Optimalisasi Model Artificial Neural Network Menggunakan Certainty Factor (C-ANN) Untuk Pemetaan Kerawanan Tanah Longsor Skala Semi-Detil di DAS Bendo, Kabupaten Banyuwangi

Syamsul Bachri; Kresno Sastro Bangun Utomo; Sumarmi Sumarmi; Mohammad Naufal Fathoni; Yulius Eka Aldianto

doi:10.22146/mgi.57869

Optimalisasi Model Artificial Neural Network Menggunakan Certainty Factor (C-ANN) Untuk Pemetaan Kerawanan Tanah Longsor Skala Semi-Detil di DAS Bendo, Kabupaten Banyuwangi

https://doi.org/10.22146/mgi.57869

Syamsul Bachri^(1*), Kresno Sastro Bangun Utomo⁽²⁾, Sumarmi Sumarmi⁽³⁾, Mohammad Naufal Fathoni⁽⁴⁾, Yulius Eka Aldianto⁽⁵⁾

(1) Jurusan Geografi, Fakultas Ilmu Sosial, Universitas Negeri Malang, Indonesia
(2) Jurusan Geografi, Fakultas Ilmu Sosial, Universitas Negeri Malang, Indonesia
(3) Jurusan Geografi, Fakultas Ilmu Sosial, Universitas Negeri Malang, Indonesia
(4) Jurusan Geografi, Fakultas Ilmu Sosial, Universitas Negeri Malang, Indonesia
(5) Jurusan Geografi, Fakultas Ilmu Sosial, Universitas Negeri Malang, Indonesia
(*) Corresponding Author

Abstract

Kerawanan longsor di DAS Bendo termasuk dalam kerawanan kelas sedang hingga tinggi. Sampai dengan saat ini, pemetaan rawan longsor di DAS Bendo baru dilakukan pada skala pemetaan 1:250.000. Penelitian ini bertujuan untuk melakukan pemodelan pemetaan kerawanan longsor di DAS Bendo pada skala semi-detil. Metode yang digunakan dalam penelitian ini adalah optimalisasi model artificial neural network menggunakan certainty factor (C-ANN). Peta kerawanan dibangun berdasarkan faktor pengontrol tanah longsor yang berkorelasi positif terhadap kejadian longsor menggunakan Certainty Factor. Sedangkan pemodelan prediksi kerawanan menggunakan model ANN, khususnya arsitektur BPNN (back-propagation neural network). Hasil pemodelan menunjukkan bahwa model C-ANN (7 variabel independen) memiliki nilai AUC (0,916) lebih tinggi daripada model ANN (0,778). Faktor redundansi data, multikolinieritas data, dan proporsi kejadian longsor terhadap cakupan wilayah penelitian mengakibatkan ketidakpastian dalam data variabel independen. Melalui penelitian ini ditemukan hasil bahwa kondisi kerawanan longsor di DAS Bendo masuk kategori tinggi, khususnya pada lereng atas Gunung Ijen, Rante, dan Merapi.

Landslide disaster in DAS Bendo is categorized as moderate to highly susceptible. Until today, landslide hazard mapping in DAS Bendo has been carried out with a scale 1:250.000. This study aimed to model landslide susceptibility mapping on a semi-detailed scale. The method used in this research was the integration of the Certainty Factor with Artificial Neural Network models (C-ANN).The development of susceptibility mapping based on factors that positively correlate to landslide events using Certainty Factor. While the susceptibility prediction model using the ANN model, specifically the BPNN (back-propagation neural network) architecture. Modelling results show that the C-ANN model (7 independent variables) has an AUC value (0.916) higher than the ANN model (0.778). Data redundancy factors, multicollinearity of data, and the proportion of landslide events to the study area's coverage resulted in uncertainty in the independent variable data. This research found that the Landslide hazard in the Bendo Watershed is in the high category, especially on the upper slopes of Mount Ijen, Rante, and Merapi.

Keywords

Certainty Factor, Artificial Neural Network, Sistem Informasi Geografi, Penginderaan Jauh, Pemetaan Kerawanan Tanah Longsor

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DOI: https://doi.org/10.22146/mgi.57869