Decision Support System for Aircraft Takeoff and Landing Using Mamdani Fuzzy Logic Based on Weather Parameters

Armansyah Armansyah; Suhendro Yusuf Irianto

doi:10.47065/bits.v7i1.7464

Armansyah Armansyah * Institut Informatika dan Bisnis Darmajaya, Bandar Lampung, Indonesia
Suhendro Yusuf Irianto Institut Informatika dan Bisnis Darmajaya, Bandar Lampung, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/bits.v7i1.7464

Keywords: Decision Support System; Flight Safety; Mamdani Fuzzy Logic; Takeoff and Landing Feasibility; Weather

Abstract

Aviation safety is highly influenced by weather conditions, particularly during take-off and landing, necessitating an accurate feasibility assessment. Traditional manual methods rely on subjective judgment, making them prone to inconsistencies and errors. This study proposes a decision support system utilizing Mamdani fuzzy logic to process real-time meteorological data from the Radin Inten II station and assess take-off and landing feasibility. The system evaluates key weather parameters, including wind speed, wind direction, visibility, precipitation, and cloud height. Testing 31 data samples from BMKG, the system achieved an accuracy of 96.77%, with 30 out of 31 outputs matching standard aviation criteria. These results indicate that the system significantly improves decision-making reliability. The Mamdani fuzzy logic approach proves effective in interpreting complex weather data and generating consistent, data-driven recommendations to support safe aircraft operations.

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References

K. Shirini, H. S. Aghdasi, and S. Saeedvand, “A Comprehensive Survey on Multiple-Runway Aircraft Landing Optimization Problem,” Int. J. Aeronaut. Space Sci., vol. 25, no. 4, pp. 1574–1602, Oct. 2024, doi: 10.1007/s42405-024-00747-z.

B.-S. Indonesia, “Air Transportation Statistics 2022.” Accessed: Jun. 30, 2025. [Online]. Available: https://www.bps.go.id/en/publication/2023/11/27/aec509f099371a2655c5ee0f/air-transportation-statistics-2022.html

Z. Sokol, J. Szturc, J. Orellana-Alvear, J. Popová, A. Jurczyk, and R. Célleri, “The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review,” Remote Sensing, vol. 13, no. 3, p. 351, Jan. 2021, doi: 10.3390/rs13030351.

G.-P. Chen, T.-S. Chen, and T.-C. Chen, “Obstacle Avoidance for Flight Safety of Unmanned Aerial Vehicles Using Deep Reinforcement Learning,” in 2024 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS), Singapore: IEEE, Aug. 2024, pp. 1–5. doi: 10.1109/apwcs61586.2024.10679316.

H. Liu, R. Xie, H. Qin, and Y. Li, “Research on dangerous flight weather prediction based on machine learning,” J. Phys.: Conf. Ser., vol. 2870, no. 1, p. 012020, Oct. 2024, doi: 10.1088/1742-6596/2870/1/012020.

I. Ostroumov and N. Kuzmenko, “Aviation Weather Data Processing with Spline Functions,” in 2021 IEEE 12th International Conference on Electronics and Information Technologies (ELIT), Lviv, Ukraine: IEEE, May 2021, pp. 67–70. doi: 10.1109/elit53502.2021.9501065.

“Permenhub No. 95 Tahun 2018.” Accessed: Jun. 30, 2025. [Online]. Available: https://peraturan.bpk.go.id/Details/102846/permenhub-no-95-tahun-2018?utm_source=chatgpt.com

E. Mangortey, O. J. Pinon-Fischer, T. G. Puranik, and D. N. Mavris, “Predicting The Occurrence of Weather And Volume Related Ground Delay Programs,” in AIAA Aviation 2019 Forum, American Institute of Aeronautics and Astronautics. doi: 10.2514/6.2019-3188.

J. Mihajlović, D. Burić, and M. Milenković, “Synoptic characteristics of an extreme weather event: The tornadic waterspout in Tivat (Montenegro), on June 9, 2018,” Geogr. Pol., vol. 94, no. 1, pp. 68–90, 2021, doi: 10.7163/GPol.0194.

Y. Ardi, S. Effendi, and E. B. Nababan, “Mamdani and Sugeno Fuzzy Performance Analysis on Rainfall Prediction,” Rand. Inter. Social Sci. J., vol. 2, no. 2, pp. 176–192, Apr. 2021, doi: 10.47175/rissj.v2i2.240.

N. Rajesh Mavani, C. Y. Lim, H. Hashim, N. Abd. Rahman, and J. Mohd Ali, “Fuzzy Mamdani based user-friendly interface for food preservatives determination,” Food and Bioproducts Processing, vol. 126, pp. 282–292, Mar. 2021, doi: 10.1016/j.fbp.2021.01.012.

