PERBANDINGAN FUZZY C-MEANS DAN K-MEANS PADA KLASTERISASI BAWANG MERAH
Article Sidebar
Read Counter : 6
Download : 1
Main Article Content
Abstract
Penelitian ini bertujuan untuk membandingkan performa algoritma Fuzzy C-Means (FCM) dan K-Means dalam klasterisasi data produksi bawang merah di Indonesia. Pendekatan yang digunakan adalah machine learning berbasis unsupervised clustering, dengan evaluasi kinerja berdasarkan tiga metrik utama: Silhouette Score, Davies-Bouldin Index, dan waktu komputasi. Data produksi dianalisis secara spasial menggunakan koordinat geografis kabupaten/kota dan divisualisasikan dalam bentuk peta klaster dan grafik performa. Hasil eksperimen menunjukkan bahwa FCM memberikan hasil klasterisasi yang lebih akurat dan stabil, terutama pada wilayah dengan karakteristik produksi yang tumpang tindih, sedangkan K-Means lebih unggul dari segi efisiensi waktu komputasi. Visualisasi spasial mengungkapkan pola distribusi produksi yang timpang, dengan Pulau Jawa mendominasi klaster produksi tinggi. Temuan ini menunjukkan bahwa metode klasterisasi cerdas dapat menjadi solusi potensial dalam pengembangan sistem informasi geografis pertanian yang adaptif dan berbasis data.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This work is licensed under a Jurnal Komunikasi Creative Commons Attribution-ShareAlike 4.0 International License.
References
[1] Badan Pusat Statistik, Statistik Hortikultura Indonesia 2023. BPS Indonesia, 2023.
[2] A. R. Rohmatullah, D. Rahmalia, and M. S. Pradana, “Implementasi K-Means dan Fuzzy C-Means untuk Klasterisasi Pertanian di Lamongan,” Jurnal Ilmiah Teknosains, vol. 5, no. 2, pp. 86–93, 2020.
[3] M. Rivai, N. Frasiska, and Rosni, “Analysis of Fuzzy C-Means Clustering Method on Grouping Provinces in Indonesia Based on Economic Growth in 2023,” Int J Sci Res Sci Eng Technol, vol. 11, no. 6, pp. 380–388, 2024.
[4] B. Choudhary and V. Saxena, “Clustering of Agriculture Data through Fuzzy C Means Technique,” African Journal of Biological Sciences, vol. 6, no. 13, pp. 3312–3329, 2024.
[5] F. A. Putra and E. D. Kurniawan, “Perbandingan K-Means dan Fuzzy C-Means pada Klasterisasi Produksi Tanaman Pangan,” Jurnal Ilmu Komputer dan Informasi, vol. 10, no. 3, pp. 174–181, 2021.
[6] R. A. Ningtyas, Y. N. Nasution, and Syaripuddin, “Pengelompokan Wilayah di Kalimantan dengan Fuzzy C-Means,” Jurnal Eksponensial Statistika, vol. 13, no. 2, pp. 141–146, 2022.
[7] B. Choudhary and V. Saxena, “Clustering of Agriculture Data through Fuzzy C‐Means Technique,” African Journal of Biological Sciences, vol. 6, no. 13, pp. 3312–3329, 2024.
[8] F. A. Adhzima, Y. Arkeman, and I. Hermadi, “The Clustering Rice Plant Diseases Using Fuzzy C‐Means and Genetic Algorithm,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 2, pp. 280–285, 2021.
[9] M. T. A. Rahman and M. Hasan, “Hybrid FCM Algorithm Using PSO for Agricultural Classification,” International Journal of Computing and Digital Systems, vol. 10, no. 1, pp. 75–82, 2021.
[10] S. C. Chang, W. C. Chuang, and J. T. Jeng, “New Interval Improved Fuzzy C-Means Clustering Algorithms under Different Distance Measures,” Applied Sciences, vol. 13, no. 22, p. 12531, 2023.
[11] A. Pickens and S. Sengupta, “Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, FCM, and Evolutionary Neural Networks,” Mach Learn Knowl Extr, vol. 3, no. 3, pp. 695–719, 2021.
[12] Y. Zhao, J. Wang, and H. Liu, “An Improved K-Means Clustering Algorithm Based on Density,” IEEE Access, vol. 9, pp. 117129–117138, 2021.
[13] M. I. Aslam, S. Khan, M. Imran, K. Mehmood, and R. Alotaibi, “Clustering Techniques in Precision Agriculture: A Review,” Sustainability, vol. 13, no. 14, p. 7680, 2021.
