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River environmental pollution by industrial waste and household waste is common today. In its function, the river is an ecosystem of various living things, including humans who often use rivers to support their daily activities. Along with the automatic development of the era, many technological inventions have been used to solve the problem of waste in rivers. This study discusses how to obtain a river cleanliness level using a Deep Convolutional Neural Network based on digital image processing as a method of detecting waste based on the amount of garbage in the river. To achieve this goal, several stages are required. The first stage begins with preprocessing, namely the process of making learning data which results in two categories, namely garbage and non-waste. The second stage is the process of recognizing objects on a computer that functions as a program dataset. The third stage is creating a selection area on the river surface which is given the term ROI (Region Of Intereset). The next step is calculating the average detected waste in pixels in the video from the average pixel results obtained as a reference to determine the level of river cleanliness. In this study, the level of river cleanliness was modeled uniformly into four classes, including the very dirty category with the percentage of waste detected 76% -100%, dirty with 51% -75% detected waste process, quite dirty with a percentage of detected waste 26% -50%, clean with a percentage of detected waste from 0% -25%. Furthermore, the calculation is simulated with a desktop-based application with the Python programming language.
Copyright (c) 2020 Aditya Yuli Setyawan, Yosi Kristian
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