FUZZY LOGIC APPROACH TO FOREST FIRE RISK ASSESSMENT IN TANJUNG PUTTING NATIONAL PARK

  • Rani Natalia Purba Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara, Indonesia https://orcid.org/0009-0009-9669-5751
  • Esther Sorta Mauli Nababan Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara, Indonesia https://orcid.org/0000-0001-7346-1500
  • Prana Ugiana Gio Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara, Indonesia https://orcid.org/0000-0002-5916-0653
  • Zahedi Zahedi Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara, Indonesia https://orcid.org/0000-0002-5579-7024
  • Muhammad Romi Syahputra Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara, Indonesia https://orcid.org/0000-0003-3557-0778
DOI: https://doi.org/10.30598/barekengvol20iss2pp1583-1598
Keywords: Decision making, Early warning, Fuzzy Sugeno, Forest fire, Risk identification

Abstract

In implementing fuzzy logic, the Sugeno fuzzy method faces several challenges, such as issues in determining the fuzzy rule base and the occurrence of undefined outputs (defuzzification) with values of 0/0. This study examines the application of the Sugeno fuzzy method in identifying the level of forest fire risk by considering various variables. The variables are temperature, humidity, and wind speed. The model is developed using fuzzy rules constructed based on the relationships among the variables. The test results show that after modifying the membership function boundaries to decimal values approaching the original lower bounds, the Zero-Order Sugeno fuzzy method can produce an average forest fire risk level of 68.83 (high category) in Tanjung Puting National Park. In addition, applying the First-Order Sugeno fuzzy method produces a multiple linear regression model that can be applied within the rule base, resulting in an average forest fire risk level of 68.89 (high category) at the same location. During the evaluation phase, the First-Order Sugeno model achieved a lower RMSE value (15.47) than the Zero-Order model (16.03), indicating that it is more suitable for handling extreme conditions such as dangerous spikes in risk. Therefore, this approach has the potential to serve as an effective early warning system for forest fire mitigation, supporting decision-making processes.

 

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References

N. Ionuț , C. Delcea and N. Chiriță, "MATHEMATICAL PATTERNS IN FUZZY LOGIC AND ARTIFICIAL INTELLIGENCE FOR FINANCIAL ANALYSIS: A BIBLIOMETRIC STUDY," Mathematics, vol. 12, no. 5, p. 782, 2024, doi: https://doi.org/10.3390/math12050782.

M. Negnevitsky, "THE HISTORY OF ARTIFICIAL INTELLIGENCE OR FROM THE "DARK AGES" TO THE KNOWLEDGE-BASED SYSTEMS," WIT Transactions on Information and Communication Technologies, vol. 19, no. 1, pp. 1-21, 2024, doi: https://doi.org/10.2495/AI970381.

D. Vinsensia, "ANALISIS KINERJA PELAYANAN KESEHATAN DENGAN PENDEKATAN LOGIKA FUZZY SUGENO," Jurnal Media Informatika, vol. 2, no. 2, pp. 62-73, 2021, doi: https://doi.org/10.55338/jumin.v2i2.695.

N. N. Lyimo, Z. Shao, A. M. Ally, N. Y. D. Twumasi, O. Altan and C. A. Sanga, "A FUZZY LOGIC-BASED APPROACH FOR MODELLING UNCERTAINTY IN OPEN GEOSPATIAL DATA ON LANDFILL SUITABILITY ANALYSIS," Journal of Marketing Analytics, vol. 9, no. 12, p. 737, 2020, doi: https://doi.org/10.3390/ijgi9120737.

D. Farhan and F. Sulianta, "IMPLEMENTATION OF FUZZY TSUKAMOTO LOGIC TO DETERMINE THE NUMBER OF SEEDS KOI FISH IN THE SUKAMANAH CIANJUR FARMERS GROUP," Jurnal Teknik Informatika (Jutif), vol. 4, no. 1, pp. 187-198, 2023, doi: https://doi.org/10.52436/1.jutif.2023.4.1.477.

K. Muflihunna and Mashuri, "PENERAPAN METODE FUZZY MAMDANI DAN METODE FUZZY SUGENO DALAM PENENTUAN JUMLAH PRODUKSI," UNNES Journal of Mathematics, vol. 11, no. 1, pp. 22-37, 2022, doi: https://doi.org/10.15294/ujm.v11i1.50060.

K. S. T. R. Alves, C. D. de Jesus and E. . P. de Aguiar, "A NEW TAKAGI-SUGENO-KANG MODEL TO TIME SERIES FORECASTING," Engineering Applications of Artificial Intelligence, vol. 133, no. Engineering Applications of Artificial Intelligence, p. 108155, 2024, doi: https://doi.org/10.1016/j.engappai.2024.108155.

V. YASIN, Z. AZMI, I. JUNAEDI, A. ZULKARNAIN SIANIPAR, I. RIRIS IMMASARI and M. OCTAVIA, "DISASTER CONTROL SYSTEM FOR LANDSLIDES USING SUGENO FUZZY ALGORITHM," J. Theor. Appl. Inf. Technol, vol. 102, no. 6, 2024.

N. S. Muhtadi and D. M. Rahman, "ANALISIS RISIKO KEBAKARAN HUTAN DENGAN LOGIKA FUZZY MAMDANI," Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 1, 2024, doi: https://doi.org/10.23960/jitet.v12i1.3902.

