<![CDATA[Fiber-to-the-Home (FTTH) networks are widely used to provide high-speed broadband services, but optical power degradation can reduce network performance and service quality. This study compares Random Forest (RF) and Long Short-Term Memory (LSTM) for predicting FTTH network conditions classified as Normal, Warning, and Critical. The study used 63,145 historical records collected from 58 Optical Network Terminals (ONTs) between March and May 2026. To provide a fair comparison, RF was trained using engineered tabular features, including lag and rolling-window statistics, while LSTM used six-step sequential data representing approximately the previous six hours. Model performance was evaluated using accuracy, precision, recall, F1-score, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), training time, and inference time. The results show that RF substantially outperformed LSTM, achieving 98.41% accuracy, precision, recall, and F1-score, with an MAE of 0.0173 and RMSE of 0.1420. RF also required only 2.667 seconds for training and 0.0102 ms for inference, compared with 189.98 seconds and 0.1856 ms for LSTM. Per-class evaluation confirmed that RF performed well across all network conditions, with precision and recall above 99% for Normal, above 94% for Warning, and above 90% for Critical. A strict chronological train-test split further confirmed the robustness of RF, which achieved 98.59% accuracy. Feature importance analysis showed that historical optical power, particularly lag-based features, was the most influential predictor of network degradation. These findings indicate that FTTH optical power degradation can be effectively modeled using engineered tabular features rather than a purely sequential approach. Finally, the RF model was integrated into a web-based monitoring dashboard with WhatsApp-based early warnings to support proactive FTTH network maintenance.]]>
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