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GeoPlanning 
Journal of Geomatics and Planning                                                                                                                Vol. 11, No. 1, 2024     

 

Original Research 

Mapping Landslide Vulnerability using Machine 

Learning Approach along the Taba Penanjung-

Kepahiang Road, Bengkulu Province 

Camelia B. Abrar1, Ashar M. Lubis2*, Darmawan I. Fadli2, Arya J. Akbar1, 

Rida Samdara1  

1. Physics Study Program, University of Bengkulu, Indonesia 

2. Geophysics Study Program, University of Bengkulu, Indonesia 

DOI: 10.14710/geoplanning.11.1.43-56 

Abstract 

Landslides occur when masses of rock, debris or soil move due to various factors and processes that cause land movement. 

The Taba Penanjung-Kepahiang route is one of the areas in Bengkulu Province that is highly prone to landslides. This 

causeway is the only fastest land route connecting the Bengkulu-Kepahiang area. In recent years, the road area has often 

been cut off due to landslides and fallen trees, which have caused road access to be cut off and obstructed and claimed lives. 

This study uses a Machine Learning (ML) and GIS approach with Variable Frequency Ratio using 16 independent factors 

obtained from the spatial database and DEM, which correlate with landslide events. This research aims to gain an in-depth 

understanding of the factors that cause landslides. In addition, the research focus is the development of a Disaster Mitigation 

Model to design and implement effective strategies to reduce the risk and impact of landslide disasters through in-depth 

analysis The dependent factor is the location of the landslide from the historical landslide area for the last five years, with a 

distribution of 70/30%. Furthermore, frequency ratio is used to analyze the correlation between conditioning factors and 

historical landslides. Then, the independent and dependent factors were normalized to create a landslide susceptibility map. 

Frequency Ratio (FR) indicates the likelihood of an event occurring, with drainage density (FR= 0.69), shear wave velocity 

(Vs30) (FR= 0.66), slope (FR= 0.60), and rainfall (FR= 0.55).  The output of the processed data is in the table below. 

Copyright © 2024 GJGP-Undip 

This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license 

1. Introduction  

Landslides are one of the geological disasters that often occur in Indonesia. Landslides occur when a mass 

of rock or soil moves due to controlling factors and triggering processes. A landslide is a movement of the ground 

with a slope direction and moves it on an avalanche (Fadilah et al., 2019). Landslide is a process of mass wastage 

that occurs on slopes formed naturally or engineered by the movement of rock masses, debris, or soil down the 

slope, which is influenced by gravity (Cruden & VanDine, 2013). Landslides occur continuously from year to 

year and make landslide disasters the center of attention and become a severe problem in almost all parts of the 

world because they cause economic or social losses to private and public property (Rotaru et al., 2007). 

One of the areas in Bengkulu with a high level of vulnerability to landslides is the Bengkulu-Kepahiang 

Route; this is because the Bengkulu-Kepahiang Route is an area with diverse geomorphological conditions. This 

area is the only fastest connecting land road that connects the Bengkulu-Kepahiang area, but the area is a forest 

area. In recent years, fallen trees and landslides have been obstructed and have caused road access to be cut off 

and obstructed, claiming lives. Previous cases show that landslides occurred at two or more points on the same 

day, causing several vehicles to get stuck between the 2 points. Landslides can occur with coverage of more than 

one slope, triggered by the same phenomenon (Froude & Petley, 2018). 

e-ISSN: 2355-6544 
 
Received: 01 April 2023;  
Accepted: 07 February 2024;  
Published: 08 March 2024. 
 
Keywords:  
Landslide, Machine Learning, 
Frequency Ratio 
 
*Corresponding author(s)  
email: asharml@unib.ac.id 
 

 

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44 

The area has no alternative roads that allow motorists to turn around until the landslide, trees, or other 

materials from the road can be cleared. Therefore, research related to analyzing and determining landslide 

hazards on the Bengkulu-Kepahiang Route must be carried out as a non-structural mitigation effort. This effort 

helps identify relatively safer areas from landslides so that material and non-material losses can be minimized. 

This research aims to identify landslide-prone areas on the Bengkulu-Kepahiang Route, with the primary 

objective of taking mitigation steps to enhance the safety of people around the area. According to Alcántara-

Ayala & Sassa, (2023) by mapping landslide-prone areas, we can focus on risk management and prevention. 

Additionally, this research offers insight into the vulnerability of landslides on the Bengkulu-Kepahiang Route, 

the region's primary land route. With a better understanding of the potential for landslides, we can take action 

to optimize transportation access and minimize disruption due to landslides, which can harm local communities 

and the economy. 

