Conference Universitas Islam Indonesia (UII)
CE ReForm

Seleksi Faktor Kondisi Kerentanan Longsor Berbasis Weights of Evidence, Variance Inflation Factor, dan Information Value melalui Google Earth Engine di Wilayah Perbukitan Menoreh, Kulon Progo

Date Published : 30 July 2026
paper-cover

Contributors

Nizar Achmad

universitas islam indonesia
Author

Setya Winarno

Universitas Islam Yogyakarta
Dosem Pembimbing 2

Sri Kusumadewi

Dosen Pembimbing 1

DOI

DOI /wp0x6m65

ISBN

2962-2697

Keywords

Tanah longsor Weights of Evidence Variance Inflation Factor Google Earth Engine Menoreh Hills

Proceeding

Vol. 6 No. 1 (2026): The 11th CE ReForm

Track

General Track

License

Copyright (c) 2026 CE ReForm

Creative Commons License

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

Abstract

The Menoreh Hills in Kulon Progo, Yogyakarta Special Region, is one of the highest-frequency landslide areas in Java. This study presents a two-stage conditioning factor selection—(1) iterative Variance Inflation Factor (VIF, threshold < 10) to eliminate multicollinearity, followed by (2) Information Value (IV, threshold ≥ 0.10) to remove weak predictors—before computing Weights of Evidence (WoE) through Google Earth Engine (GEE). The dataset comprises 695 landslide events (2017–2025) after spatial thinning. Of eight candidate factors, NDVI (VIF = 22.89) was eliminated in Stage 1, reflecting a suppression effect in which NDVI captures shared terrain variance in a tropical volcanic setting. In Stage 2, aspect (IV = 0.084) and profile curvature (IV = 0.083) were eliminated as weak predictors. The five final factors are: distance to geological formation boundary (IV = 1.020, very strong), slope (IV = 0.367, strong), distance to river (IV = 0.311, strong), binary lithology Tomk+a1 (IV = 0.258, strong), and TWI-HydroSHEDS (IV = 0.106, moderate). Results confirm the primacy of geological structural control over topographic control in Menoreh Hills landslide susceptibility. The entire pipeline, implemented through GEE and Python, is transparent, reproducible, and adaptable to other volcanic areas in Java.

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How to Cite

Achmad, N. (2026). Seleksi Faktor Kondisi Kerentanan Longsor Berbasis Weights of Evidence, Variance Inflation Factor, dan Information Value melalui Google Earth Engine di Wilayah Perbukitan Menoreh, Kulon Progo. CE ReForm, 6(1), 378-388. https://doi.org//wp0x6m65