Analysis and Effectiveness of Flood Hazard-Based Settlement Prediction Models in Tegal Regency

Imam Bagus Mulyanto*  -  Universitas Diponegoro, Indonesia
Nugroho Adi Kurniawan  -  Universitas Diponegoro
Joshua Alvin Pradipta  -  Universitas Diponegoro

(*) Corresponding Author
DOI: 10.30659/jpsa.v23i1.47670

Annual flooding in Tegal Regency reflects a mismatch between settlement expansion and environmental vulnerability, particularly within high-risk flood zones. This issue requires predictive approaches capable of directing settlement growth adaptively. This study aims to develop a Cellular Automata (CA)–based settlement growth prediction model integrated with flood hazard maps and evaluate its effectiveness in supporting disaster-resilient spatial planning. The Analytical Hierarchy Process (AHP) was applied to determine settlement-driving factors based on expert interviews. The novelty of this research lies in the direct integration of flood-hazard zoning constraints into CA transition rules, combined with field-based sample-point validation to assess spatial accuracy. The model was simulated for 2023–2043 at five-year intervals. Results indicate that proximity to existing settlements is the dominant driver, while hazard-based zoning effectively restricts expansion into high-risk areas. Field validation confirms high model accuracy, reinforcing the reliability of the predictions. Overall, the integrated CA model provides a scientific and operational basis for local governments to guide settlement development toward safer zones and enhance the resilience of spatial planning.

Keywords: Disaster resilient spatial planning, land Cover simulation, Analytical Hierarchy Process (AHP)

 

Banjir tahunan di Kabupaten Tegal menunjukkan adanya ketidaksesuaian perkembangan permukiman dengan kondisi kerentanan lingkungan, terutama pada kawasan rawan banjir. Permasalahan ini menuntut pendekatan prediktif yang mampu mengarahkan pertumbuhan permukiman secara adaptif. Penelitian ini bertujuan mengembangkan model prediksi perkembangan permukiman berbasis Cellular Automata (CA) yang diintegrasikan dengan peta bahaya banjir, serta mengevaluasi efektivitasnya untuk mendukung penataan ruang yang berketahanan bencana. Metode Analytical Hierarchy Process (AHP) digunakan untuk menentukan faktor pendorong perkembangan permukiman melalui wawancara pakar. Kebaruan penelitian terletak pada integrasi langsung antara kendala zonasi bahaya banjir dan proses transisi CA, yang dikombinasikan dengan validasi lapangan berbasis titik sampel untuk menilai kesesuaian spasial hasil simulasi. Model disimulasikan untuk periode 2023–2043 dengan interval lima tahunan. Hasilnya menunjukkan bahwa kedekatan terhadap permukiman eksisting merupakan faktor dominan, sementara penerapan zonasi bahaya secara efektif membatasi ekspansi menuju area berisiko. Validasi lapangan mengonfirmasi akurasi tinggi model, memperkuat reliabilitas prediksi yang dihasilkan. Secara keseluruhan, model CA terintegrasi ini memberikan dasar ilmiah dan operasional bagi pemerintah daerah dalam mengarahkan perkembangan permukiman ke zona aman serta meningkatkan ketangguhan rencana tata ruang.

Kata Kunci: Perencanaan Tangguh Bencana, Simulasi Penggunaan Lahan, Analytical Hierarchy Process (AHP)