Several previous studies regarding landslides on the Bengkulu-Kepahiang Cross Route, such as  Suhendra 

& Sugianto, 2018; Hadi et al., 2021; Hadi & Siswanto, 2016; and Sugianto, 2021. In general, studies on landslides 

that previous researchers have carried out show that the area on the Bengkulu-Kepahiang Cross Route has the 

potential to experience ground movement. However, the previous study was still carried out deterministically, 

and the parameters used were still very few, so it is necessary to map landslide-prone areas on the Bengkulu-

Kepahiang Cross Road. To overcome this issue, achine Learning (ML) method is one solution that can solve 

problemsThe ML model is considered essential in disaster mitigation and an ideal landslide management plan 

in landslide modeling for disaster mitigation and disaster management as a mitigation effort (Pourghasemi et al., 

2018). Making a vulnerability map requires data with high accuracy. The more input parameters, the more 

accuracy and sensitivity analysis in mapping landslide vulnerability will be more accurate (Ghorbanzadeh et al., 

2019). The ML landslide detection study uses different classifications (Roodposhti et al., 2019) to increase the 

efficiency of the outputs. 

Mapping of landslide-prone areas will be calculated using Frequency Ratio (FR) and parameters used such 

as elevation (topography), geological conditions, slope aspect (Slope Aspect), slope (Slope), rainfall, plan 

curvature, distance from faults/faults (Kavzoglu et al., 2019) as well as other supporting factors. The 

interconnection of these factors makes a holistic approach important in dealing with and preventing landslides. 

The factors mitigate the adverse impacts of landslides, and it is essential to implement a comprehensive strategy 

that includes prevention and preparedness. Sustainable land use planning, reforestation, early warning systems, 

and advanced technology integrating vulnerable areas are essential to an effective landslide mitigation plan. 

Developing a Disaster Mitigation Model in the Jalan Taba Penanjung-Kepahiang area aims to design and 

implement an effective strategy to reduce the risk and impact of landslides through an ML approach. 

Additionally, we hope that information from this research can be used to increase awareness of community 

preparedness, help design risk mitigation programs, and facilitate faster responses in emergencies. This is 

expected to reduce the impacts materially and non-materially so that the road can be traversed safely. 

2. Data and Methods 

2.1. Study Area and Geological Setting. 

The Taba Penanjung-Kepahiang route (Fig. 1) is an area that connects Central Bengkulu Regency and 

Kepahiang Regency, which are in Bengkulu Province, with an area of 1124.44 ha of the research area. This area 

is where landslides frequently occur in Bengkulu province, and narrow access roads do not allow motorists to 

turn around and cause long traffic jams. Based on DEM data, the topography on the Bengkulu-Kepahiang Cross 

Route is at an elevation of 96 to 880 masl (Suhendra & Sugianto, 2018) with high rainfall with an average of 235-

280 mm/year, so that it will increase the potential for ground movement or landslides (Natasya et al., 2022). 

Landslides occur due to factors arising from the internal geology of the slope as well as the external environment. 

The internal factors, which include geomorphology, stratum lithology, and topography, will control the 

occurrence of landslides. The main external factors in landslides are environmental factors, hydrogeology, and 

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45 

human engineering activities (Xiao et al., 2019). These factors will be input and extracted as the final input on 

ML and soil susceptibility index (LSI) in the form of FR values (Zhu et al., 2021). 

 

Figure 1. (i). Jalan Lintas Taba Penanjung – Kepahiang is using Google Satellite. (ii), (iii), (iv).  

Landslides on the Taba Penanjung Route - Kepahiang in 2021-2022 

The Hulusimpang Formation (Tomh) (Fig.2), with an orange symbol flanked by andesitic basalt volcanic 

rocks, is green. Tomh is around the late mid-Miocene Oligocene, with the most extensive zone mainly along 

the Ketahun-Musikeruh fault zone and several places in the western part of Lambar. 

 
Source: Analysis, 2022 

 Figure 2. Geological Conditions and Study Area of the Taba Penanjung-Kepahiang Route 

2.2. Frequency Ratio (FR) Calculation 

FR is a well-known method for mapping landslides (Ozdemir & Altural, 2013). FR measures the degree 

of correlation between landslide locations according to their independent factors (Solaimani et al., 2013). FR is 

a method for calculating the effect of subclasses of conditioning factors on landslides (He et al., 2012). From FR 

for each class from all data layers, it will be combined with the landslide inventory map independent factor map 

using the equation (Eq.1): 

FR = 
𝑁𝑝𝑖𝑥(𝑆𝑖)/𝑁𝑝𝑖𝑥(𝑁𝑖)

∑ 𝑁𝑝𝑖𝑥(𝑆𝑖)/∑ 𝑁𝑃𝑖𝑥(𝑁𝑖)𝑖𝑖
……………... [Eq.1] 

Npix (Si) is the number of pixels of landslides, and Npix (Ni) is the number of pixels of a class. The 

Landslide Susceptibility Index (LSI) is calculated by the sum of each factor ratio value using the equation (Eq.2):  

LSI = Fr1 + Fr2 + Fr3 + Frn……………..Eq.2] 

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46 

LSI will be obtained by adding up each FR value for each conditioning factor correlated with landslide history, 
where FR is the level of each type of factor. Landslide vulnerability maps are obtained from the correlation 
between the factors overlaid using spatial weight overlay analysis (Okoli et al., 2023). 