[14] N. Patel and M. Shah, “Performance Comparison of K-Means and K-Medoids on Agricultural Datasets,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 3, pp. 566–573, 2021.
[15] M. Verma, R. Sharma, and A. K. Verma, “Agricultural Land Use Classification Using K-Means Clustering,” International Journal of Environment and Agriculture Research, vol. 6, no. 9, pp. 10–18, 2020.
[16] A. K. Singh, S. Kumar, and P. N. Singh, “Enhancing K-Means Clustering Using Hybrid Metaheuristic Techniques,” Soft comput, vol. 28, no. 2, pp. 1029–1045, 2023.
[17] C. Huang, B. Li, and F. Wu, “Smart Agriculture Applications Using K-Means Based Data Segmentation,” Information Processing in Agriculture, vol. 9, no. 2, pp. 341–350, 2022.
[18] T. R. Almutairi and A. Z. Shaout, “A Review on K-Means Variants for Big Data Clustering,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 5, pp. 1971–1983, 2022.
[19] S. Tuslaela, R. Rusdiansyah, H. Supendar, and N. Suharyanti, “Implementation of K-Means Clustering in Food Security by Regency in East Java Province in 2022,” Sinkron: Jurnal Penelitian dan Pengabdian kepada Masyarakat, vol. 9, no. 1, pp. 54–60, 2024.
[20] W. Kusuma, A. Setiawan, and A. Prasetyo, “Comparison of k-means clustering with hierarchical agglomerative clustering for the analysis of food security of rice sector in Indonesia,” Journal of Intelligent Decision Support System, vol. 8, no. 1, pp. 22–33, 2025.
[21] Y. A. Ishak, D. Nugraha, and R. Astuti, “Advanced Clustering Approach for Mapping Regions of Paddy Productivity in Indonesia Using Intelligent K-Means,” in 2025 4th International Conference on Data Science and Information Technology (DSIT), 2025.
[22] G. Srinivasan, A. Kannan, and A. Rahman, “Energy efficient hierarchical clustering based dynamic data fusion algorithm for wireless sensor networks in smart agriculture,” Sci Rep, vol. 15, no. 1, p. 7207, 2025.
[23] T. Ahmed, Y. Zhang, and V. Kumar, “Fuzzy Methods in Smart Farming: A Systematic Review,” Comput Electron Agric, vol. 211, p. 108041, 2024.
[24] M. A. Rahmadani and others, “Advanced Clustering Approach for Mapping Regions of Paddy Productivity in Indonesia Using Intelligent K-Means,” in 2024 8th International Conference on Informatics and Computational Sciences (ICICoS), 2024, pp. 64–69. doi: 10.1109/ICICoS60247.2024.10932942.
[25] G. Rachmawati and others, “K-Means Using Dynamic Time Warping for Clustering Cities in Java Island According to Meteorological Conditions,” in 2023 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2023, pp. 96–101. doi: 10.1109/ISITIA58215.2023.10381899.
[26] I. M. Kartini, Y. Surya, and B. Triyono, “Clustering Data Meteorologi Wilayah Indonesia Timur Dengan Metode K-Means dan Fuzzy C-Means,” Jurnal Ilmiah Teknologi Informasi Asia, vol. 17, no. 2, pp. 123–130, 2023, [Online]. Available: https://ejournal.nusamandiri.ac.id/index.php/inti/article/view/5039
[27] S. Fadhilah, B. Widodo, and P. Lestari, “An Analysis of Meteorological Data in Sumatra and Nearby Using Agglomerative Clustering,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 3, pp. 412–419, 2023, [Online]. Available: https://jurnal.iaii.or.id/index.php/RESTI/article/view/5663
[28] D. N. Iliescu, “Fully Unsupervised Clustering in Nonlinearly Separable Data Using Intelligent Kernel KMeans,” in 2014 International Joint Conference on Neural Networks (IJCNN), 2014, pp. 2474–2481. doi: 10.1109/IJCNN.2014.6889674.
[29] M. D. Pratama and others, “An Intelligent Clustering Approach For Analyzing A Multivariate Time Series Dataset, Case Study COVID-19 Outbreak in Indonesia,” in 2023 7th International Conference on Informatics and Computational Sciences (ICICoS), 2023, pp. 77–82. doi: 10.1109/ICICoS60247.2023.10367007.
[30] R. K. U. Haq and others, “Student Graduation Time Prediction Using Intelligent K-Medoids Algorithm,” in 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2017, pp. 113–118. doi: 10.1109/ICACSIS.2017.8257122.