PREDIKSI PENETAPAN TARIF PENERBANGAN MENGGUNAKAN AUTO-ML DENGAN ALGORITMA RANDOM FOREST

Asriyanik

doi:10.69688/jikr.v2i1.19

Authors

Asriyanik Universitas Muhammadiyah Sukabumi Author

DOI:

https://doi.org/10.69688/jikr.v2i1.19

Keywords:

Machine learning, Algoritma Random Forest, AutoML, Prediksi Penetapan tarif, , Penerbangan

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 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.

References

B. M. Henrique, V. A. Sobreiro, and H. Kimura, “Stock price prediction using support vector regression on daily and up to the minute prices,” J. Financ. Data Sci., vol. 4, no. 3, pp. 183–201, 2018, doi: 10.1016/j.jfds.2018.04.003.

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.

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.

Z. Chen, C. Li, and W. Sun, “Bitcoin price prediction using machine learning: An approach to sample dimension engineering,” J. Comput. Appl. Math., vol. 365, 2020, doi: 10.1016/j.cam.2019.112395.

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, p. 012074, Jul. 2019, doi: 10.1088/1742-6596/1230/1/012074.

D. Sitanggang et al., “Diagnosing chicken diseases using fuzzy Tsukamoto web-based expert system,” IOP Conf. Ser. Mater. Sci. Eng., vol. 505, no. 1, p. 012086, Jul. 2019, doi: 10.1088/1757-899X/505/1/012086.

A. M. Husein, M. Harahap, S. Aisyah, W. Purba, and A. Muhazir, “The implementation of two stages clustering (k-means clustering and adaptive neuro fuzzy inference system) for prediction of medicine need based on medical data,” J. Phys. Conf. Ser., vol. 978, p. 12019, Mar. 2018, doi: 10.1088/1742-6596/978/1/012019.

W. Purba, S. Tamba, and J. Saragih, “The effect of mining data k-means clustering toward students profile model drop out potential,” J. Phys. Conf. Ser., vol. 1007, no. 1, p. 12049, 2018, [Online]. Available: http://stacks.iop.org/1742-6596/1007/i=1/a=012049

E. Saut Parsaoran Tamba, “Penerapan Data Mining Algoritma Apriori Dalam Menentukan Stok Bahan Baku Pada Restoran Nelayan,” JUSIKOM PRIMA (Junal Sist. Inf. Ilmu Komput. Prima), vol. 5, no. 2, pp. 97–102, 2022.

C. S. D. B. Sembiring, L. Hanum, and S. P. Tamba, “Penerapan Data Mining Menggunakan Algoritma K-Means Untuk Menentukan Judul Skripsi Dan Jurnal Penelitian (Studi Kasus Ftik Unpri),” J. Sist. Inf. dan Ilmu Komput. Prima(JUSIKOM PRIMA), vol. 5, no. 2, pp. 80–85, 2022, doi: 10.34012/jurnalsisteminformasidanilmukomputer.v5i2.2393.

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, pp. 254–265, 2020, doi: 10.28932/jutisi.v6i2.2672.

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.

A. Nurhopipah and U. Hasanah, “Dataset Splitting Techniques Comparison For Face Classification on CCTV Images,” vol. 14, no. 4, pp. 341–352, 2020.

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.

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.

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.

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.

M. Nawawi, “Klasifikasi Tingkat Popularitas Siswa Berdasarkan Aktifitas Komunikasi Siswa Menggunakan Smartphone dengan Teknik Logistic Regression,” vol. 4, no. 1, pp. 978–979, 2018.

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.

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, [Online]. Available: http://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/309%0Ahttp://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/download/309/239

S. P. Tamba and E. -, “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.