2.3. Landslide Inventory and Casual Factor 

Data processing uses the QGIS application and Software R. Processing data on Software R uses a grid 

base with factor attribute values from ML that differ from specific grid sizes (Wei et al., 2022). Input data is in 

the form of local characterization of the avalanche geometry and internal structure, which is used to further 

describe slope stability in modeling (Dou et al., 2020). For vulnerability analysis, a correlation is depicted 

between predisposing causes and triggering factors using a numerical model (van Asch et al., 2007). Data 

processing uses 16 independent factors (Table 1.), continuous and categorical scale factors, and dependent factors 

obtained from landslide inventory maps: location data, places, dates, and other information regarding landslides 

in an area (Guzzetti et al., 2012). In this study, 16 conditioning factors were used, which were classified using 

different methods, specifically manual, equal interval, and natural breaks (Arabameri et al., 2017). 

 The slope aspect parameter (Fig. 3.a) correlates closely with weather conditions (Bednarik et al., 2010). 

It determines its exposure to wind and sunlight, with vulnerability affecting soil moisture and vegetation. While 

in general, the curvature (Fig. 3.b) is the number of surface defects in an area. The greater the surface defects, 

the greater the degree of curvature. Curvature can map stratigraphic features using structural deformation 

models to predict natural fractures and paleo stress (Lisle, 1994; Roberts, 2001; Chopra & Marfurt, 2007). 

    
 Source: Analysis, 2022 (a.)    (b.) 

Figure 3. (a.) Slope Aspect and (b.) Curvature of Research Area 

One of the critical environmental factors in landslide mapping is the elevation (Fig.4.a) (Marjanović et al., 

2011). Figure 4. a shows elevations ranging from 112-918 m. In the research area, Musi and Manna Segments 

(Fig. 4.b) exist in the Taba Penanjung and Kepahiang Regency areas. 

    
   Source: Analysis, 2022 (a.)    (b.) 

Figure 4. (a.) Elevation. of Research Area in m and  
(b.) Distance from Fault and Fault Location on the Research Area in km 

 In the horizontal direction, Plan Curvature (Fig. 5.a) reflects ridges and valleys on a surface, affecting 

flow dispersion and convergence. In the standing order, the profile curvature (Fig. 5.b) can reflect the degree of 

slope transformation involving the flow's acceleration and deceleration (Lee et al., 2018). 

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47 

    
   Source: Analysis, 2022 (a.)    (b.) 

Figure 5. (a.) Plan Curvature and (b.) Profile Curvature of Research Area 

 The slope of the slope (Fig. 6.a) is a factor that significantly determines the occurrence of landslides in 

an area and affects the level of soil slides (Fan et al., 2022). The greater the degree of slope, the higher the level 

of vulnerability in the area. However, steep slopes naturally formed due to bedrock outcrops are not prone to 

landslides (Mohammady et al., 2012). Shear wave velocity Vs30 (Fig. 6.b) is the average shear wave velocity with 

a depth of up to 30 m from the ground surface (Hadi et al., 2018). 

    
   Source: Analysis, 2022 (a.)    (b.) 

Figure 6. (a.) Slope and (b.) Shear Wave Velocity (Vs30) of Research Area 

 Normalized Difference Vegetation Index (NDVI) refers to the active vegetation biomass or forest cover 

(Fig. 7.a). Landslides usually occur on bare land and grasslands (Wang et al., 2020). Road construction is one of 

the factors controlling slope stability, with the hypothesis that landslides occur more frequently along the road. 

This is due to cutting drainage and cutting slopes from making roads that are not suitable (Dahal et al., 2008). 

This research focuses only on the Taba Penanjung-Kepahiang Route. The relationship between distance from 

roads (Fig. 7.b) and landslide risk can be influenced by several factors, including an area's geological and 

topographic characteristics. 

    
   Source: Analysis, 2022 (a.)    (b.) 

Figure 7. (a.) Normalized Difference Vegetation Index (NDVI) and  

(b.) Distance from road at research area 

 Other topographical factors such as Topographic Wetness Index (TWI) (Fig. 8.a) and Sediment 

Phosphorous Index (SPI) (Fig. 8.b) use processed dem data in fill dem, flow direction, slope (o), and flow 

accumulation using the Jenks natural breaks method (Ciurleo et al., 2016) (Equation 3 and 4): 

TWI = loge(
𝐴

𝑡𝑎𝑛𝛽
) ……………... [Eq.3] 

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48 

SPI = A ∙ tan 𝛽……………... [Eq.4] 

 A is the flow Accumulation value in meters squared, and β is the slope(o). TWI accurately describes how 

topographic changes impact land runoff, while SPI reflects the ability of a water system to erode the soil surface 

(Moore & Grayson, 1991; Xiao et al., 2019). 

   
    Source: Analysis, 2022 (a.)    (b.) 

Figure 8. (a.) Topographic Wetness Index (TWI) and (b.) Sediment Phosphorous Index (SPI) of research area 

Geological factors in the form of lithology, where there are only two classifications of geological 

conditions, Hulusimpang formations, and Andesit-Basalt Volcanic Rocksstructures, are described in (Fig. 9.a). 

