Analisis Penerapan Hyperparameter Tuning Pada Machine Learning Untuk Meningkatkan Akurasi Prediksi Tingkat Penjualan

• SUGIYANTI
• 14210253

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ABSTRAK

ABSTRAK

Nama              : Sugiyanti

NIM                 : 14210253

Program Studi : Ilmu Komputer

Fakultas          : Teknologi Informasi

Jenjang           : Strata Dua (S2)

Konsentrasi     : Data Mining

Judul             : “Analisis Penerapan Hyperparameter Tuning Pada Machine Learning Untuk Meningkatkan Akurasi Prediksi Tingkat Penjualan”

Pertumbuhan pelaku bisnis penjualan semakin meningkat, untuk itu perlu adanya suatu prediksi terhadap tingkat penjualan untuk penjualan masa depan agar tidak mengalami kerugian finansial. Tujuan dari penelitian ini adalah untuk mendapatkan model prediksi tingkat penjualan dengan akurat. Pihak penjual akan memahami fitur-fitur penting yang mempengaruhi terhadap tingkat atau nilai penjualan. Metode yang digunakan dalam peneltiian ini adalah algoritma regression Machine Learning (ML) serta menggunakan hyperparameter tuning. Tahap preprocessing dalam penelitian ini sangat penting untuk dapat menghasilkan nilai prediksi yang lebih baik. Hasil dari penelitian ini menghasilkan algoritma terbaik yaitu XGBoost dengan hasil Root Mean Squared Error (RMSE) adalah 968 dan nilai Mean Absolute Error (MAE) adalah 713, hasil tersebut lebih baik dari hasil penelitian sebelumnya dengan menggunalan algoritma yang sama yaitu XGBoost tetapi tidak menggunakan hyperparameter tuning optuna menghasilkan nilai RMSE sebesar 1052, dan MAE sebesar 739.03. Sehingga dengan menggunakan hyperparameter tuning optuna dapat menurunkan nilai error hasil prediksi.

KATA KUNCI

Prediksi,Penjualan,Hyperparameter,Optuna,XGBoost

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