,6187%; dan 6,2559%. SVR-PSO terbukti lebih efektif untuk memprediksi data keberangkatan, kedatangan, transit, dan total dibandingkan dengan metode SVR dan SVR-GA. Namun, metode Moving Average berkerja lebih baik dibandingkan dengan metode SVR, SVR-GA, dan SVR-PSO untuk data kedatangan, transit, dan total dengan MAPE pada data tersebut secara berurutan adalah 5,7870%; 8,5180%; dan 6,1210%. ==========================================================================================================
The number of flights in airport is keep growing. It supports the increasing of number of passengers which is one of the parameter for the airport management to determine when and what that they should prepare. By knowing the number of passengers in every month, it will make the airport management easier to make a shortterm plan. The purpose of this final project is to build a program which could predict the monthly number of passengers in airport using the historical data of number of passengers in previous months. There are four data scenarios in this final project which are data of number of passenger in departure, arrival, transit, and total. Those datas predicted using the combination of Support Vector Regression and Particle Swarm Optimization (SVR-PSO) and the result is being compared with Support Vector Regression (SVR), Support Vector Regression with Genetic Algorithm (SVR-GA), and Moving Average. The MAPE of SVR-PSO in predicting the number of passengers in departure, arrival, transit, and total sequentially are 6,6696%; 7,3784%; 11,61867%; and 6,2559%. In this case, SVR-PSO is more effective to predict the number of passengers than SVR and SVR-GA. But Moving Average works better than SVR, SVR-GA, and SVR-PSO in predicting the number of passengers in arrival, transit, and total with MAPE for those datas respectively are 5,7870%; 8,5180%; and 6,1210%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSIf 006.3 Hid p-1 3100018075487 |
| Uncontrolled Keywords: | bandar udara; particle swarm optimization; prediksi penumpang; support vector regression; airport; particle swarm optimization; prediction of number of passengers; support vector regression. |
| Subjects: | Q Science > Q Science (General) > Q337.3 Swarm intelligence Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression Q Science > QA Mathematics > QA336 Artificial Intelligence Q Science > QA Mathematics > QA76.9.D343 Data mining. Querying (Computer science) |
| Divisions: | Faculty of Information and Communication Technology > Informatics > 55201-(S1) Undergraduate Thesis |
| Depositing User: | Rani Aulia Hidayat |
| Date Deposited: | 24 Jul 2018 02:08 |
| Last Modified: | 07 Oct 2020 03:24 |
| URI: | http://repository.its.ac.id/id/eprint/52188 |
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