PREDIKSI PENETAPAN TARIF PENERBANGAN MENGGUNAKAN AUTO-ML DENGAN ALGORITMA RANDOM FOREST
Abstract
With so many airlines competing with each other, airlines are competing to become the consumer/market's main choice, but to achieve this, there is no airline strategy that can predict the price of airline tickets according to market needs. To meet the needs of airlines, we need a way to determine the price of airline tickets according to market needs with the help of the influence of technology and information. This research method was carried out using Google Collaboratory as a media to create a data model automated machine learning (AutoML) with the Random Forest, Logistic Regression and Gradient Boosting Regressor algorithms. In this study, the model that produced the highest R2 value and the lowest RMSE was a random forest with an R2 value of 83.91% and an RMSE of $175.9. However, from the three models, Random Forest got a change in accuracy of 1.96% to 85.87. To assist in predicting the determination of flight fares, airline companies can more easily and be alert to determine flight fares that are in accordance with the market. Therefore, Random Forest can be declared better than Logistic Regression and Gradient Boosting models. The Random Forest model that has been created can be used to predict in real-time using Machine Learning.
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[2] P. Yu and X. Yan, “Stock price prediction based on deep neural networks,” Neural Comput. Appl., vol. 32, no. 6, pp. 1609–1628, 2020, doi: 10.1007/s00521-019-04212-x.
[3] W. Purba, W. Siawin, and . H., “Implementasi Data Mining Untuk Pengelompokkan Dan Prediksi Karyawan Yang Berpotensi Phk Dengan Algoritma K-Means Clustering,” J. Sist. Inf. dan Ilmu Komput. Prima(JUSIKOM PRIMA), vol. 2, no. 2, pp. 85–90, 2019, doi: 10.34012/jusikom.v2i2.429.
[4] Edric and S. P. Tamba, “Prediksi Penyakit Gagal Jantung Dengan Menggunakan Random Forest,” J. Sist. Inf. dan Ilmu Komput. Prima(JUSIKOM PRIMA), vol. 5, no. 2, pp. 176–181, 2022, doi: 10.34012/jurnalsisteminformasidanilmukomputer.v5i2.2445.
[5] P. P. Putra and A. S. Chan, “Pengembangan Aplikasi Perhitungan Prediksi Stock Motor Menggunakan Algoritma C 4.5 Sebagai Bagian dari Sistem Pengambilan Keputusan (Studi Kasus di Saudara Motor),” INOVTEK Polbeng - Seri Inform., vol. 3, no. 1, p. 24, 2018, doi: 10.35314/isi.v3i1.296.
[6] C. Nas, “Data Mining Prediksi Minat Calon Mahasiswa Memilih Perguruan Tinggi Menggunakan Algoritma C4.5,” J. Manaj. Inform., vol. 11, no. 2, pp. 131–145, 2021, doi: 10.34010/jamika.v11i2.5506.
[7] G. I. Merdekawati and Ismail, “Prediksi Curah Hujan Di Jakarta Berbasis Algoritma Levenberg Marquardt,” J. Ilm. Inform. Komput., vol. 24, no. 2, pp. 116–128, 2019, doi: 10.35760/ik.2019.v24i2.2366.
[8] U. Inyaem, “Construction Model Using Machine Learning Techniques for the Prediction of Rice Produce for Farmers,” 2018 3rd IEEE Int. Conf. Image, Vis. Comput. ICIVC 2018, pp. 870–874, 2018, doi: 10.1109/ICIVC.2018.8492883.
[9] E. Manalu, F. A. Sianturi, and M. R. Manalu, “Penerapan Algoritma Naive Bayes Untuk Memprediksi Jumlah Produksi Barang Berdasarkan Data Persediaan dan Jumlah Pemesanan Pada CV. Papadan Mama Pastries,” J. Mantik Penusa, vol. 1, no. 2, pp. 16–21, 2017, [Online]. Available: https://ezp.lib.unimelb.edu.au/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ffh&AN=2008-10-Aa4022&site=eds-live&scope=site
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[11] C. M. Sitorus, A. Rizal, and M. Jajuli, “Prediksi Risiko Perjalanan Transportasi Online Dari Data Telematik Menggunakan Algoritma Support Vector Machine,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 2, Aug. 2020, doi: 10.28932/jutisi.v6i2.2672.
[12] R. S. Oktavian and S. Budi, “Analisis Dataset Google Playstore Menggunakan Metode Exploratory Data Analysis,” J. Strateg. Maranatha, vol. 2, no. 2, pp. 636–649, 2020.
[13] A. Nurhopipah and U. Hasanah, “Dataset Splitting Techniques Comparison For Face Classification on CCTV Images,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 14, no. 4, p. 341, Oct. 2020, doi: 10.22146/ijccs.58092.
[14] S. P. Tamba, M. D. Batubara, W. Purba, M. Sihombing, V. M. Mulia Siregar, and J. Banjarnahor, “Book data grouping in libraries using the k-means clustering method,” J. Phys. Conf. Ser., vol. 1230, no. 1, p. 012074, Jul. 2019, doi: 10.1088/1742-6596/1230/1/012074.
[15] V. M. Mulia Siregar and H. Sugara, “Implementation of artificial neural network to assesment the lecturer‘s performance,” IOP Conf. Ser. Mater. Sci. Eng., vol. 420, no. 1, p. 012112, Oct. 2018, doi: 10.1088/1757-899X/420/1/012112.
[16] A. Ramdan, U. Siliwangi, N. Widyasono, U. Siliwangi, H. Mubarok, and U. Siliwangi, “Prediksi Jaringan TOR dan VPN menggunakan Algoritma K-Nearest Neighbour pada Trafik Darknet,” vol. 05, no. 01, pp. 21–35, 2022.
[17] N. G. Ramadhan, F. D. Adhinata, A. Jala, T. Segara, and D. Putra, “Deteksi Berita Palsu Menggunakan Metode Random Forest dan Logistic Regression,” vol. 9, no. 2, pp. 251–256, 2022, doi: 10.30865/jurikom.v9i2.3979.
[18] M. Nawawi, “Klasifikasi Tingkat Popularitas Siswa Berdasarkan Aktifitas Komunikasi Siswa Menggunakan Smartphone dengan Teknik Logistic Regression,” vol. 4, no. 1, pp. 978–979, 2018.
[19] E. N. Fauziyah and S. R. Nudin, “Sistem Pendukung Keputusan Penentuan Jurusan di SMKN 1 Pungging Menggunakan Gradient Boosting Tree,” vol. 3, pp. 42–50, 2021.
[20] S. P. Tamba, A. W. Tan, Y. Gunawan, and ..., “Penerapan Data Mining Untuk Pembuatan Paket Promosi Penjualan Menggunakan Kombinasi Fp-Tree Dan Tid-List,” … (Teknik Inf. dan …, vol. 4, 2021
Published
2022-06-30
How to Cite
ZEBUA, Yakub Anuyuta et al.
PREDIKSI PENETAPAN TARIF PENERBANGAN MENGGUNAKAN AUTO-ML DENGAN ALGORITMA RANDOM FOREST.
Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 5, n. 1, p. 115-122, june 2022.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/508>. Date accessed: 16 apr. 2024.
doi: https://doi.org/10.37600/tekinkom.v5i1.508.
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