  1. Aristo, M. R., & Hizbaron, D. R. (2023). Pengaruh faktor kerentanan fisik dan sosial akibat erupsi gunung merapi terhadap ketercapaian sdgs dan irbi. Jurnal Teknosains, 12(2), 177. https://doi.org/10.22146/teknosains.76613
  2. Awaliyah, N., Ariyaningsih, A., & Ghozali, A. (2020). Analisis Faktor yang Berpengaruh Terhadap Terjadinya Banjir di DAS Ampal/Klandasan Besar dan Kesesuaian Program dengan Faktor Penanganannya. Jurnal Penataan Ruang, 15(2), 57. https://doi.org/10.12962/j2716179x.v15i2.7376
  3. Burhan, I. M., Achmad, A., Rizkiya, P., & Hasan, Z. (2020). Forecasting the land use change of urban coastal area in Banda Aceh and its impact on urban sustainability using LandUseSIM cellular automata simulation model. Aceh International Journal of Science and Technology, 9(3), 120–131. https://doi.org/10.13170/aijst.9.3.17303
  4. Cilliers, D. P. (2019). Considering flood risk in spatial development planning: A land use conflict analysis approach. Jamba: Journal of Disaster Risk Studies, 11(1), 1 – 9. https://doi.org/10.4102/JAMBA.V11I1.537
  5. Congalton, R., & Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Third Edition. https://doi.org/10.1201/9780429052729
  6. Eldi. (2020). ANALISIS PENYEBAB BANJIR DI DKI JAKARTA. Journal of Physics A: Mathematical and Theoretical, 44(8), 90–96. https://doi.org/10.1088/1751-8113/44/8/085201
  7. Febianti, V., Sasmito, B., & Bashit, N. (2022). Pemodelan Perubahan Tutupan Lahan Berbasis Penginderaan Jauh (Studi Kasus: Kota Semarang). Jurnal Geodesi Undip Oktober, 11(3), 111–120. https://doi.org/https://doi.org/10.15294/jg.v12i1.8019
  8. Feng, Y., & Qi, Y. (2018). Modeling patterns of land use in Chinese cities using an integrated cellular automata model. ISPRS International Journal of Geo-Information, 7(10). https://doi.org/10.3390/ijgi7100403
  9. Hanafi, F., Rahmadewi, D., & Setiawan, F. (2021). Land Cover Changes Based on Cellular Automata for Land Surface Temperature in Semarang Regency. Geosfera Indonesia, 6, 301. https://doi.org/10.19184/geosi.v6i3.23471
  10. Handayani, W., Fisher, M. R., Rudiarto, I., Sih Setyono, J., & Foley, D. (2019). Operationalizing resilience: A content analysis of flood disaster planning in two coastal cities in Central Java, Indonesia. International Journal of Disaster Risk Reduction, 35(May 2018), 101073. https://doi.org/10.1016/j.ijdrr.2019.101073
  11. Heiland, P. (2017). Contributions of spatial planning in the context of flood risk management; [Beitrag der Raumplanung beim Hochwasserrisikomanagement]. WasserWirtschaft, 107(11), 20 – 23. https://doi.org/10.1007/s35147-017-0181-6
  12. Hirabayashi, Y., Tanoue, M., Sasaki, O., Zhou, X., & Yamazaki, D. (2021). Global exposure to flooding from the new CMIP6 climate model projections. Scientific Reports, 11(1), 1–7. https://doi.org/10.1038/s41598-021-83279-w
  13. Insani, T. D., Rudiarto, I., Handayani, W., & Wijaya, H. B. (2022). Rural livelihood resilience on multiple dimensions: a case study from selected coastal areas in Central Java. World Review of Science, Technology and Sustainable Development, 18(2), 176–193. https://doi.org/10.1504/WRSTSD.2022.121303
  14. Khomariyah, N. L., Astutik, S., & Apriyanto, B. (2022). Penggunaan SIG Untuk Pemetaan Mitigasi Bencana Banjir di Desa Sidorejo Kecamatan Rowokangkung Kabupaten Lumajang. Majalah Pembelajaran Geografi, 5(1), 26. https://doi.org/10.19184/pgeo.v5i1.31194
  15. Larasati, N. M. (2017). Analisis Penggunaan Dan Pemanfaatan Tanah (P2T) Menggunakan Sistem Informasi Geografis Kecamatan Banyumanik Tahun 2016. Jurnal Geodesi Undip, 6, 89–97. https://doi.org/https://doi.org/10.14710/jgundip.2017.18131
  16. Lenaini, I. (2021). Teknik Pengambilan Sampel Purposive Dan Snowball Sampling. HISTORIS: Jurnal Kajian, Penelitian & Pengembangan Pendidikan Sejarah, 6(1), 33–39. http://journal.ummat.ac.id/index.php/historis
  17. Liu, J., Xiong, J., Chen, Y., Sun, H., Zhao, X., Tu, F., & Gu, Y. (2023). An integrated model chain for future flood risk prediction under land-use changes. Journal of Environmental Management, 342, 118125. https://doi.org/https://doi.org/10.1016/j.jenvman.2023.118125
  18. Mansour, A., Mrad, D., & Djebbar, Y. (2024). Advanced modeling for flash flood susceptibility mapping using remote sensing and GIS techniques: a case study in Northeast Algeria. Environmental Earth Sciences, 83(2). https://doi.org/10.1007/s12665-023-11324-0