S. Budiyanto, L. M. Silalahi, F. A. Silaban, U. Darusalam, S. Andryana and I. F. Rahayu, "OPTIMIZATION OF SUGENO FUZZY LOGIC BASED ON WIRELESS SENSOR NETWORK IN FOREST FIRE MONITORING SYSTEM," 2020 2nd International Conference on Industrial Electrical and Electronics (ICIEE), pp. 126-134, 2020, doi: https://doi.org/10.1109/ICIEE49813.2020.9277365.

S. Tuhpatussania, S. Erniwati and Z. Mutaqin, "PERBANDINGAN METODE AGGLOMERATIVE HIERARCHICAL CLUSTERING DAN METODE KMEDOIDS DALAM PENGELOMPOKAN DATA TITIK PANAS KEBAKARAN HUTAN DI INDONESIA," Journal Computer and Technology, vol. 2, no. 1, pp. 21-38, 2024, doi: https://doi.org/10.69916/comtechno.v2i1.146

C. Fuchs, S. Spolaor and M. Nobile, "PYFUME: A PYTHON PACKAGE FOR FUZZY MODEL ESTIMATION," in 2020 IEEE international conference on fuzzy systems (FUZZ-IEEE), Glasgow, 2020, doi: https://doi.org/10.1109/FUZZ48607.2020.9177565.

A. Sahour, F. Boumehrez, F. Maamri, H. Djellab and B. Abdelali, "FOREST FIRE RISK MONITORING USING FUZZY LOGIC AND IOT TECHNOLOGY," Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 12, no. 2, pp. 281-290, 2024, doi: https://doi.org/10.52549/ijeei.v12i2.5242.

M. A. Hafiz, "PENERAPAN LOGIKA FUZZY SUGENO UNTUK OPTIMASI STOK BIJI KOPI PADA KAFE ROOSTER," Jurnal Fasilkom, vol. 13, no. 2, pp. 165-172, 2023, doi: https://doi.org/10.37859/jf.v13i02.5460.

D. Upuy and A. H. Hiariey, "IMPLEMENTASI FUZZY SUGENO UNTUK MENENTUKAN JUMLAH PRODUKSI TAHU," Jurnal Teknologi Informasi dan Terapan, vol. 10, no. 2, pp. 91-94, 2023, doi: https://doi.org/10/25047/jtit.v10i2.

R. Apriliana, A. Damayanti and A. B. Pratiwi, "SISTEM PAKAR DIAGNOSA HIPERTIROID MENGGUNAKAN CERTAINTY FACTOR DAN LOGIKA FUZZY," Contemporary Mathematics and Applications (ConMathA), vol. 2, no. 1, p. 57, 2020.

M. Abdy, "PENGGUNAAN BILANGAN FUZZY SEGITIGA PADA PERBANDINGAN KEMAMPUAN PROSES," Jurnal Matematika, Statistika dan Komputasi, vol. 14, no. 2, pp. 137-142, 2018, doi: https://doi.org/10.20956/jmsk.v14i2.3552.

S. Hajar, M. Badawi, Y. D. Setiawan, M. N. H. Siregar and A. P. Windarto, "PREDIKSI PERHITUNGAN JUMLAH PRODUKSI TAHU MAHANDA DENGAN TEKNIK FUZZY SUGENO," J-SAKTI (Jurnal Sains Komputer dan Informatika), vol. 4, no. 1, pp. 210-219, 2020, doi: https://doi.org/10.30645/j-sakti.v4i1.200.

B. Rahman, T. Mantoro, S. Andryana and S. B. Wicaksono, "OPTIMIZING PLANT WATERING EFFICIENCY VIA IOT: FUZZY SUGENO METHOD WITH ESP8266 MICROCONTROLLER," TEM Journal, vol. 13, no. 3, p. 1849, 2024.

D. Jatikusumo and R. R. Hidayat, "OPTIMASI PENENTUAN LOKASI BENCANA ALAM DENGAN REGRESI LINIER SEDERHANA DAN BERGANDA," Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3S1, 2024, doi: https://doi.org/10.23960/jitet.v12i3S1.5257, doi: https://doi.org/10.23960/jitet.v12i3S1.5257.

H. Timothy O, "ROOT MEAN SQUARE ERROR (RMSE) OR MEAN ABSOLUTE ERROR (MAE): WHEN TO USE THEM OR NOT," Geoscientific Model Development Discussions, vol. 2022, pp. 1-10, 2022, doi: https://doi.org/10.5194/gmd-15-5481-2022.

Published
2026-01-26
How to Cite
[1]
R. N. Purba, E. S. M. Nababan, P. U. Gio, Z. Zahedi, and M. R. Syahputra, “FUZZY LOGIC APPROACH TO FOREST FIRE RISK ASSESSMENT IN TANJUNG PUTTING NATIONAL PARK”, BAREKENG: J. Math. & App., vol. 20, no. 2, pp. 1583-1598, Jan. 2026.
Issue
Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2026 Rani Natalia Purba, Esther Sorta Mauli Nababan, Prana Ugiana Gio, Zahedi Zahedi, Muhammad Romi Syahputra

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