Fuzzy C-Means with Borda Algorithm in Cluster Determination System for Food Prone Areas in Aceh Utara

Mutammimul Ula(1*); Munirul Ula(2); Desvina Yulisda(3); Susanti Susanti(4);

(1) Universitas Malikussaleh
(2) Universitas Malikussaleh
(3) Universitas Malikussaleh
(4) Universitas Malikussaleh
(*) Corresponding Author



In this research, the clustering of food prone areas in Aceh Utama is based on the Index Ketahanan Pangan (IKP) indicators compiled by Badan Ketahanan Pangan (BKP) using Fuzzy C-Means (FCM) and Borda algorithms. The fuzzy C-Means algorithm was used to classify food-prone areas with three clusters: very prone, moderately prone, and prone. The Borda algorithm was used to choose the most prone area from very prone clusters, which are considered urgently to be followed up by decision-makers. Based on the research results, it was found that in the aspect of food availability, four sub-districts are moderately prone, 10 are prone, and 13 are very prone. Regarding food affordability, it found that 12 sub-districts are moderately prone, seven are prone, and eight are very prone. Regarding food utilization, one sub-district is moderately prone, three are prone, and 23 are very prone. The results of voting using the Borda algorithm in very prone clusters are obtained Sawang District from the aspect of food availability, Syamtalira Aron District from the aspect of food affordability, and Lapang District from the aspect of food utilization. The clustering system is built based on the web using the PHP programming language.


Food Prone Areas, Clustering, Fuzzy C-Means, Borda, Website


Full Text:


Article Metrics

Abstract view: 195 times
PDF view: 102 times

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v15i1.1481.21-31



M. Ariani, "Penguatan ketahanan pangan daerah untuk mendukung ketahanan pangan nasional." Pusat Analisis dan Kebijakan Pertanian. Bogor, 2007.

R. K. Dinata, N. Hasdyna, S. Retno, "K-means algorithm for clustering system of plant seeds specialization areas in east Aceh." ILKOM Jurnal Ilmiah, 13(3), 235-243, 2021.

D. L. Rahakbauw, V. Y. I. Ilwaru, "Implementasi Fuzzy C-Means Clustering dalam penentuan beasiswa." Barekeng: Jurnal Ilmu Matematika dan Terapan, 11(1), 1-12, 2017.

J. Salat, S. Achmady, "Minimalisasi distorsi dari segmentasi citra metode otsu menggunakan Fuzzy Clustering", ILKOM Jurnal Ilmiah, 10(1), 80-85, 2018.

N. Nurjanah, F. Andi, F. Indriani, "Implementasi metode Fuzzy C-Means pada sistem clusteringdata varietas padi." KLIK-Kumpulan Jurnal Ilmu Komputer, 1(1), 23-32, 2017.

E. Rouza, L. Fimawahib, "Implementasi Fuzzy C-Means clustering dalam pengelompokan UKM di Kabupaten Rokan Hulu." Techno. Com 19(4), 481-495, 2020.

W. Suwarso, “Application of Fuzzy C-Means Clustering Method using Matlab To Map the Potential of Rice Plant in Bekasi Regency”. Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data), 1(2), 93-103, 2018.

P. Valsalan, P. Sriramakrishnan, S. Sridhar, "Knowledge based fuzzy c-means method for rapid brain tissues segmentation of magnetic resonance imaging scans with CUDA enabled GPU machine." Journal of Ambient Intelligence and Humanized Computing, 1-14, 2020.

H. Kumar, I. Tyagi, "Implementation and comparative analysis of k-means and fuzzy c-means clustering algorithms for tasks allocation in distributed real time system," International Journal of Embedded and Real-Time Communication Systems (IJERTCS), 10(2), 66-86, 2019.

S. Askari, "Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development", Expert Systems with Applications, 165, 113856, 2021.

A. Parlina, K. Ramli, H. Murfi, "Parlina, A., Ramli, K., & Murfi, H. (2021). Exposing emerging trends in smart sustainable city research using deep autoencoders-based fuzzy c-means", Sustainability, 13(5), 2876, 2021.

J. Zhou, W. Pedrycz, X. Yue, C. Gao, Z. Lai, J. Wan, "Projected fuzzy C-means clustering with locality preservation. Pattern Recognition, 113, 107748, 2021.

M. Orouskhani, D. Shi, X. Cheng, "A fuzzy adaptive dynamic NSGA-II with fuzzy-based borda ranking method and its application to multimedia data analysis", IEEE Transactions on Fuzzy Systems, 29(1), 118-128, 2020.

S. Panja, S. Bag, F. Hao, B. Roy, "A Smart contract system for decentralized borda count voting." IEEE Transactions on Engineering Management, 67(4), 1323-1339, 2020.

H. Liao, X. Wu, X. Mi, F. Herrera, "An integrated method for cognitive complex multiple experts multiple criteria decision making based on ELECTRE III with weighted Borda rule. Omega, 93, 102052, 2020.


  • There are currently no refbacks.

Copyright (c) 2023 Mutammimul Ula, Munirul Ula, Desvina Yulisda, Susanti

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