The supporting factor used is the average rainfall data in the last ten years obtained from the Data Center for 

River Region VII (BWSVII) Bengkulu City (Fig. 9.b). The rainfall data is allocated and applied in analyzing 

recurring periods (Koutsoyiannis, 2004; Wallis et al., 2007; Shou & Lin, 2020). 

   
   Source: Analysis, 2022 (a.)    (b.) 

Figure 9. (a.) Lithology at Research Area; (b.) Rainfall at Research Area in mm/year 

 The slope control factor to the ratio of the total length of the river basin is called the drainage density 

(Fig. 10.b). In general, the higher the density of infiltration drainage, the lower it will be, and the movement of 

the soil surface will be faster. Drainage density indicates the degree of saturation with the flow, which can 

adversely affect slope saturation (Pachauri et al., 1998; Nagarajan et al., 2000; Çevik & Topal, 2003). The distance 

between drainage systems (Fig. 10.a) and landslide risk is also essential in planning infrastructure and 

minimizing landslide risk. Good drainage can help reduce groundwater levels around slope areas. 

    
   Source: Analysis, 2022 (a.)    (b.) 

Figure 10. (a.) Distance from Drainage and (b.) Drainage Density at Research Area 

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49 

This research began with a literature study, carried out by studying previous studies related to this 

proposed research. The research data collection carried out in this research was a secondary collection in the 

form of DEMNAS data, Geological maps, and administrative maps. Next, data processing uses the FR algorithm. 

The next stage is an analysis of the results of existing ML algorithms. Analysis was carried out using the ROC 

curve to evaluate and validate the data obtained to obtain the best landslide susceptibility model. The higher the 

accuracy value of the ROC curve, the better the model produced, and vice versa. The analysis results will be 

depicted as a landslide susceptibility map on the Bengkulu-Kepahiang Route. In general, the research stages are 

as shown in the flow diagram below (Fig. 11) 

 
Figure 11. Research Diagram 

Table 1. Conditioning Factors and Their Classification 

Factors Classes Data Scale Techniques 

Aspect (A) F (–1); N (0–22.5; 337.5–360); NE (22.5–
67.5); E (67.5–112.5); SE (112.5–157.5); S 

(157.5–202.5); SW (202.5–247.5); W 
(247.5–292.5); NW (292.5–337.5) 

https://earthexplorer.usgs.gov/ 
(22 December 2022) 

Geospatial Data Cloud 

8 x 8 m DEM 

Curvature 
(C)  

-319-0; 0; 0-268; https://earthexplorer.usgs.gov/ 
(22 December 2022) 

Geospatial Data Cloud 

8 x 8 m DEM 

Elevation 
(E) 

112-150; 250-450; 450-650; 650-850; 850-
918 

https://earthexplorer.usgs.gov/ 
(22 December 2022) 

Geospatial Data Cloud 

8 x 8 m DEM 

Distance From 
Fault (DF) 

0-100; 100-250; 250-350; 350- 450; >450 https://geologi.esdm.go.id/geomap 
Indonesia Catalogue Service For Geographic 

Information 

Buffering 

Plan Curvature 
(PLC) 

(-13)-(-10); (-10)-0; 0-12 https://earthexplorer.usgs.gov/ 
(22 December 2022) 

Geospatial Data Cloud Continue 

8 x 8 m DEM 

Profile Curvature 
(PRC) 

(-15)-(-10); (-10)-0; 0-5; 5-10; 10-20 https://earthexplorer.usgs.gov/ 
(22 December 2022) 

Geospatial Data Cloud 

8 x 8 m DEM 

Slope (o) 
(S) 

4o-8o; 8o-16o; 16o-35o; 35o-55o; >55o https://earthexplorer.usgs.gov/ 
(22 December 2022) 

Geospatial Data Cloud 

8 x 8 m DEM 

SPI (-10)-(-6); (-6)-(-2); (-2)-2; 2-6; 6-7 https://earthexplorer.usgs.gov/ 
(22 December 2022) 

Geospatial Data Cloud 

8 x 8 m DEM 

NDVI 1300-1500; 1500-1700; 1700-1900; 1900-
2100; 2100-2685 

https://earthexplorer.usgs.gov/ 
(22 December 2022) 

Geospatial Data Cloud 

Extract By Mask 

Drainage Density 
(DRD) 

0-0.5; 0.5-1; 1-1.5; 1.5-2; 2-2.91 https://earthexplorer.usgs.gov/ 
(22 December 2022) 

Geospatial Data Cloud 

8 x 8 m DEM 

TWI 1.7-8; 8-16; 16-20 https://earthexplorer.usgs.gov/ 
(22 December 2022) 

Geospatial Data Cloud 

8 x 8 m DEM 

Vs30 360-760; 760-1500 https://earthexplorer.usgs.gov/ 
(28 December 2022) 

Geospatial Data Cloud 

Extract By Mask 

Lithology 
(LIT) 

1;2 https://www.indonesia-geospasial.com/ 
National Catalogue Service For Geographic 

Information 

Digitization process 

Rainfall 
(R) 

2500-2800; 2800-3165 Data Curah Hujan 10 tahun  
Balai Wilayah Sungai VII Kota Bengkulu.  

