Abstract:
Waste is still a problem faced in daily. A common problem that is closely related to waste is its
inefficient management where organic waste is still found mixed with other types of waste in
landfills. The solution that can be done to overcome waste management is to utilize the object
detection method. Object detection is a method that can be used to determine the presence of
objects in an image. This method can be used in waste sorting to make it more efficient than
traditional methods. Image annotation is performed as an initial step in building an object
detection system. Image annotation’s function is to provide the necessary information for the
system to recognize object features in the image. This research aims to annotating the image of
organic waste into two types, namely wet organic waste and dry organic waste. The images are
acquired in several position variations so that the annotated images can produce models with
various conditions that affect the results of object detection. Image annotation is done by
utilizing Python-based software. The results of the image annotations that have been carried out
are as many as 100 images of wet organic waste and 100 images of dry organic waste, so that
there are a total of 200 images that have been annotated.
