Modeling of Organic Waste Classification as Raw Materials for Briquettes using Machine Learning Approach

Abstract

The existence of organic waste must be utilized by the community so that it does not only end up in landfills but can also be processed into something constructive so that it is useful and has high economic value. Organic waste can be converted into raw materials to manufacture of biomass briquettes. Machine learning techniques were developed for technological applications, object detection, and categorization. Methods with artificial reasoning networks that use a number of algorithms, such as the Naive Bayes Classifier, will work together in determining and identifying certain characteristics in a digital data set. The manufacturing method goes through several processes with a waste classification model as a source of learning data. The image data is based on five types: coconut shells, sawdust, corn cobs, rice husks, and plant leaves. The research aims to identify and classify types of waste both organically and non-organically so that it will make it easier to sort waste. The results of testing the organic waste application from digital images have an accuracy rate of 97%. The model design carried out in training data is useful for producing a data model.

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