Kriging Interpolation 
method 

Distance From 
Road (DR) 

0-100; 100-250; 250-350; 350-450; >450 https://tanahair.indonesia.go.id/portal-web 
Peta AOI 

Buffering 

Distance From 
Drainage 
(DD) 

0-1000; 1000-2500; 2500-3500; 3500-4500; 
>4500 

https://earthexplorer.usgs.gov/ 
(22 December 2022) 

Geospatial Data Cloud 

Buffering  

Source: Analysis, 2022  

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https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://geologi.esdm.go.id/geomap
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://earthexplorer.usgs.gov/
https://www.indonesia-geospasial.com/
https://tanahair.indonesia.go.id/portal-web
https://earthexplorer.usgs.gov/


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3. Result and Discussion 

3.1  Correlation Analysis between Landslide and Independent Factor 

The ratio between the slide classification, a percentage of the overall failure, and the class area as a 

percentage of the entire map is called FR (Nourani et al., 2014). FR is generated from each conditioning factor 

with weights in each sub-class (Umar et al., 2014). The correlation of each element with landslide history 

determines the FR value (Shahabi et al., 2014). The conditioning factor sub-class is used in the input variables 

of the data-based model, with the classification of parameters used in determining the value of the distribution 

of attribute intervals related to the sub-class (Huang et al., 2020). The FR results are a probability comparison 

value between safe and landslide-prone areas, normalized to FRn with a range of 0-1 to see the correlation 

between conditioning factors and landslide history (Chen & Chen, 2021). To show the correlation between 

conditioning factors, pairwise value analysis is used to ensure each index factor's independence level between 

each conditioning factor.  

Table 2. Pairwise Comparison Matrix of Conditioning Factors 

Factors S E DF SPI A C PRC PLC R DR TWI DD NDVI DRD Vs30 LIT 

S 1.00                

E 0.51 1.00               

DF 0.68 1.34 1.00              

SPI 0.45 0.89 0.66 1.00             

A 0.37 0.72 0.54 0.81 1.00            

C 0.21 0.41 0.30 0.46 0.56 1.00           

PRC 0.35 0.69 0.51 0.78 0.95 1.69 1.00          

PLC 0.04 0.09 0.07 0.10 0.12 0.22 0.13 1.00         

R 0.16 0.31 0.23 0.35 0.43 0.76 0.45 3.48 1.00        

DR 0.61 1.21 0.90 1.36 1.67 2.97 1.76 13.69 3.93 1.00       

TWI 0.64 1.26 0.94 1.42 1.74 3.10 1.83 14.27 4.10 1.04 1.00      

DD 0.45 0.89 0.66 1.00 1.22 2.17 1.28 10.01 2.88 0.73 0.70 1.00     

NDVI 0.48 0.95 0.71 1.07 1.32 2.34 1.38 10.79 3.10 0.79 0.76 1.08 1.00    

DRD 1.11 2.18 1.62 2.46 3.02 5.37 3.17 24.70 7.10 1.80 1.73 2.47 2.29 1.00   

Vs30 0.54 1.07 0.80 1.20 1.48 2.63 1.55 12.10 3.48 0.88 0.85 1.21 1.12 0.49 1.00  

LIT 0.02 0.04 0.03 0.05 0.06 0.10 0.06 0.48 0.14 0.04 0.03 0.05 0.04 0.02 0.04 1.00 

Source: Analysis 2022  

 The relationship between conditioning factors is shown in Table 2. The vertical table shows the plan 

curvature as the indicator with the highest correlation among other factors. While horizontally, the drainage 

density indicator has the highest value among other factors. The highest correlation is owned by drainage 

density and plan curvature, with a correlation of 24.70. Previous case studies, by Hadi et al. (2018) and Sugianto 

(2021) regarding landslide-prone mapping using different factors. Sugainto's 2020 study revealed the structure 

of the shear wave velocity (Vs) or subsurface structures along the Bengkulu Kepahiang Causeway based on 

measurements and inversion of microtremor data. This study showed a correlation between the rate of the Vs3o 

shear waves on the Taba Penanjung - Kepahiang Cross Road, the results of which were associated with the 

potential for landslides. Whereas Hadi et al.'s research, applied the HVSR and SAW methods related to the 

potential for landslides in the Kepahiang district. Both of these studies still use deterministic methods using only 

a few parameters. 

 This research uses 16 parameters from processed DEM data extracts and processed catalog map data. The 

area of this study is only 1124.44 (ha), with the minimal classification of conditioning factors due to the small 

space. These conditioning factors greatly influence the FR results, where a few combinations will result in a low 

FR. Classifying many conditioning factors with a large area is necessary to increase the FR output. FR analysis 

shows (Table 3.) that in Aspect indications, the highest probability is in the North West class, while the lowest 

possibility is in the East class. This is related to the vegetation index (NDVI) in the northwest direction, which 

is dominated by a high vegetation index. Following Lee & Min (2001), there is a correlation between the 

vegetation index and the slope. As for the elevation indicator, the highest probability is at an altitude of 450-650 

with FR=0.39 and a massive difference with the lowest FR in the 112-250 altitude class with FR=0.08. The 

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relationship between the fault and the landslide shows that class 800-1200 has the highest probability value with 

FR=0.49, while the fault distance with the lowest FR is in class >1200 with FR=0.02.  

