OPTIMISASI METODE PREDIKSI PADA DATA CUSTOMER ATTACK LIST
Abstrak
Kunjungan pelanggan di bengkel resmi TOYOTA untuk servis terdata oleh sistem aplikasi. Pengelolaan data pelanggan /data seranganlist diperlukan oleh tim perbaikan umum , untuk melakukan tindak lanjut berupa konfirmasi pemberitahuan-penjadwalan servis selanjutnya. Proses ini sudah berjalan selama kurun waktu 2 tahun, namun hasil penjadwalan kedatangan kunjungan servis pelanggan belum sesuai dari total data pelanggan ( Attacklist ) yang dikonfirmasi. Penelitian ini dilakukan untuk memprediksi variabel waktu servis, menggunakan metode eksplorasi jarak dan analisa data ( EDA ), membuat model algoritma Linear Regression, Random Forest Regressor dan Gradient Boosting Regressor yang digunakan untuk prediksi. Dengan menggunakan metodologi Cross-Industry Standard Process for Data Mining ( CRISP-DM ). Nilai prediksi yang dihasilkan oleh setiap model algoritma, untuk variabel rentang_hari (waktu servis selanjutnya) memiliki akurasi yang mendekati nilai aktual/sebenarnya. Sehingga penelitian ini merekomendasikan penggunaan model algoritma, yang dapat diterapkan pada sistem aplikasi yang ada. Sehingga optimalisasi data pelanggan/ Attacklist bersinergi dengan proses bisnis yang berjalan.##submission.downloads##
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2025-08-21
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