Model Business Intelligence Berbasis Explainable AI dan Analisis Ketidakpastian untuk Rekomendasi Strategi Penjualan UMKM
DOI:
https://doi.org/10.57093/jisti.v9i2.429Keywords:
Business Intelligence, Explainable AI, Random Forest, Conformal Prediction, UMKMAbstract
Penelitian ini mengembangkan model Business Intelligence yang mengintegrasikan Explainable AI dan analisis ketidakpastian untuk mendukung keputusan penjualan UMKM Coppo Awi. Data observasi mencakup 809 catatan produk-harian pada Januari–Juni 2025; sebanyak 739 observasi memenuhi kebutuhan fitur historis. Data dipisahkan secara kronologis menjadi 495 pelatihan, 120 kalibrasi, dan 124 pengujian. Random Forest dan XGBoost dibandingkan dengan tiga metode dasar. Random Forest menghasilkan MAE 2,361 unit, RMSE 3,130 unit, dan WAPE 25,11% pada data uji. SHAP mengidentifikasi rerata penjualan 14 hari sebelumnya sebagai fitur dominan. Conformal Prediction dengan target cakupan 90% menghasilkan cakupan empiris 94,35% dan lebar interval rata-rata 11,51 unit. Rekomendasi yang dihasilkan berupa indikasi peninjauan kebutuhan restok, bukan klaim peningkatan keuntungan. Hasil menunjukkan kelayakan awal integrasi prediksi, interpretabilitas, dan ketidakpastian dalam analitik penjualan UMKM, dengan kebutuhan validasi lanjutan atas catatan stok dan pendapatan
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