 In the Curvature indication, the highest probability is in the concave and convex class with FR=0.38. The 

Plan Curvature indication has the highest probability value in 2 categories, specifically (-10)-0 and 0-12 with 

FR=0.34, and the lowest FR is (-13) -(-10) with FR=0.32. As for the Profile Curvature indicator, the highest 

probability is in class 10-20 with FR = 0.32, while the lowest is in class 0-5 with FR = 0.11. These three 

indicators are extracted from the same input data. With spatial analysis, the extraction of the three indicators is 

produced simultaneously. In the Slope indicator, the highest probability is in the class with the highest degree 

of slope, specifically >55 with FR=0.60, while the lowest FR is in the slope with the lowest degree, specifically 

4-8 degrees with FR=0.003. This is consistent with a general aspect, the shear stress slope material will increase 

according to the increase in slope degrees, and landslides are likely to occur on the steepest slopes (Yilmaz, 2009). 

 The highest probability of SPI is in class 6-7, where FR=0.35, while the lowest chance is in class (-2)-2, 

with FR=0.08. As for NDVI, the highest probability is in the vegetation class 1300-1500 with FR = 0.29 and in 

class 2100-2658 showing no likelihood of landslides with that class, where the results of the data allocation in 

that class show FR = 0. At Drainage Density, the highest probability is at a density of 0.5-1 with FR=0.69. At a 

density of 1.5-2.2-2.91, it shows that there is no probability in that class with FR=0. For the TWI indicator, the 

probability of landslides is in class 8-16 with FR=0.46, followed by class 1.7-8 with FR=0.45, and the lowest 

probability is in class 16-20 with FR=0.08. In the Vs30 indicator, there are only two classes. The highest 

probability is found at the shear wave speed with a value of 360-760 FR=0.66, and the lowest is at 760-1500 

with FR=0.34. Lithological indicators in this area only have two types of rocks: Quaternary Volcanoes and 

Andesite. The highest probability is in Andesitic rocks with FR=0.51, while in Quaternary volcanoes, the 

probability differs significantly from Andesitic rocks with FR=0.49. 

 The Rainfall Indicator is allocated from rainfall data for the previous ten years, so 2-factor classifications 

are obtained. The highest probability is in class 2500-2800 with FR=0.55, not different from the probability in 

class 2800-3165 with FR=0.45. According to Regmi et al. (2014), landslides usually occur along cut roads and 

road construction processes that damage the natural conditions of the slopes. In this indicator, the distance of 

the research area from the station causes the minimum classification it can obtain. On the distance from the road 

indicator, the highest probability is the shortest distance, specifically 0-100 with FR=0.43, while the lowest 

probability is at distances of 340-450 and >450 with FR=0.06 and 0.08. Distance From Drainage indicator, the 

highest probability is at a distance of 1000-2500 with FR = 0.35, while the lowest probability is at a distance > 

4500 with FR = 0.08, which indicates that the frequency of landslides decreases with increasing distance to the 

drainage canal and can be attributed to the fact that during rainstorms the groundwater level will rise and the 

initiation of landslides is affected by the modified terrain conditions by ditch erosion (Dai & Lee, 2001). 

 The landslide vulnerability map on the Taba Penanjung – Kepahiang Cross Road is divided into five areas 

landslide areas with low, medium, high, and very high vulnerability. The results show that a very low 

classification has an area of 8% of the total area, low and high with an area of 25%, medium with 28%, and very 

high with 14%. As shown in Figure 13  the final map of the landslide vulnerability mapping shows that the area 

symbol in red is for an area very prone to landslides. In contrast, the area with a blue sign indicates that the area 

has a very low landslide vulnerability. This research aims to identify landslide vulnerability in road areas through 

correlation analysis between images and diagrams. Image diagram in Figure 12, displays a comprehensive 

presentation of the area in Figure 13 where the red area indicates a high level of vulnerability to landslides, 

supported by the significant frequency of landslides in that area. Meanwhile, the blue area is considered safe 

against landslides, even though it has experienced such events because the factors that have been identified 

indicate a low level of vulnerability. 

 

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52 

Table 3. FR Value Calculation on Factor Classification 

Factor Classification  Classified 
Area/Km2 

Proportion of 
Classified 
Area/% 

Number of 
Landslide 
Points/pts 

Proportion of the 
Number of Landslide 

Points/% 

Density of Landslide 
Points/(pts/km2) 

Frequency 
Ratio (FR) 

