Adaptive Neuro-Fuzzy Inference System (ANFIS) Method for Developing a Decision Support System for Determining Landslide Susceptibility

Main Article Content

Dede Sukmawan
Muchtar Ali Setyo Yudono
Danang Purwanto
Dio Damas Permadi
Anang Suryana
Utamy Sukmayu Saputri
Marina Artiyasa


Landslide catastrophes are one of the disasters that frequently occur in Indonesia owing to the weather and climatic features, regional terrain, and geological formations that make this nation prone to landslides. The primary goal of this research is to compare the application of the fuzzy logic technique and the adaptive neuro-fuzzy inference system (ANFIS) approach to landslide detection sensors based on prior research in order to identify landslide-prone locations more easily. The Adaptive Neuro-Fuzzy Inference System (ANFIS) technique analyzes the landslide area using three factors. Rainfall, land slope, and soil moisture are examples of these factors. This variable is used to assess the area's level of vulnerability to landslides: very safe, relatively safe, relatively potential, potential, and very potential. In the study, each piece of data is subjected to a training and testing procedure to identify landslide vulnerability, with the factors and weighting methods aligned with current government standards. This study compares the rules outcomes to those of past studies as well as the system results. Based on the studies findings, it can be stated that the decision support system for the degree of landslide vulnerability utilizing the ANFIS approach is superior to the fuzzy logic method, with an accuracy rate of 86.21%.

Article Details

How to Cite
D. Sukmawan, “Adaptive Neuro-Fuzzy Inference System (ANFIS) Method for Developing a Decision Support System for Determining Landslide Susceptibility”, Fidelity, vol. 5, no. 1, pp. 1-8, Jan. 2023.
Received 2022-10-08
Accepted 2022-12-05
Published 2023-01-31


[1]    V. Barrile, F. Cirianni, G. Leonardi, and R. Palamara, “A Fuzzy-based Methodology for Landslide Susceptibility Mapping,” in Procedia - Social and Behavioral Sciences, 2016, vol. 223, pp. 896–902, doi: 10.1016/j.sbspro.2016.05.309.

[2]    G. Leonardi, R. Palamara, and F. Suraci, “A fuzzy methodology to evaluate the landslide risk in road lifelines,” in AIIT 2nd International Congress on Transport Infrastructure and Systems in a changing world (TIS ROMA 2019), 2020, vol. 45, no. 2019, pp. 732–739, doi: 10.1016/j.trpro.2020.02.104.

[3]    G. Leonardi, R. Palamara, and F. Cirianni, “Landslide Susceptibility Mapping Using a Fuzzy Approach,” in World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium 2016, WMCAUS 2016, 2016, vol. 161, pp. 380–387, doi: 10.1016/j.proeng.2016.08.578.

[4]    D. Dhianaufal, T. H. W. Kristyanto, T. L. Indra, and R. Syahputra, “Fuzzy logic method for landslide susceptibility mapping in volcanic sediment area in Western Bogor,” in AIP Conference Proceedings, 2018, vol. 2023, no. October 2018, doi: 10.1063/1.5064187.

[5]    N. D. R. Sugianti, I. B. K. Widiartha, and A. Y. Husodo, “Prototype Early Warning System Tanah Longsor Menggunakan Fuzzy Logic Berbasis Google Maps,” J-Cosine, vol. 3, no. 2, pp. 154–161, 2019, doi:

[6]    F. Fianti, O. D. Rahayuningsih, N. P. Aryani, and I. Yulianti, “Fuzzy logic for landslide susceptibility level in kecamatan Ungaran Barat,” in 6th International Conference on Mathematics, Science, and Education (ICMSE 2019), 2019, vol. 1567, no. 4, pp. 1–4, doi: 10.1088/1742-6596/1567/4/042095.

[7]    B. S. Budianto, M. Y. Jarwadi Purwanto, W. Widiatmaka, and L. B. Prasetyo, “Penerapan Metoda Fuzzy Dalam Klasifikasi Lahan Kritis Berbasis Hydraulic Response Unit (Hru) Subdas Cisangkuy,” J. Pengelolaan Sumberd. Alam dan Lingkung. (Journal Nat. Resour. Environ. Manag., vol. 8, no. 1, pp. 120–126, 2018, doi: 10.29244/jpsl.8.1.120-126.

[8]    M. Idhom, F. T. Anggraeny, G. S. Budiwitjaksono, Z. A. Achmad, and Munoto, “Soil Movement Monitoring System Based on IoT using Fuzzy Logic,” Int. J. Data Sci. Eng. Anaylitics, vol. 1, no. 2, pp. 63–73, 2021, doi: 10.33005/ijdasea.v1i2.14.

[9]    E. P. Utomo, “Landslide Hazard Assesment on Java Island,” JSD.Geol, vol. 23, no. 1, pp. 13–29, 2013, doi:

[10]    R. T. Saputra, S. R. Utami, and C. Agustina, “Hubungan Kemiringan Lereng Dan Persentase Batuan Permukaan Terhadap Longsor Berdasarkan Hasil Simulasi,” J. Tanah dan Sumberd. Lahan, vol. 9, no. 2, pp. 339–346, 2022, doi: 10.21776/ub.jtsl.2022.009.2.14.

[11]    N. Herawadi Sudibyo, M. Ridho, K. Kunci, S. Cahaya, B. Alam, and P. Dini, “Pendeteksi Tanah Longsor Menggunakan Sensor Cahaya,” J. TIM Darmajaya, vol. 01, no. 02, pp. 218–227, 2015.

[12]    J. F. Schilter, “Identifying Key Factors of Affecting Translational Landslides in Part of the Yakima Fold and Thrust Belt, Wahington State,” 2019, doi: 10.1130/abs/2019cd-329064.

[13]    M. Capitani, A. Ribolini, and M. Bini, “Susceptibility to translational slide-type landslides: Applicability of the main scarp upper edge as a dependent variable representation by reduced chi-square analysis,” Int. J. Geo-Information, vol. 7, no. 9, pp. 1–15, 2018, doi: 10.3390/ijgi7090336.

[14]    A. Basofi, A. Fariza, and M. R. Dzulkarnain, “Landslides susceptibility mapping using fuzzy logic: A case study in Ponorogo, East Java, Indonesia,” in Proceedings of 2016 International Conference on Data and Software Engineering, ICoDSE 2016, 2017, pp. 0–6, doi: 10.1109/ICODSE.2016.7936156.

[15]    M. D. Wardhana, A. Sofwan, and I. Setiawan, “Fuzzy Logic Method Design for Landslide Vulnerability,” in The 4th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2019), 2019, vol. 3004, no. 201 9, pp. 1–6, doi:

Most read articles by the same author(s)

1 2 > >>