<![CDATA[This study aims to recognize letter patterns in Indonesian Sign Language (BISINDO) using the Random Forest algorithm. BISINDO represents 26 alphabet letters through distinct hand gestures. To automate the recognition process, this research utilizes MediaPipe for real-time feature extraction. MediaPipe detects and tracks hand movements using handedness to distinguish between left and right hands, as well as landmarks and world landmarks to capture key coordinate points that serve as input features for classification. The model is evaluated under various conditions, including differences in distance and lighting. Experimental results show that the model performs optimally at a distance of 30 cm. Under normal lighting conditions, the system achieves an accuracy of 88% by correctly recognizing 23 classes in the first test, and improves to 96% with 25 recognized classes in the second test, resulting in an average accuracy of 92%. However, under low-light conditions at the same distance, performance decreases, where only 9 out of 13 tested classes are correctly recognized, yielding an accuracy of 69%. When the distance increases to 60 cm, the model’s performance further declines, with 22 classes recognized at 84% accuracy in the first test and 20 classes at 76% accuracy in the second test. Further evaluation using a confusion matrix shows excellent classification performance, achieving 100% accuracy, recall, and precision. Overall, the integration of MediaPipe and Random Forest proves effective for BISINDO letter recognition and contributes to the development of assistive technologies that support inclusive communication through computer vision and machine learning approaches.]]>
