Analisis Ekspor Kopi Menggunakan Clustering K-Means dan Davies-Bouldin Index
DOI:
https://doi.org/10.22441/fifo.2025.v17i2.006Keywords:
Klasterisasi, Data Mining, K-Means, Davies-Bouldin Index, Segmentasi PasarAbstract
Indonesia merupakan salah satu produsen kopi terbesar di dunia, sehingga sektor ekspor menjadi pilar penting dalam perekonomian nasional. Aktivitas ekspor berperan dalam meningkatkan keuntungan, memperluas pangsa pasar, serta menjaga kestabilan harga komoditas dan nilai tukar. Penelitian ini mengkaji penerapan metode K-Means Clustering untuk menganalisis kinerja ekspor kopi berdasarkan negara tujuan. Data penelitian diperoleh dari catatan ekspor perusahaan Café Coffee pada periode 2023–2024, mencakup 40 negara tujuan beserta total kuantitas ekspor. Pengolahan data dilakukan melalui teknik data mining clustering dengan ukuran jarak Euclidean Distance. Hasil analisis menunjukkan bahwa algoritma K-Means berhasil mengelompokkan laba ekspor ke dalam tiga kategori, yaitu laba rendah, sedang, dan tinggi. Validasi model dilakukan menggunakan Davies-Bouldin Index (DBI) dengan nilai 0,422, yang mengindikasikan kualitas klaster yang baik dan dapat diterima.Downloads
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