Optimasi Prediksi Nilai Ekonomi Penjualan Musiman dengan Decision Tree ACO di Indonesia
Keywords:
Decision Tree, Ant Colony Optimization, Prediksi Penjualan, Optimasi Fitur, Ekonomi DigitalAbstract
Penelitian ini bertujuan untuk mengoptimalkan prediksi nilai ekonomi penjualan musiman di Indonesia dengan mengintegrasikan algoritma Decision Tree dan Ant Colony Optimization (ACO). Data penelitian diperoleh dari catatan transaksi penjualan bulanan e-commerce periode 2024-2026, mencakup variabel kategori produk, metode pembayaran, ongkos kirim, serta total nilai penjualan. Model Decision Tree digunakan sebagai dasar prediksi, sementara ACO diterapkan untuk melakukan optimasi pemilihan fitur sehingga model lebih adaptif terhadap kompleksitas data musiman. Hasil penelitian menunjukkan bahwa integrasi Decision Tree - ACO mampu menurunkan nilai error secara signifikan, ditunjukkan melalui konvergensi RMSE pada iterasi awal, serta menghasilkan prediksi yang lebih mendekati nilai aktual dibandingkan metode tradisional. Analisis residual memperlihatkan sebagian besar kesalahan prediksi terkonsentrasi di sekitar nol, menandakan stabilitas model, meskipun masih terdapat deviasi pada kasus ekstrem. Kesimpulannya, kombinasi Decision Tree - ACO terbukti efektif dalam meningkatkan akurasi dan interpretabilitas prediksi penjualan musiman, sehingga dapat dijadikan dasar pengambilan keputusan strategis dalam konteks ekonomi digital Indonesia.
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