Cluster Analysis Menggunakan Algoritma Fuzzy C-means dan K-means Untuk Klasterisasi dan Pemetaan Lahan Pertanian di Minahasa Tenggara
DOI:
https://doi.org/10.24002/jbi.v8i3.1317Abstract
Abstract.
The purpose of this study is to perform cluster analysis and implementation by utilizing fuzzyc-means (FCM) and k-means (KM) to process agricultural data based on the data mining results. The fuzzy c-means (FCM) and k-means (KM) are implemented to find out and form the agricultural land clusters which appropriate the commodity types based on the supporting attributes that are used. The analysis and implementation results could provide some land information such as the number of the clusters, the land areas, the region areas, the locations and the productivity levels. The results of this study could be applied as the suggestion in converting the land functions and structuring the agricultural lands. The utilization of Openstreetmap is an open source solution which is implemented in the application. It could give visual information related to the agricultural land regions based on the clusters which make it easier to comprehend.
Keywords: Cluster analysis, C-means, K-means, GIS, Data mining
Abstrak.
Penelitian ini bertujuan untuk melakukan analisis cluster dan implementasinya dengan menggunakan algoritma fuzzy c-means (FCM) dan k-means (KM) untuk mengelola data pertanian dari hasil data mining yang dilakukan. Fuzzy c-means (FCM) dan k-means (KM) dimplementasikan untuk menemukan dan membentuk klaster-klaster daerah lahan pertanian sesuai dengan jenis komoditi berdasarkan atribut-atribut pendukung yang digunakan. Hasil analisis dan implementasi dapat menyediakan informasi lahan seperti jumlah kluster, luas lahan, luas daerah, letak dan tingkat produktifitas. Hasil yang diperoleh dapat menjadi bahan masukan dalam proses alih fungsi dan penataan lahan pertanian. Penggunaan Openstreetmap merupakan solusi open source yang diimplementasikan pada aplikasi dapat memberikan informasi visual daerah-daerah lahan pertanian berdasarkan klaster yang dihasilkan sehingga lebih mudah untuk dipahami.
Keywords: Cluster analysis, c-means, k-means, GIS, Data mining
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