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Abstract
The COVID-19 pandemic has succeeded in bringing down various industrial sectors, including the aviation industry. This pandemic impacted the depletion of the operational fleet. In 2022, the number of domestic aircraft ready for operation was only around 55%–60% compared to 2019. However, with all its limitations, the aviation industry must develop the best strategy to revive in this post-pandemic era. One of the strategies undertaken is selecting the most efficient and economical aircraft type to cut costs amid market uncertainty due to this pandemic. In this regard, research was carried out to predict when aviation activity in Indonesia would reach regular in January 2020 and the forecast for the dominance of aircraft types in domestic flight. Multilayer Perceptrons (MLP) show that domestic flight activities in Indonesia will reach the standard point in March 2024. From the forecast result, the error rate using MAPE is 0.52%. The aircraft that dominates Indonesia’s domestic flight activities during 2020–2022 is the Airbus 320 type. Meanwhile, for the next two years—2023 & 2024—it is predicted that the Airbus 320 type will continue to dominate the flights.
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References
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References
Biro Komunikasi dan Informasi Publik, Menuju Normalisasi Aktivitas Penerbangan di Bandara-Bandara Indonesia, Jakarta 2022.
L. A. Mahardhika, Mulai Pulih, Kemenhub Prediksi Penumpang Angkutan Udara Tembus 98,67 Juta 2023, Bisnis.com, Jakarta 2023.
H. Suropati, Peramalan Pasar Transportasi Udara Domestik Indonesia Pascapandemi Covid-19, Jurnal Transportasi 22(3) (2022) 227–236.
F. Rianda, Pemodelan Intervensi Untuk Menganalisis dan Meramalkan Jumlah Penumpang Pesawat di Bandara Soekarno-Hatta Akibat Pandemi Covid-19, Seminar Nasional Official Statistics 2021(1) (2021) 283–292.
J. H. Wilson and B. Keating, Business Forecasting with Accompanying Excel Based Forecast Xtm Software, McGraw-Hill Education 2002.
B. Zahra, M. Lazaar, H. Omara, and O. Mahboub, Effect of Architecture in Recurrent Neural Network Applied on the Prediction of Stock Price, IAENG Int. J. Comput. Sci. 47 (2020).
C. Jayaweera and N. Aziz, Development and comparison of Extreme Learning machine and multi-layer perceptron neural network models for predicting optimum coagulant dosage for water treatment, J Phys Conf Ser 1123(1) (2018).
T. Toharudin et al., Indonesia in Facing New Normal: An Evidence Hybrid Forecasting of COVID-19 Cases Using MLP, NNAR and ELM, Engineering Letters 29(2) (2021).
M.-C. Popescu, V. Balas, L. Perescu-Popescu, and N. Mastorakis, Multilayer perceptron, and neural networks, WSEAS Transactions on Circuits and Systems 8(7) (2009) 579–588.
B. Venkateswaran and G. Ciaburro, Neural Networks with R, Packt 2017.
A. de Myttenaere, B. Golden, B. Le Grand, and F. Rossi, Mean Absolute Percentage Error for regression models, Neurocomputing 192 (2016) 38–48.
C. D. Lewis, Industrial and Business Forecasting Methods, Butterworth-Heinemann 1982.
Nur, “Jokowi umumkan PPKM darurat di Jawa dan Bali 3-20 Juli 2021: Ini akan lebih ketat dari kebijakan pembatasan sebelumnya, BBC News Indonesia, Jakarta 2021.
J. Bailey, L. Ahlgren, and P. Pande, The Boeing 737 vs Airbus A320 - Which Plane Is Best?, Simple Flying, 2023.