Prediksi Minat Pencarian Layanan Pesan-Antar Makanan Online (GoFood dan GrabFood) di Indonesia Menggunakan Algoritma Random Forest Regression dengan Walk-Forward Validation - Sinta Bella; Achmad Baijuri; Fajriyanto Fajriyanto (Bulletin of Computer Science Research, 2026)
The rapid growth of online food delivery services in Indonesia, particularly GoFood and GrabFood, creates significant operational challenges due to unpredictable fluctuations in user interest that cause driver and merchant capacity imbalances. Actual transaction data is proprietary, necessitating a proxy-data approach using Google Trends search interest indices. This study predicts GoFood search interest in Indonesia using Random Forest Regression based on Google Trends data from January 2018 to December 2025 (96 monthly records). The primary contributions of this study are threefold: first, the application of walk-forward validation as a methodologically sound evaluation approach for time-series data that eliminates temporal data leakage; second, the use of lag features (GoFood_lag1 and GrabFood_lag1) ensuring all predictor variables are practically available at prediction time; and third, empirical validation that this approach yields more conservative and scientifically defensible evaluations compared to conventional random split methods. Evaluation results yield MSE 1.4510, RMSE 1.2046, R² 0.5065, and MAPE 6.55%, demonstrating adequate generalization capability for data-driven operational planning.