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Corn consumption in Indonesia can reach around 14.37 million tons and continues to increase every year. This increase is directly proportional to the demand from corn processing industries/factories/warehouses to farmers for the supply of corn ingredients. However, the industry often finds corn with quality that is not suitable for production from farmers/collectors, this causing losses for industries. This problem is very important to overcome so that losses can be reduced and the results of corn processing production can also have good quality. This research proposes an image processing solution for industrial corn quality classification using the Convolutional Neural Network (CNN) method. This research trains the CNN to be able to recognize the quality of corn by utilizing 600 images/images of corn as a dataset consisting of 300 images of good quality corn and 300 images of poor quality corn. The CNN method is then used as an algorithm to recognize good quality corn and which is not. The research results so far show an accuracy rate of 99.1% with 10 epoch training. [ABSTRACT FROM AUTHOR]

Skin Cancer is one of the most common types of disease in the world, including Indonesia. This disease is caused by food that can be toxic to the human body and the effects of global warming. Especially melanoma, this type of skin cancer that occurs in melanocyte cells is a very serious threat. In the United States, although melanoma is only 5% of all cases of skin cancer that occur, melanoma has been the cause of death in 75% cases of death from skin cancer in the country. Melanoma has a similar shape as mole, birthmark or beauty sign that is often ignored. To recognize the presence of melanoma early on, this study uses the method of deep convolutional neural network to classify skin cancers that occur in melanocytes, where melanoma grows. The deep learning method used has good accuracy and good use in image recognition. Dermoscopy image of skin cancer is used as input for image processing. Before entering the classification stage, the image is processed with the image processing Scaling and contrast enhancement. After testing phase in this study, it concludes that the proposed method has the ability to classify skin cancer that occurs in melanocytes quite well with an accuracy of 93.3%. [ABSTRACT FROM AUTHOR]

Currently, many companies are competing to produce robots that can help humans. Robots can work with high accuracy, efficiency, and low power in the production of goods. Automated Guided Vehicle is one form of robot that is useful in industry. Automated Cars are another form of functional industrial robot that transports with programmable paths and directions automatically. In this paper, we will discuss the navigation system in Automatic Cars based on previously mapped paths. The robot wheel serves to adjust the direction and position of the robot. The automatic movement can be carried out using computer vision. Warehouses that are not neatly arranged cause the navigation system to be less than optimal. The types of controllers used include classical controllers, fuzzy logic, artificial neural networks, and H∞. The purpose of this research is to produce a precise car front view monitoring system using image processing with high enough quality for automatic car navigation. The benefit for science is the development of automatic cars, which so far have not been widely carried out in Indonesia. The results of this study can be useful for the development of methods to build an automatic car system. The benefit for the electronics industry is the availability of control equipment designs to be able to control cars automatically. The effect on society is in the form of future vehicles that can move automatically without a driver. Based on the computer vision system test for Raspberry Pi-based car navigation, it can be ascertained that the car works well and has a small physique, so it looks neat and concise. The movement of the car from the first coordinate point to the next coordinate point is very precise and precise. By installing the L298N motor driver module, it is easier to set the program in python, so that the results sent to the DC motor can be used directly. Using a DC motor that has an excellent performance in terms of torque and speed. [ABSTRACT FROM AUTHOR]