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Abstract
Kertajati International Airport is one of the newest airports located in Majalengka regency, West Java province. The establishment of this airport has sparked interest as a case study, particularly regarding land-use changes around the Kertajati area and, more broadly, in Majalengka Regency. This study aims to measure the extent of changes in vegetated and non-vegetated land around Kertajati International Airport, Majalengka Regency, West Java Province. The methodology employed involves the Support Vector Machine (SVM) classification method. Various kernels, including (1) linear, (2) polynomial, (3) radial basis function (RBF), and (4) sigmoid, were simulated in the analysis. The data used in this study comprise Landsat 8 satellite imagery obtained from Google Earth Engine, utilizing bands such as red, green, blue, near-infrared (NIR), and shortwave infrared (SWIR) for the years 2013 and 2023. The dataset was split using the hold-out method into four scenarios, with varying training and testing data proportions: 75%-25%, 80%-20%, 85%-15%, and 90%-10%. Each scenario was repeated 40 times to ensure robust results. The best results were achieved using the SVM model with an RBF kernel at a data split ratio of 75%-25%, as indicated by the highest accuracy scores. Consistent with the accuracy, the evaluation metrics also fell into a high-performance category. Land area predictions for the vicinity of Kertajati International Airport were analyzed based on the optimal data split proportion. The results of this study reveal a significant reduction in vegetated land from 2013 to 2023, accompanied by a notable increase in non-vegetated land over the same period.
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Copyright (c) 2025 Syifa Fauziyah, Achmad Fauzan
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References
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References
D. Setiady and P. Danoedoro, “Prediksi Perubahan Lahan Pertanian Sawah Sebagian Kabupaten Klaten dan Sekitarnya Menggunakan Cellular Automata dan Data Penginderaan Jauh,” Universitas Gadjah Mada, Yogyakarta, 2016.
I. Hanafi, Y. Pujowati, and M. A. Muhtadi, “Pengaruh Pembangunan Infrastruktur Transportasi Berkelanjutan terhadap Mobilitas dan Lingkungan di Kalimantan,” Jurnal Multidisiplin West Science, vol. 02, no. 10, pp. 908–917, 2023.
G. G. Praditya, “Hubungan Bandar Udara Kertajati dengan Perubahan Sosial-Ekonomi Penduduk Sekitar Bandara,” 2021.
A. Rahmadan et al., “Dampak Pembangunan Bandara Internasional Jawa Barat Kertajati Terhadap Peningkatan Pendapatan Asli Daerah di Kabupaten Majalengka,” JURNAL PRINSIP VOLUME, vol. 1, no. 1, 2024, doi: 10.36859/prinsip.v1i1.2929.
B. Hermanto, “Dampak Pembangunan Bandara Internasional Kertajati dalam Kajian Green Political Theory,” Jurnal Ilmu Sosial dan Ilmu Politik Universitas Jambi (JISIP-UNJA), vol. 5, pp. 62–73, 2021.
E. Transparan Putra Zebua and P. Rosyani, “Perancangan Deteksi Objek Kendaraan Bermotor Berbasis OpenCV Phyton menggunakan Metode HOG-SVM untuk Analisis Lalu Lintas Cerdas,” Jurnal Artificial Inteligent dan Sistem Penunjang Keputusan, vol. 2, no. 1, pp. 16–26, 2024, [Online]. Available: https://jurnalmahasiswa.com/index.php/aidanspk
C. El Morr, M. Jammal, H. Ali-Hassan, and W. El-Hallak, “Support Vector Machine,” 2022, pp. 385–411. doi: 10.1007/978-3-031-16990-8_13.
S. Suthaharan, “Support Vector Machine,” 2016, pp. 207–235. doi: 10.1007/978-1-4899-7641-3_9.
M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intelligent Systems and their Applications, vol. 13, no. 4, pp. 18–28, 1998, doi: 10.1109/5254.708428.
Y. Ma and G. Guo, Eds., Support Vector Machines Applications. Cham: Springer International Publishing, 2014. doi: 10.1007/978-3-319-02300-7.
K. Saravanan, R. B. Prakash, C. Balakrishnan, G. V. P. Kumar, R. Siva Subramanian, and M. Anita, “Support Vector Machines: Unveiling the Power and Versatility of SVMs in Modern Machine Learning,” in 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 2023, pp. 680–687. doi: 10.1109/ICIMIA60377.2023.10426542.
D. Anguita, A. Ghio, N. Greco, L. Oneto, and S. Ridella, “Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory,” in The 2010 International Joint Conference on Neural Networks (IJCNN), 2010, pp. 1–8. doi: 10.1109/IJCNN.2010.5596450.
G. W. Pereira, D. S. M. Valente, D. M. de Queiroz, N. T. Santos, and E. I. Fernandes-Filho, “Soil mapping for precision agriculture using support vector machines combined with inverse distance weighting,” Precis Agric, vol. 23, no. 4, pp. 1189–1204, Aug. 2022, doi: 10.1007/s11119-022-09880-9.
K. Ghanem, F. J. Aparicio-Navarro, K. G. Kyriakopoulos, S. Lambotharan, and J. A. Chambers, “Support Vector Machine for Network Intrusion and Cyber-Attack Detection,” in 2017 Sensor Signal Processing for Defence Conference (SSPD), 2017, pp. 1–5. doi: 10.1109/SSPD.2017.8233268.
