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License plates play an important role in vehicle identification in a variety of applications such as traffic safety, parking management, and traffic enforcement. In this study, we propose the development of license plate recognition applications using optical character recognition (OCR) and convolutional neural network (CNN) techniques. The OCR method is used to recognize characters on license plates and the CNN method is used to recognize license plates in images. The purpose of this research is to develop a system that can automatically recognize and recognize license plates in images. The OCR method accurately recognizes the characters on the license plate. Additionally, the CNN method is employed to detect license plates with good accuracy, even in various formats of license plates. The proposed methods in this research are implemented in the form of an application using the Python programming language. The application takes vehicle images as input and produces text recognition of the license plate as output. Furthermore, the application can also display additional information such as date, time, location, and detected vehicle type. Through this research, it is expected that the license plate recognition application using OCR and CNN methods can contribute to improving efficiency and reliability in automatic license plate recognition. Moreover, this application also has the potential to be used in various security applications, traffic monitoring, and automatic vehicle recognition

Cancer is the second most common cause of death in the world. WHO notes, deaths caused by cancer will reach 10 million cases in 2021. Of many cancers, breast cancer is a cancer with the most cases. Early diagnosis of breast cancer plays an important role in the treatment process. Various imaging methods, including magnetic mammography, are used to diagnose breast cancer. With the help of machine learning, the process of diagnosing breast cancer with mammography images is more precise and accurate. Various machine-learning methods have been developed by researchers to diagnose breast cancer. Among them is a deep learning method that can achieve good feature representation and can solve the problem of image classification and object localization. Through a systematic literature review, this research collects and analyzes related studies regarding the classification of breast cancer that have been done previously. Several aspects that will be evaluated include the methods used, data sources used, and accuracy of the method used. This research is expected to provide clear knowledge about the advantages and disadvantages of using artificial intelligence techniques for breast cancer classification. The results of this study can provide insight for researchers and medical practitioners in the further development and application of deep learning methods in the diagnosis and classification of breast cancer.

Plant diseases are taken into consideration as one of the major factors affecting food production and minimizing losses in production, and plant diseases must have rapid detection and recognition. Tomato (Lycopersicon esculentum) is one of the foodstuffs that are rich in nutrition and nutrients. Tomatoes are widely consumed by many countries including Indonesia and are hunted to be created in various spices, so tomatoes have a role in the economy due to the large demand. The recent enlargement of device studying techniques has found its application in plant disease detection especially tomato plants, presenting a powerful tool with relatively accurate effects. In this study, we present a systematic literature review aimed at identifying disease images in tomato plants. In this regard, we review 16 studies selected in the last five years with different approaches to address aspects related to tomato plant disease detection.