  19. Meng, M., Dąbrowski, M., Tai, Y., Stead, D., & Chan, F. (2019). Collaborative spatial planning in the face of flood risk in delta cities: A policy framing perspective. Environmental Science and Policy, 96, 95 – 104. https://doi.org/10.1016/j.envsci.2019.03.006
  20. Moghadas, M., Asadzadeh, A., Vafeidis, A., Fekete, A., & Kötter, T. (2019). A multi-criteria approach for assessing urban flood resilience in Tehran, Iran. International Journal of Disaster Risk Reduction, 35(February), 101069. https://doi.org/10.1016/j.ijdrr.2019.101069
  21. Nabila, D. A. (2023). Pemodelan prediksi dan kesesuaian perubahan penggunaan lahan menggunakan Cellular Automata-Artificial Neural Network (CA-ANN). Tunas Agraria, 6(1), 41–55. https://doi.org/10.31292/jta.v6i1.203
  22. Ningrum, A. S., & Ginting, K. B. (2020). Strategi Penanganan Banjir Berbasis Mitigasi Bencana Pada Kawasan Rawan Bencana Banjir di Daerah Aliran Sungai Seulalah Kota Langsa. Geography Science Education Journal (GEOSEE), 1(1), 6–13. https://jurnal.unsil.ac.id/index.php/geosee/article/view/1919
  23. Parasdyo, M. M., & Susilo, B. (2012). Komparasi Akurasi Model Cellular Automata untuk Simulasi Perkembangan Lahan Terbangun dari Berbagai Variasi Matriks Probabilitas Transisi. Masterplanning Futures, 238–274. https://doi.org/https://doi.org/10.24252/jpm.v9i1.10488
  24. Pérez-Molina, E., Sliuzas, R., Flacke, J., & Jetten, V. (2017). Developing a cellular automata model of urban growth to inform spatial policy for flood mitigation: A case study in Kampala, Uganda. Computers, Environment and Urban Systems, 65, 53 – 65. https://doi.org/10.1016/j.compenvurbsys.2017.04.013
  25. Pratomoatmojo, N. A. (2018). Permodelan Perubahan Penggunaan Lahan Berbasis Cellular Automata dan Sistem Informasi Geografis dengan Menggunakan LanduseSim. Jurnal Penataan Ruang, 13(1), 26. https://doi.org/10.12962/j2716179x.v13i1.7064
  26. Rahmawati, M., & Pratomoatmojo, N. A. (2020). Pemodelan Perubahan Penggunaan Lahan Berbasis Cellular Automata pada Wilayah Peri Urban Kota Surabaya di Kabupaten Sidoarjo. Jurnal Teknik ITS, 8(2). https://doi.org/10.12962/j23373539.v8i2.48484
  27. Rahmawaty, M. A., & Hasan, A. F. (2023). Mapping The Location of Flood Shelters in Demak Regency using The Spatial Multi Criteria Evaluation Method. IOP Conference Series: Earth and Environmental Science, 1264(1). https://doi.org/10.1088/1755-1315/1264/1/012010
  28. Rusmawan, R. (2018). Pemilihan Lahan Untuk Lokasi Permukiman. Geomedia: Majalah Ilmiah Dan Informasi Kegeografian, 7(2), 41–48. https://doi.org/10.21831/gm.v7i2.19088
  29. Sadewo, M. N., & Buchori, I. (2018). Deteksi Perubahan Luasan Mangrove Teluk Youtefa Kota Jayapura Menggunakan Citra Landsat Multitemporal Simulasi Perubahan Penggunaan Lahan Akibat Pembangunan Kawasan Industri Kendal (KIK) Berbasis Cellular Automata. Majalah Geografi Indonesia, 32(2), 115. https://doi.org/10.22146/mgi.33755
  30. Sejati, A. W., Putri, S. N. A. K., Rahayu, S., Buchori, I., Rahayu, K., Andika Wiratmaja, I. G. A. M., Muzaki, A. J., & Basuki, Y. (2023). Flood Hazard Risk Assessment based on Multicriteria Spatial Analysis GIS as Input for Spatial Planning Policies in Tegal Regency, Indonesia. Geographica Pannonica, 27(1), 50–68. https://doi.org/10.5937/gp27-40927
  31. Septawicaksono, D. S., & Pratomoatmojo, N. A. (2020). Prediksi Perkembangan Pemukiman Berbasis Cellular Automata dengan Batasan Kawasan Rawan Banjir di Perkotaan Kabupaten Bojonegoro. Jurnal Teknik ITS, 8(2), 131–137. https://doi.org/10.12962/j23373539.v8i2.48831
  32. Szwagrzyk, M., Kaim, D., Price, B., Wypych, A., Grabska, E., & Kozak, J. (2018). Impact of forecasted land use changes on flood risk in the Polish Carpathians. Natural Hazards, 94(1), 227–240. https://doi.org/10.1007/s11069-018-3384-y
  33. Yohanes Paulus Goo Ado, Rieneke L. E. Sela, & F. W. (2023). JURNAL. Prediksi Perubahan Penggunaan Lahan Berbasis Cellular Automata Di Kota Batam Tahun 2041, 13(2), 19–28. https://doi.org/https://doi.org/10.35799/jbl.v13i2.46570

Jurnal Planologi
Published by Pusat Studi Planologi Universitas Islam Sultan Agung Semarang, in collaboration with Asosiasi Sekolah Perencanaan Indonesia.
Jl. Kaligawe Raya KM. 4 Semarang, Indonesia
Phone: +6212345678
Email: jurnalplanologi@unissula.ac.id

View My Stats

e-ISSN: 1829-9172

p-ISSN: 2615-5257

DOI : 10.30659/japs

Creative Commons License

Get a feed by atom here, RRS2 here and OAI Links here

apps