Airport Clustering in Indonesia Based on Domestic Air Traffic Using the K-Means Algorithm

Mohammad Rohmad Nurkhoirofiq; Rossi Passarella; Osvari Arsalan

doi:10.33365/teknoinfo.v20i2.1808

Mohammad Rohmad Nurkhoirofiq Sriwijaya University
Rossi Passarella Informatics Engineering, Sriwijaya University, Indonesia https://orcid.org/0000-0002-7243-0451
Osvari Arsalan Informatics Engineering, Sriwijaya University, Indonesia https://orcid.org/0000-0003-3474-5812

DOI: https://doi.org/10.33365/teknoinfo.v20i2.1808

Keywords: Airport Clustering, K-Means, Air Transportation, Data Mining, Airport Activity

Abstract

Indonesia, as an archipelagic country, relies heavily on air transportation to support interregional connectivity. Airports play an important role in supporting community mobility, economic activities, and equitable regional development. However, the level of air transportation activity at each airport in Indonesia varies and may change over time. This study aims to cluster airports in Indonesia based on domestic air transportation activity using the K-Means algorithm. The dataset used in this study consists of historical data from the period 2015–2023, including aircraft movements, passenger traffic, baggage volume, cargo volume, and mail shipments obtained from the HUBNET system of the Ministry of Transportation. The analysis process includes exploratory data analysis, data preprocessing, feature selection, and clustering modeling using the K-Means algorithm. The number of clusters is determined using the Elbow and Silhouette methods, resulting in candidate values of K = 2, 3, and 5. The evaluation results show that K = 2 has the best quality with a Silhouette Score of 0.6254, a Calinski Harabasz Index of 3543.93, and a Davies Bouldin Index of 0.5938. However, K = 3 is chosen as it is more representative, consisting of 260 low-activity airports, 1063 medium, and 627 high. Temporal analysis shows 233 cluster transitions across 123 airports, with the highest increase in 2016 (31 airports) and the highest decrease in 2018 (23 airports). The results indicate that airport activity is dynamic and that clustering can be used as a basis for data-driven airport planning and evaluation.

References

Kementerian Perhubungan Republik Indonesia, Laporan Kinerja Kementerian Perhubungan. 2023. Accessed: Nov. 10, 2025. [Online]. Available: https://ppid.dephub.go.id/fileupload/informasi-setiap-saat/20241021185227.LKIP_Kemenhub_Tahun_2023.pdf

“Optimalisasi Pemanfaatan Transportasi Udara Bandara Aek Godang dan Bandara Jenderal Besar AH Nasution untuk di Sumatera Utara | Jurnal Informatika Ekonomi Bisnis.” Accessed: Jan. 10, 2026. [Online]. Available: https://infeb.org/index.php/infeb/article/view/1080

Badan Pusat Statistik, “Statistik Transportasi Udara 2023,” 2023, Accessed: Nov. 10, 2025. [Online]. Available: https://www.bps.go.id/id/publication/2024/11/25/a8fac4f872ffc88d3f45d0d3/statistik-transportasi-udara-2023.html

J. Pauwels, S. Buyle, and W. Dewulf, “Insights in diversity: A latent profile cluster analysis of regional airports in Western Europe,” Research in Transportation Business & Management, vol. 63, p. 101477, Dec. 2025, doi: 10.1016/J.RTBM.2025.101477.

L. Wan et al., “Life Cycle Prediction of Airport Operation Based on System Dynamics,” Sustainability 2024, Vol. 16, Page 9596, vol. 16, no. 21, p. 9596, Nov. 2024, doi: 10.3390/SU16219596.

L. E. C. Rocha, “Dynamics of air transport networks: A review from a complex systems perspective,” Chinese Journal of Aeronautics, vol. 30, no. 2, pp. 469–478, Apr. 2017, doi: 10.1016/J.CJA.2016.12.029.

R. Passarella, R. Febrianti, M. Vindriani, and M. S. Adenan, “A Clustering Approach to Enhancing Marine Transportation Services in the Thousand Islands Regency, Indonesia,” International Journal of Transport Development and Integration, vol. 9, no. 1, pp. 131–141, Mar. 2025, doi: 10.18280/ijtdi.090112.

S. Siti Syaoqiyah, Y. Selvina Yulianti, and N. Ayu Rahmadenti, “Classification of Domestic Flight Passengers at Main Airports Using the K-Means Clustering Method,” International Journal of Information System & Technology Akreditasi, vol. 8, no. 158, pp. 1–5, 2024, [Online]. Available: https://www.bps.go.id/

D. L. Trenggonowati, M. Ulfah, R. Ekawati, and V. A. Yusuf, “Organization clustering airports using K-Means clustering algorithm,” IOP Conf. Ser. Mater. Sci. Eng., vol. 673, no. 1, Dec. 2019, doi: 10.1088/1757-899X/673/1/012081.

Y. Gao, “What is the busiest time at an airport? Clustering U.S. hub airports based on passenger movements,” J. Transp. Geogr., vol. 90, p. 102931, Jan. 2021, doi: 10.1016/J.JTRANGEO.2020.102931.

C. Luo, T. Ma, M. Wang, X. Hu, C. Liu, and X. Wang, “Analysis of the temporal and spatial evolution of China’s air passenger transportation and the impact of policies on passenger flows,” PLoS One, vol. 20, no. 5, p. e0323482, May 2025, doi: 10.1371/JOURNAL.PONE.0323482.