I. Dagal, W. F. Mbasso, H. Ambe, B. Erol, and P. Jangir, “Adaptive Fuzzy Logic Control Framework for Aircraft Landing Gear Automation: Optimized Design, Real-Time Response, and Enhanced Safety,” Int. J. Aeronaut. Space Sci., Mar. 2025, doi: 10.1007/s42405-025-00922-w.

A. P. U. Siahaan, “Take Off and Landing Prediction Using Fuzzy Logic,” International Journal of Recent Trends in Engineering & Research (IJRTER), vol. 02, no. 12, 2016, doi: 10.31227/osf.io/2c538.

W. Pratiwi, A. Sofwan, and I. Setiawan, “Implementation of fuzzy logic method for automation of decision making of Boeing aircraft landing,” IJ-AI, vol. 10, no. 3, p. 545, Sep. 2021, doi: 10.11591/ijai.v10.i3.pp545-552.

Y.-J. Noh et al., “A Framework for Satellite-Based 3D Cloud Data: An Overview of the VIIRS Cloud Base Height Retrieval and User Engagement for Aviation Applications,” Remote Sensing, vol. 14, no. 21, p. 5524, Nov. 2022, doi: 10.3390/rs14215524.

I. Gultepe et al., “A Review of High Impact Weather for Aviation Meteorology,” Pure Appl. Geophys., vol. 176, no. 5, pp. 1869–1921, May 2019, doi: 10.1007/s00024-019-02168-6.

F. Elfaladonna and I. G. T. Isa, “Uji Efektivitas Metode Fuzzy Logic Mamdani pada Penerimaan Beasiswa Menggunakan Matlab,” SINTECH (Science and Information Technology) Journal, vol. 5, no. 1, Art. no. 1, Apr. 2022, doi: 10.31598/sintechjournal.v5i1.1043.

S. Kusumadewi, L. Rosita, and E. Gustri Wahyuni, “Fuzzy linear regression based on a hybrid of fuzzy C-means and the fuzzy inference system for predicting serum iron levels in patients with chronic kidney disease,” Expert Systems with Applications, vol. 227, p. 120314, Oct. 2023, doi: 10.1016/j.eswa.2023.120314.

S. N. Putri and D. R. S. Saputro, “Construction fuzzy logic with curve shoulder in inference system mamdani,” J. Phys.: Conf. Ser., vol. 1776, no. 1, p. 012060, Feb. 2021, doi: 10.1088/1742-6596/1776/1/012060.

H. Baomar and P. J. Bentley, “Autonomous landing and go-around of airliners under severe weather conditions using Artificial Neural Networks,” in 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS), Linkoping: IEEE, Oct. 2017, pp. 162–167. doi: 10.1109/RED-UAS.2017.8101661.

G. Selvachandran et al., “A New Design of Mamdani Complex Fuzzy Inference System for Multiattribute Decision Making Problems,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 4, pp. 716–730, Apr. 2021, doi: 10.1109/TFUZZ.2019.2961350.

E. Pourjavad and R. V. Mayorga, “A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system,” J Intell Manuf, vol. 30, no. 3, pp. 1085–1097, Mar. 2019, doi: 10.1007/s10845-017-1307-5.

D. Y. Darmawi, G. W. Nurcahyo, and S. Sumijan, “Fuzzy Sistem Fuzzy Menggunakan Metode Sugeno Dalam Akurasi Penentuan Suhu Kandang Ayam Pedaging,” jidt, vol. 3, no. 2, pp. 72–77, 2021, doi: 10.37034/jidt.v3i2.95.

R. Reynaldi, W. Syafrizal, and M. F. A. Hakim, “Analisis Perbandingan Akurasi Metode Fuzzy Tsukamoto dan Fuzzy Sugeno Dalam Prediksi Penentuan Harga Mobil Bekas,” Indonesian Journal of Mathematics and Natural Sciences, vol. 44, no. 2, Art. no. 2, Oct. 2021, doi: 10.15294/ijmns.v44i2.32967.

E. Setianingtyas, M. A. S. Yudono, and A. Erfina, “Pengambilan Keputusan Lepas Landas Pesawat Boeing Menggunakan Metode Logika Fuzzy Mamdani,” Metrik, vol. 16, no. 01, p. 32, Jun. 2023, doi: 10.26714/me.v16i01.10930.

Murugesan. G. et al., “Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease,” BioMed Research International, vol. 2022, no. 1, Jan. 2022, doi: 10.1155/2022/2653665.

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