J. and Z. S. A. and A. O. A. and L. M. and U. I. and I. C. Hai Tao and Zhou, “Evaluation of Text Classification Using Support Vector Machine Compare with Naive Bayes, Random Forest Decision Tree and K-NN,” in Proceedings of ICACTCE’23 — The International Conference on Advances in Communication Technology and Computer Engineering, Z. and K. N. Iwendi Celestine and Boulouard, Ed., Cham: Springer Nature Switzerland, 2023, pp. 321–331.
M. E. D. Chaves, M. C. A. Picoli, and I. D. Sanches, “Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review,” Remote Sens (Basel), vol. 12, no. 18, p. 3062, Sep. 2020, doi: 10.3390/rs12183062.
C. Wang et al., “Landsat-8 to Sentinel-2 Satellite Imagery Super-Resolution-Based Multiscale Dilated Transformer Generative Adversarial Networks,” Remote Sens (Basel), vol. 15, no. 22, p. 5272, Nov. 2023, doi: 10.3390/rs15225272.
Y. T. Widayati, Y. Prihati, and S. Widjaja, “Analisis dan Komparasi Algoritma Naive Bayes dan C4.5 untuk Klasifikasi Loyalitas Pelanggan MNC Play Kota Semarang,” TRANSFORMTIKA, vol. 18, no. 2, pp. 161–172, 2021.
S. F. Mohammed and G. J. M. Mahdi, “Non-linear support vector machine classification models using kernel tricks with applications,” 2024, p. 040017. doi: 10.1063/5.0196147.
A. Patle and D. S. Chouhan, “SVM kernel functions for classification,” in 2013 International Conference on Advances in Technology and Engineering (ICATE), IEEE, Jan. 2013, pp. 1–9. doi: 10.1109/ICAdTE.2013.6524743.
F. Putrawansyah, “Penerapan Metode Support Vector Machine Terhadap Klasifikasi Jenis Jambu Biji,” JIKO (Jurnal Informatika dan Komputer), vol. 8, no. 1, p. 193, Feb. 2024, doi: 10.26798/jiko.v8i1.988.
R. Munawarah, O. Soesanto, M. Reza Faisal, J. A. Yani Km, and K. selatan, “Penerapan Metode Support Vector Machine Pada Diagnosa Hepatitis,” Kumpulan Jurnal Ilmu Komputer, vol. 4, 2016.
H.-C. Sun and Y.-C. Huang, “Support Vector Machine for Vibration Fault Classification of Steam Turbine-Generator Sets,” Procedia Eng, vol. 24, pp. 38–42, 2011, doi: 10.1016/j.proeng.2011.11.2598.
C. Z. V. Junus, T. Tarno, and P. Kartikasari, “Klasifikasi Menggunakan Metode Support Vector Machine dan Random Forest untuk Deteksi Awal Risiko Diabetes Melitus,” Jurnal Gaussian, vol. 11, no. 3, pp. 386–396, Jan. 2023, doi: 10.14710/j.gauss.11.3.386-396.
L. Widya Astuti, I. Saluza, Faradilla, and M. Fadhiel Alie, “Optimalisasi Klasifikasi Kanker Payudara Menggunakan Forward Selection pada Naive Bayes,” Jurnal Ilmiah Informatika Global, vol. 11, pp. 63–67, 2020, [Online]. Available: https://archive.ics.uci.edu/ml/machine-learning-
G. M. Foody, “Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient,” PLoS One, vol. 18, no. 10, p. e0291908, Oct. 2023, doi: 10.1371/journal.pone.0291908.
P. Gimeno, V. Mingote, A. Ortega, A. Miguel, and E. Lleida, “Generalizing AUC Optimization to Multiclass Classification for Audio Segmentation With Limited Training Data,” IEEE Signal Process Lett, vol. 28, pp. 1135–1139, 2021, doi: 10.1109/LSP.2021.3084501.
S. Ruuska, W. Hämäläinen, S. Kajava, M. Mughal, P. Matilainen, and J. Mononen, “Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle,” Behavioural Processes, vol. 148, pp. 56–62, Mar. 2018, doi: 10.1016/j.beproc.2018.01.004.
P. Rachman Hakim, A. Hadi Syafrudin, S. Salaswati, S. Utama, and W. Hasbi, “Development of Systematic Image Preprocessing of LAPAN-A3/IPB Multispectral Images,” International Journal of Advanced Studies in Computer Science in Engineering, vol. 7, no. 10, 2018.
S. Gibbons and W. Wu, “Airports, access and local economic performance: evidence from China,” J Econ Geogr, vol. 20, no. 4, pp. 903–937, Jul. 2020, doi: 10.1093/jeg/lbz021.
R. Florida, C. Mellander, and T. Holgersson, “Up in the air: the role of airports for regional economic development,” Ann Reg Sci, vol. 54, no. 1, pp. 197–214, Jan. 2015, doi: 10.1007/s00168-014-0651-z.
J. Cidell, “The role of major infrastructure in subregional economic development: an empirical study of airports and cities,” J Econ Geogr, vol. 15, no. 6, pp. 1125–1144, Nov. 2015, doi: 10.1093/jeg/lbu029.