Classification of Flood-Prone Areas Using 10-Fold Cross Validation and K-Nearest Neighbors

Adyatma Andhika Bagaskara; Kristoko Dwi Hartomo

doi:10.32520/stmsi.v13i1.3637

Classification of Flood-Prone Areas Using 10-Fold Cross Validation and K-Nearest Neighbors

Adyatma Andhika Bagaskara, Kristoko Dwi Hartomo

Abstract

Floods occur when the volume of water exceeds the capacity of containment or the normal flow of water channels, such as rivers, lakes, canals, or drainage systems. In 2021 and 2022, there were 88 flood disasters in Semarang. Factors influencing the occurrence of flood disasters include rainfall, land slope, and the vegetation cover index (NDVI). The main objective of this research is to identify and classify areas in Semarang that are susceptible to flooding by mapping them based on rainfall, vegetation index, and land slope. The classified statuses are flood-prone areas and non-flood-prone areas. A spatial assessment of areas prone to flooding based on GIS is performed using the K-Nearest Neighbor (K-NN) classification algorithm with the R programming language to accomplish this objective. Optimal k testing using the 10-fold cross-validation method reveals that the highest accuracy of the system is achieved with the K-NN method at k=7, reaching 86%. The findings from this study suggest that eight sub-district areas within Semarang are susceptible to flooding, whereas 167 areas in Semarang do not exhibit susceptibility to floods. The potential areas prone to flooding are represented by making a Semarang flood susceptibility map using Quantum GIS software.

Full Text:

PDF

References

A. W. Adi et al., “Indeks risiko bencana Indonesia tahun 2021,” pp. 11–3, 2022.

A. T. Ujung, A. L. Nugraha, and H. S. Firdaus, “Kajian Pemetaan Risiko Bencana Banjir Kota Semarang Dengan Menggunakan Sistem Informasi Geografis,” J. Geod. Undip, vol. 8, no. 4, pp. 154–164, 2019.

R. Situmorang et al., “Model Algoritma K-Nearest Neighbor (K-NN) dan Naïve Bayes,” vol. 7, no. 1, pp. 250–254, 2023.

B. J. A. Gunadi, A. L. Nugraha, and A. Suprayogi, “Aplikasi Pemetaan Multi Risiko Bencana di Kabupaten Banyumas Menggunakan Open Source Software GIS,” Geod. Undip Oktober, vol. 4, no. 4, pp. 287–296, 2015.

F. Tempola, M. Muhammad, and A. Khairan, “Perbandingan Klasifikasi Antara KNN dan Naive Bayes pada Penentuan Status Gunung Berapi dengan K-Fold Cross Validation,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, p. 577, 2018, doi: 10.25126/jtiik.201855983.

K. Pasaribu, R. Saputra, and C. Setianingsih, “Sistem Informasi Monitoring Bencana Alam Dari Data Media Sosial Menggunakan Metode K-Nearest Neighbor Natural Disaster Monitoring Information System From Social Media Data Using K-Nearest Neighbor Method,” vol. 8, no. 5, pp. 6684–6693, 2021.

S. Cumel, David Zamri, Rahmaddeni, “Perbandingan Metode Data Mining untuk Prediksi Banjir Dengan Algoritma Naïve Bayes dan KNN,” SENTIMAS Semin. Nas. Penelit. dan …, pp. 40–48, 2022, [Online].Available: https://journal.irpi.or.id/index.php/sentimas/article/view/353%0Ahttps://journal.irpi.or.id/index.php/sentimas/article/download/353/132.

Z. E. Fitri, L. N. Y. Syahputri, and A. M. N. Imron, “Classification of White Blood Cell Abnormalities for Early Detection of Myeloproliferative Neoplasms Syndrome Based on K-Nearest Neighbour,” Sci. J. Informatics, vol. 7, no. 1, pp. 136–142, 2020, doi: 10.15294/sji.v7i1.24372.

H. Azis, P. Purnawansyah, F. Fattah, and I. P. Putri, “Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung,” Ilk. J. Ilm., vol. 12, no. 2, pp. 81–86, 2020, doi: 10.33096/ilkom.v12i2.507.81-86.

R. R. R. Arisandi, B. Warsito, and A. R. Hakim, “Aplikasi Naïve Bayes Classifier (Nbc) Pada Klasifikasi Status Gizi Balita Stunting Dengan Pengujian K-Fold Cross Validation,” J. Gaussian, vol. 11, no. 1, pp. 130–139, 2022, doi: 10.14710/j.gauss.v11i1.33991.

N. Anggraini et al., “Analisis Pemetaan Daerah Rawan Banjir Di Kota Medan Tahun 2020,” J. Samudra Geogr., vol. 4, no. 2, pp. 27–33, 2021, doi: 10.33059/jsg.v4i2.3851.

Y. Riko, A. I. Meha, and S. Y. J. Prasetyo, “Perubahan Konversi Lahan Menggunakan NDVI, EVI, SAVI dan PCA pada Citra Landsat 8 (Studi Kasus : Kota Salatiga),” Indones. J. Comput. Model., vol. 1, pp. 25–30, 2019.

S. Yulianto Joko Praetyo, K. Dwi Hartomo, B. Hasiholan Simanjuntak, and D. Widiyanto Candra, “Mitigation & Identification for Local Aridity, Based of Vegetation Indices Combined with Spatial Statistics & Clustering K Means,” J. Phys. Conf. Ser., vol. 1235, no. 1, 2019, doi: 10.1088/1742-6596/1235/1/012028.

P. S. Ananda, “Analisa Potensi Bencana Tanah Longsor menggunakan Interpolasi Inverse Distance Weighted ( IDW ),” vol. 6, no. 1, pp. 6–9, 2022.

B. Laurensz, F. Lawalata, and S. Y. J. Prasetyo, “Potensi resiko banjir dengan menggunakan citra satelit (Studi kasus: Kota Manado, Provinsi Sulawesi Utara),” Indones. J. Comput. Model., vol. 2, no. 1, pp. 17–24, 2019, [Online]. Available: https://ejournal.uksw.edu/icm/article/view/2536.

DOI: https://doi.org/10.32520/stmsi.v13i1.3637

Article Metrics

Abstract view : 84 times
PDF - 33 times

Refbacks

There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Username
Password
Remember me