Analysis of Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) Algorithms to Predict the Number of Airplane Passengers at Makassar Sultan Hasanuddin International Airport : Systematic Literature Review
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Jun 30, 2025
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Abstract
This study compares the performance of Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), and hybrid techniques to forecast the number of aircraft passengers. This analysis was carried out utilizing the Systematic Literature Review (SLR) method and the PRISMA approach. Only 11 of the 44,564 items filtered during the initial round met the inclusion requirements. The LSTM model performed well in capturing time series patterns, however XGBoost was more robust when employed on data with noise and outliers. The hybrid model (LSTM + XGBoost) performed the best, with an average accuracy of 96%, RMSE of 0.015, and MAPE of 2.45%. This demonstrates that the hybrid technique is quite good in predicting the number of airplane passengers, particularly for complicated, dynamic, and seasonal time series data. These findings are recommended for the development of machine learning-based prediction systems for airports.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Ainul Idham, & Efy Yosrita. (2025). Analysis of Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) Algorithms to Predict the Number of Airplane Passengers at Makassar Sultan Hasanuddin International Airport : Systematic Literature Review. Jurnal E-Komtek, 9(1), 74-88. https://doi.org/10.37339/e-komtek.v9i1.2298
References
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[2] M. S. Y. Lubis, "Implementasi Artificial Intelligence Pada Sistem Manufaktur Terpadu," 2021.
[3] A. Soy Temür, M. Akgün, and G. Temür, "Predicting housing sales in Turkey using ARIMA, LSTM and hybrid models," J. Bus. Econ. Manag., vol. 20, no. 5, pp. 920–938, 2019, doi: 10.3846/jbem.2019.10190.
[4] R. Primartha, Belajar Machine Learning. Bandung: Informatika, 2018.
[5] M. D. Wuryandari and I. A., "Perbandingan Metode Jaringan Syaraf Tiruan Backpropagation Dan Learning Vector Quantization Pada Pengenalan Wajah," Jurnal Komputer dan Informatika (KOMPUTA), vol. 1, no. 1, 2012.
[6] R. Wijayanti, "Analisa Klasifikasi Kartu Kredit Menggunakan Algoritma Naive Bayes," Jurnal Mantik Penusa, vol. 10, no. 2, pp. 80–86, 2018.
[7] H. A. Sunata, F. J. Rianto, and Y., "K-Means Clustering dan Principal Component Analysis (PCA) Dalam Radial Basis Function Neural Network: Komparasi Tujuh Algoritma Identifikasi Fraud ATM Pada PT. Bank Central Asia Tbk," Jurnal XYZ, vol. 7, no. 3, pp. 441–450, 2020.
[8] S. Li and X. Zhang, "Research On Orthopedic Auxiliary Classification and Prediction Model Based On XGBoost Algorithm," Neural Computing And Applications, vol. 32, no. 7, pp. 1971–1979, 2020.
[9] N. N. et al., "Prediksi Kecelakaan Lalu Lintas Di Bali Dengan XGBoost Pada Python," Jurnal XYZ, vol. 8, no. 3, pp. 188–196, 2020.
[10] I. Hanif, "Implementing Extreme Gradient Boosting (XGBoost) Classifier To Improve Customer Churn Prediction," 2020.
[11] U. Gunter and B. Zekan, "Forecasting air passenger numbers with a GVAR model," Annals of Tourism Research, vol. 103, 2021, doi: 10.1016/j.annals.2021.103252.
[12] F. Nourzadeh, S. Ebrahimnejad, K. Khalili-Damghani, and A. Hafezalkotob, "Forecasting the international air passengers of Iran using an artificial neural network (ANN)," International Journal of Industrial Systems Engineering, vol. 33, no. 1, pp. 1-20, 2020, doi: 10.1504/IJISE.2020.106089.
[13] D. Pitriyani and Y. Permanasari, "Prediksi Jumlah Penumpang Pesawat dengan Backpropagation Neural Network," Jurnal Rekayasa Mesin, vol. 2, no. 2, pp. 1-8, 2022, doi: 10.29313/jrm.v2i2.1327.
[14] H. Donny, R. Ahmad Asrofiq, Y. Yunefri, and R. Rahmaddeni, "Analisis Sentimen Ulasan Penumpang Maskapai Penerbangan Di Indonesia Dengan Algoritma Random Forest Dan KNN," Jurnal Nasional Teknologi dan Sistem Informasi, vol. 6, no. 2, pp. 191–198, 2024, doi: 10.31849/zn.v6i2.19177.
[15] B. P. Dewani, S. Suhartono, and M. S. Akbar, "Peramalan Jumlah Penumpang dan Barang di Bandar Udara Internasional Juanda dan Pelabuhan Tanjung Perak Menggunakan Model Hybrid ARIMAX dan Deep Learning Neural Networks," Jurnal Teknologi Informasi, vol. 2, no. 1, pp. 45–55, 2019, doi: 10.12962/j27213862.v2i1.6805.
[16] D. I. Purnama and O. P. Hendarsin, "Peramalan Jumlah Penumpang Berangkat Melalui Transportasi Udara di Sulawesi Tengah Menggunakan Support Vector Regression (SVR)," Jurnal Optimasi, vol. 2, no. 2, pp. 445–456, 2020, doi: 10.34312/jjom.v2i2.4458.
[17] Y. A. Faeni, N. E. Pribadi, and J. B. R. Daba, "Prediksi Tingkat Kepenuhan Penumpang Pesawat dari Bandara Hang Nadim Batam," Jurnal Sistem Cerdas, vol. 2, no. 2, pp. 28–35, 2019, doi: 10.37396/jsc.v2i2.28.
[18] W. H. Ratoebandjoe, M. Kamisutara, and A. Muchayan, "Penerapan Data Mining Klasterisasi Data Penumpang Pesawat pada Bandara Indonesia Menggunakan Algoritma K-Means," Jurnal Ilmu Komputer dan Bisnis, vol. 15, no. 1, pp. 25–32, 2024, doi: 10.47927/jikb.v15i1.705.
[19] A. Ramadhan, R. Saputra, and K. K., "Prediksi Jumlah Penumpang Bandar Udara Halu Oleo Kendari Menggunakan Multi-layer Perceptron," Jurnal Informatika dan Teknik Komputer, vol. 4, no. 2, pp. 55–65, 2023, doi: 10.53682/jointer.v4i02.229.
[20] S. Sunardi, D. Dwiyanto, M. Sinambela, J. Jamaluddin, and D. Manalu, "Prediction of Domestic Passengers at Kualanamu International Airport Using Long Short Term Memory Network," Jurnal Informatika dan Rekayasa Sistem, vol. 4, no. 2, pp. 566–575, 2019, doi: 10.17605/JMEANS.V4I2.566.