ANALISIS CLUSTERING PRESTASI PEMANEN KELAPA SAWIT MENGGUNAKAN METODE HARD C-MEANS PADA PTPN IV PALMCO KEBUN SAWIT HULU
Abstrak
Abstrak— Evaluasi prestasi pemanen kelapa sawit di PTPN IV PalmCo Kebun Sawit Hulu selama ini belum didukung oleh pengelompokan yang objektif, sehingga keputusan pembinaan dan pemberian penghargaan belum berbasis data. Penelitian ini bertujuan mengelompokkan prestasi pemanen menggunakan metode Hard C-Means (HCM) dan mengevaluasi kualitas pengelompokan yang dihasilkan. Data yang digunakan adalah rerata capaian tahun 2024–2025 dari 268 pemanen pada 13 afdeling dengan tiga variabel, yaitu Tandan Buah Segar (TBS), brondolan, dan hari kerja (HK). Data dinormalisasi dengan Min–Max, kemudian dikelompokkan ke dalam tiga cluster menggunakan jarak Euclidean. kualitas cluster diukur menggunakan Silhouette Coefficient dan Davies–Bouldin Index (DBI). Algoritma konvergen pada iterasi ke-9 dan menghasilkan cluster prestasi tinggi sebanyak 57 pemanen (21,3%), prestasi sedang 139 pemanen (51,9%), dan prestasi rendah 72 pemanen (26,9%). Nilai DBI sebesar 0,771 merupakan yang terendah untuk c = 2–7, sedangkan nilai Silhouette sebesar 0,429 menunjukkan struktur cluster yang tergolong lemah–moderat; meskipun demikian, partisi yang sama diperoleh dari 100 inisialisasi acak dan dari normalisasi z-score. Analisis lanjutan menunjukkan bahwa produktivitas harian cluster rendah dan sedang tidak berbeda signifikan (971 dan 982 kg/HK; p = 0,848), sehingga rendahnya prestasi lebih disebabkan oleh jumlah hari kerja yang kecil. Hasil ini dapat dijadikan dasar evaluasi kinerja, pembinaan, dan pemberian penghargaan pemanen.
Kata kunci: Hard C-Means, clustering, prestasi pemanen, kelapa sawit, Davies–Bouldin Index, Python.
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