  Object detection plays an important role in various fields. Developing
detection models for 2D objects that experience rotation and texture variations
is a challenge. In this research, the initial stage of the proposed model
integrates the gray-level co-occurrence matrix (GLCM) and local binary patterns
(LBP) texture feature extraction to obtain feature vectors. The next stage is
classifying features using k-nearest neighbors (KNN) and random forest (RF), as
well as voting ensemble (VE). System testing used a dataset of 4,437 2D images,
the results for KNN accuracy were 92.7% and F1-score 92.5%, while RF
performance was lower. Although GLCM features improve performance on both
algorithms, KNN is more consistent. The VE approach provides the best
performance with an accuracy of 93.9% and an F1 score of 93.8%, this shows the
effectiveness of the ensemble technique in increasing object detection
accuracy. This study contributes to the field of object detection with a new
approach combining GLCM and LBP as feature vectors as well as VE for
classification