L. Siozos-Rousoulis, D. Robert, and W. Verbeke, “A study of the U.S. domestic air transportation network: temporal evolution of network topology and robustness from 2001 to 2016,” Journal of Transportation Security, vol. 14, no. 1–2, p. 55, Jun. 2021, doi: 10.1007/S12198-020-00227-X.

“HUBNET - KEMENHUB - Hubnet.” Accessed: Jan. 15, 2026. [Online]. Available: https://hubnet.kemenhub.go.id/

H. Rigneault et al., “Data Collection Methods and Tools for Research; A Step-by-Step Guide to Choose Data Collection Technique for Academic and Business Research Projects,” International Journal of Academic Research in Management (IJARM), vol. 10, no. 1, pp. 10–38, Sep. 2021, doi: 10.34894/VQ1DJA.

J. L. Ballard, Z. Wang, W. Li, L. Shen, and Q. Long, “Deep learning-based approaches for multi-omics data integration and analysis,” BioData Mining 2024 17:1, vol. 17, no. 1, pp. 38-, Oct. 2024, doi: 10.1186/s13040-024-00391-z.

“statistik-transportasi-udara-2023”.

S. Dhummad, “The Imperative of Exploratory Data Analysis in Machine Learning,” Scholars Journal of Engineering and Technology Abbreviated Key Title: Sch J Eng Tech, vol. 13, no. 1, pp. 30–44, 2025, doi: 10.36347/sjet.2025.v13i01.005.

J. H. T. Kim and H. Kim, “Estimating Skewness and Kurtosis for Asymmetric Heavy-Tailed Data: A Regression Approach,” Mathematics, vol. 13, no. 16, p. 2694, Aug. 2025, doi: 10.3390/MATH13162694/S1.

K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.

M. K. Hasan, M. A. Alam, S. Roy, A. Dutta, M. T. Jawad, and S. Das, “Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021),” Inform. Med. Unlocked, vol. 27, p. 100799, Jan. 2021, doi: 10.1016/J.IMU.2021.100799.

A. Sina Booeshaghi and L. Pachter, “Normalization of single-cell RNA-seq counts by log(x + 1) or log(1 + x),” Bioinformatics, vol. 37, no. 15, pp. 2223–2224, Aug. 2021, doi: 10.1093/BIOINFORMATICS/BTAB085.

U. M. Khaire and R. Dhanalakshmi, “Stability of feature selection algorithm: A review,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 4, pp. 1060–1073, Apr. 2022, doi: 10.1016/J.JKSUCI.2019.06.012.

M. Fira, L. Goras, and H. N. Costin, “Evaluating Sparse Feature Selection Methods: A Theoretical and Empirical Perspective,” Applied Sciences 2025, Vol. 15, Page 3752, vol. 15, no. 7, p. 3752, Mar. 2025, doi: 10.3390/APP15073752.

G. I. Hapsari, R. Munadi, B. Erfianto, and I. D. Irawati, “Feature Selection Using Pearson Correlation for Ultra-Wideband Ranging Classification,” Jurnal RESTI, vol. 9, no. 2, pp. 209–217, Apr. 2025, doi: 10.29207/RESTI.V9I2.6281.

F. V. P. Samosir, L. P. Mustamu, E. D. Anggara, A. I. Wiyogo, and A. Widjaja, “Exploratory Data Analysis terhadap Kepadatan Penumpang Kereta Rel Listrik,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 7, no. 2, pp. 449-467–449 – 467, Aug. 2021, doi: 10.28932/JUTISI.V7I2.3700.

C. A. Rickert, M. Henkel, and O. Lieleg, “An efficiency-driven, correlation-based feature elimination strategy for small datasets,” APL Machine Learning, vol. 1, no. 1, Mar. 2023, doi: 10.1063/5.0118207.

V. Çetin and O. Yildiz, “A comprehensive review on data preprocessing techniques in data analysis,” vol. 28, no. 2, pp. 299–312, 2022, doi: 10.5505/pajes.2021.62687.

K. P. Sinaga and M. S. Yang, “Unsupervised K-means clustering algorithm,” IEEE Access, vol. 8, pp. 80716–80727, 2020, doi: 10.1109/ACCESS.2020.2988796.

I. K. Khan et al., “Determining the optimal number of clusters by Enhanced Gap Statistic in K-mean algorithm,” Egyptian Informatics Journal, vol. 27, p. 100504, Sep. 2024, doi: 10.1016/j.eij.2024.100504.

M. Shutaywi and N. N. Kachouie, “Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering,” Entropy 2021, Vol. 23, Page 759, vol. 23, no. 6, p. 759, Jun. 2021, doi: 10.3390/E23060759.

Z. Syahputri, S. Sutarman, and M. A. P. Siregar, “Determining The Optimal Number of K-Means Clusters Using The Calinski Harabasz Index and Krzanowski and Lai Index Methods for Groupsing Flood Prone Areas In North Sumatra,” Sinkron, vol. 9, no. 1, pp. 571–580, Jan. 2024, doi: 10.33395/sinkron.v9i1.13246.

Y. Zhang et al., “Review and revamp of compositional data transformation: A new framework combining proportion conversion and contrast transformation,” Comput. Struct. Biotechnol. J., vol. 23, pp. 4088–4107, Dec. 2024, doi: 10.1016/J.CSBJ.2024.11.003.

I. K. khan et al., “Addressing limitations of the K-means clustering algorithm: outliers, non-spherical data, and optimal cluster selection,” AIMS Mathematics 2024 9:25070, vol. 9, no. 9, pp. 25070–25097, 2024, doi: 10.3934/MATH.20241222.