Aspect (-1) 
(-1)-22.5 
22.5-67.5 
67.5-112.5 

112.5-157.5 
157.5-202.5 
202.5-247.5 
247.5-292.5 
292.5-337.5 

7070 
11162 
13282 
22373 
30960 
28036 
25426 
25063 
9923 

4.07 
6.44 
7.66 

12.91 
17.86 
16.17 
14.67 
14.46 
5.72 

960 
2496 
1088 
960 

1856 
3776 
3648 
3840 
3264 

4.38 
11.40 

4.97 
4.38 
8.47 

17.25 
16.66 
17.54 
14.91 

1.08 
1.77 
0.65 
0.34 
0.47 
1.07 
1.14 
1.21 
2.60 

0.10 
0.17 
0.06 
0.03 
0.05 
0.10 
0.11 
0.12 
0.25 

Curvature -319-0 
0 

0-268 

31293 
97435 
46971 

17.81 
55.45 
26.73 

4928 
10368 
7040 

22.06 
46.41 
31.51 

1.24 
0.84 
1.18 

0.38 
0.26 
0.36 

Elevation 112-150 
250-450 
450-650 
650-850 
850-918 

40348 
28087 
23581 
41270 
42413 

22.96 
15.98 
13.42 
23.48 
24.13 

2432 
4864 
6528 
5440 
3072 

10.88 
21.77 
29.22 
24.35 
13.75 

0.47 
1.36 
2.18 
1.04 
0.57 

0.08 
0.24 
0.39 
0.18 
0.10 

Distance 
From 
Fault 

<400 
400-800 
800-1200 

>1200 

63850 
57157 
39505 
15172 

36.34 
32.53 
22.48 
8.63 

5760 
6464 
9472 
448 

26.01 
29.19 
42.77 

2.02 

0.72 
0.80 
1.18 
0.06 

0.26 
0.29 
0.43 
0.02 

Plan 
Curvature 

(-13) -(-10) (-
10)-0 
0-12 

33273 
96843 
45511 

18.94 
55.14 
25.91 

3968 
12480 
5888 

17.76 
55.87 
26.36 

0.94 
1.01 
1.02 

0.32 
0.34 
0.34 

Profile 
Curvature 

(-15) -(-10) 
(-10)-0 

0-5 
5-10 

10-20 

8724 
44124 
81519 
36651 
4681 

4.96 
25.11 
46.39 
20.86 
2.66 

2112 
5568 
8128 
5184 
1344 

9.45 
24.92 
36.38 
23.20 

6.01 

1.90 
0.99 
0.78 
1.11 
2.26 

0.27 
0.14 
0.11 
0.16 
0.32 

Slope (o) 4o-8o 

8o-16o 

16o-35o 

35o-55o >55o 

15990 
45271 
99830 
12120 

84 

9.22 
26.12 
57.60 
6.99 
0.04 

64 
3328 

15104 
3328 

64 

0.29 
15.20 
69.00 
15.20 

0.29 

0.03 
0.58 
1.20 
2.17 
6.03 

0.003 
0.06 
0.12 
0.22 
0.60 

SPI (-10) -(-6) 
(-6) -(-2) 

(-2)-2 
2-6 
6-7 

6261 
18724 
60076 
71244 
19394 

3.56 
10.65 
34.19 
40.54 
11.03 

960 
2432 
3712 

10112 
5120 

4.29 
10.88 
16.61 
45.27 
22.92 

1.21 
1.02 
0.49 
1.12 
2.08 

0.20 
0.17 
0.08 
0.19 
0.35 

NDVI 1300-1500 
1500-1700 
1700-1900 
1900-2100 
2100-2685 

27087 
37659 
37766 
44657 
28515 

15.41 
21.43 
21.49 
25.41 
16.23 

4864 
6080 
6400 
4928 

0 

21.83 
27.29 
28.73 
22.12 

0 

1.42 
1.27 
1.34 
0.87 

0 

0.29 
0.26 
0.27 
0.18 

0 

Drainage 
Density 

0-0.5 
0.5-1 
1-1.5 
1.5-2 

2-2.91 

142290 
11577 
9443 
9817 
2567 

80.98 
6.58 
5.37 
5.58 
1.46 

18432 
3712 
128 

0 
0 

82.75 
16.66 

0.57 
0 
0 

1.02 
2.53 
0.11 

0 
0 

0.28 
0.69 
0.03 

0 
0 

TWI 1.7-8 
8-16 

16-20 

105265 
59795 
10639 

59.91 
34.03 
6.05 

13952 
8128 
256 

62.46 
36.38 

1.14 

1.04 
1.07 
0.19 

0.45 
0.46 
0.08 

Vs30 360-760 
760-1500 

17861 
157865 

10.16 
89.83 

4032 
18176 

18.15 
81.84 

1.79 
0.91 

0.66 
0.34 

Lithology 1 
2 

162323 
13348 

92.37 
7.59 

20480 
1728 

92.21 
7.78 

1.00 
1.02 

0.49 
0.51 

Rainfall 2500-2800 
2800-3165 

119300 
56391 

67.90 
32.09 

16000 
6272 

71.83 
28.16 

1.06 
0.88 

0.55 
0.45 

Distance 
From 
Road 

0 - 100 
100 - 250 
250 - 350 
350 - 450 

>450 

45297 
37072 
32586 
31378 
29156 

25.81 
21.12 
18.56 
17.88 
16.61 

11136 
3328 
5184 
1088 
1344 

50.43 
15.07 
23.47 

4.92 
6.08 

1.95 
0.71 
1.26 
0.28 
0.37 

0.43 
0.16 
0.28 
0.06 
0.08 

Distance 
From 
Drainage 

0 - 1000 
1000 - 2500 
2500 - 3500 
3500 - 4500 

>4500 

57274 
32863 
27695 
25365 
32292 

32.63 
18.72 
15.78 
14.45 
18.40 

6400 
7360 
2176 
4480 
1664 

28.98 
33.33 

9.85 
20.28 

7.53 

0.89 
1.78 
0.62 
1.40 
0.41 

0.17 
0.35 
0.12 
0.27 
0.08 

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53 

 
Source: Analysis, 2022 

Figure 12. Diagram of Comparison of Landslide Susceptibility Area in (%) 

In this research, focus is given to a relatively small research area that has a high probability of landslides. 

This aims to provide a more specific analysis compared to previous research conducted at the sub-district or 

district level. By utilizing ML method, this research is one of the first to apply this approach to a highway area 

that frequently experiences landslides, including the 12 most frequently landslide areas, according to the 

Regional Disaster Management Agency (BPBD). 

 
   Source: Analysis, 2022 

Figure 13. Landslide Susceptibility Mapping 

In this research we have been successfully implementing the ML approach to access and to map landslide 

vulnerability along Taba Penanjung Kepahiang road. Previously The ML has been widely used in landslide 

mapping in several regions in Indonesia, such as Aldiansyah & Wardani (2024), Darminto et al. (2021) and 

Irawan et al., (2021). Previous researches used ML with different algorithms, with almost the same input 

parameters. The broad field of study makes it easier to process data with good output, which is different from 

this research. This research only focuses on the road area, so it only maximizes the FR algorithm, but the results 

of this research can be used in the landslide mitigation process. Finally, our results can also become a reference 

for the government and stakeholders in regional development, planning, and disaster management. It is also 

hoped that the resulting mapping can become an effective pre-disaster tool for carrying out specific and optimal 

mitigation in the area so that losses due to landslides can be minimized. It is hoped that this research can 

significantly contribute to efforts to prevent and manage disasters in highway areas that are vulnerable to 

landslides.  

4. Conclusion 

Landslides are described as rock or soil movements influenced by various factors, causing economic and 

social losses. The Bengkulu-Kepahiang route has been identified as vulnerable due to diverse geomorphological 

conditions and landslides that disrupt road access and cause casualties The lack of alternative ways to advertise 

the impact of landslides on transportation has prompted the need for research to analyze and determine the 

dangers of landslides. This research aims to identify areas prone to landslides using ML as an effective disaster 

mitigation tool. ML models are essential for landslide modeling (Purwanto, 2021), because they provide accurate 

vulnerability maps by combining several input parameters. The study area of the Taba Penanjung-Kepahiang 

Road has varying levels of vulnerability to landslides caused by several factors such as altitude, geological 

conditions, slope aspect, rainfall, curvature of the land, and distance from the fault. This research uses the FR 

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54 

method to map landslide-prone areas, emphasizing the need for a holistic approach to handling and preventing 

landslides. Vulnerability analysis integrates various factors, including topography, lithology, vegetation, 

distance from roads, and drainage characteristics. The Landslide Susceptibility Index (LSI) is calculated by 

adding up the FR values for each factor thereby contributing to developing a comprehensive Disaster Mitigation 

Model using ML. 

Mapping landslide-prone areas can use a ML method approach with Variable FR to help find the spatial 

relationship between landslide events and conditioning factors extracted using the weights of each class of each 

conditioning factor with an area. Data processing uses 16 independent and dependent factors as historical points 

in the occurrence of landslides in the last five years. Data processing uses pixel or grid methods with a specific 

grid size for each factor attribute value of the ML model. Modeling-based representation of slope stability 

requires several indicators involving local characterization of landslide geometry and internal structure. The 

data processing results are a landslide hazard map with five classifications: very low, low, medium, high, and 

very high (very vulnerable). This research is still dominated by medium areas, precisely 28%. For future research, 

adding more parameters with more detailed classification and higher correlation can make the output results 

more accurate. This study emphasizes the importance of multidimensional landslide mitigation strategies, 

including sustainable land use planning, reforestation, early warning systems, and advanced technologies. The 

research aims to reduce the risk and impact of landslides, optimize transportation access, and increase community 

awareness and preparedness This research contributes valuable information to designing risk mitigation 

programs, facilitating rapid response to emergencies, ensuring safer road traffic, and minimizing material and 

non-material impacts. 

5. Acknowledgments 

 The authors would like to thank the Geohazards and Climate Change (GCC) Laboratory, Department of 

Physics, Bengkulu University, for technical assistance; the Geospatial Information Agency (BIG); the Bengkulu 

Province Regional Disaster Management Agency (BPBD); and the Sumatra VII River Basin Agency, for 

providing data in the study area for analysis; and the Ministry of Education, Culture, Research and Technology 

for support in the form of Student Creativity Program funds funded through the PKM-RE scheme. Most of the 

figures were generated by QGIS (QGIS Development Team, 2022). 

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