The development of the Generative Adversarial Network is currently very fast. First introduced by Ian Goodfellow in 2014, its development has accelerated since 2018. Currently, the need for datasets is sometimes still lacking, while public datasets are sometimes still lacking in number. This study tries to add an image dataset for supervised learning purposes. However, the dataset that will be studied is a unique dataset, not a dataset from the camera. But the image dataset by doing the augmented process by generating from the existing image. By adding a few changes to the augmentation process. So that the image datasets become diverse, not only datasets from camera photos but datasets that are carried out with an augmented process. Camera photos added with painting images will become still images with a newer style. There are many studies on Style transfer to produce images in drawing art, but it is possible to generate images for the needs of image datasets. The resulting force transfer image data set was used as the test data set for the Convolutional Neural Network classification. Classification can also be used to detect specific objects or images. The image dataset resulting from the style transfer is used for the classification of goods transporting vehicles or trucks. Detection trucks are very useful in the transportation system, where currently many trucks are modified to avoid road fees
Management of forest resources can generate income for individuals or companies including agroforestry and ecotourism. In times of a pandemic like recent times, the application of technology with the Industry 4.0 framework is needed the most and is no longer a privilege. However, the implementation of Industry 4.0 technology requires large investment or cost so that a collaborative ecosystem is required from several stakeholders such as forest planter, crop planters, livestock and ecotourism business actors with suppliers, customers and policy makers. In its application, even for Information Technology (IT) system in general, the use of Enterprise Architecture is a mediator between business language and IT language. With all of that background and a literature study approach, this research will propose an Enterprise Architecture design with an Industry 4.0 framework in the agroforestry industry and ecotourism that can be used together to form an integrated ecosystem from various stakeholders, both for companies of various scales and small farmers if supported by the government or a larger company for better efficiency and common interests.
Currently, many roads in Indonesia are damaged. This is due to the presence of large vehicles and large loads that often pass. The more omissions are carried out, the more damaged and severe the road is. The central government and local governments often carry out road repairs, but this problem is often a problem. Damaged roads are indeed many factors, one of which is the road load. The road load is caused by the number of vehicles that carry more than the specified capacity. There are many methods used to monitor roads for road damage. The weighing post is a means used by the government in conducting surveillance. This research is not a proposal to monitor the road, but this is only to create a model for the purpose of detecting heavily or lightly loaded vehicles. This research is to classify using Convolutional Neural Network (CNN) with pre-trained Resnet50. The model generated from the Convolutional Neural Network training process reaches above 90%. Generate Image deep learning algorithms such as the Generative Adversarial Network currently generate a lot of synthetic images. The testing dataset that will be used is generated from style transfer. The model is tested using a testing dataset from the generated style transfer. Style transfer is a method of generating images by combining image content with image styles. The model is pretty good at around 92% for training and 88% for testing, can it detect image style transfer? The Convolutional Neural Network model is said to be good if it is able to recognize the image correctly, considering that the accuracy of the model is very good. One of the reasons why the training model is good but still makes errors during testing, then the image dataset is overfitting
Smartphone technology is currently developing rapidly. In an effort to improve and empower companies, smart is used as a tool for anything in office work and in projects. For example, smartphones are currently used for meetings or coordination with leaders and employees. In the project, smartphones can be used to monitor employees. One of the problems with work on projects is that employees often leave project work for several reasons. The position of employees is often unknown to other employees or leaders. So the idea arose to find out the whereabouts of the employee. Monitoring employees on the project is very important, where the presence of these employees is needed to coordinate. If the employee is at risk of a work accident, the management quickly knows the position of the employee. The idea of making employee monitoring in the project by creating an application system for employee positions in the project. In addition to monitoring, employees do not need to attend the office, from the employee's home directly to the project site and do asben from the project site. The data will record the name of the employee, the position of the employee in the project, the time of absence, see the tasks that will be carried out per day. The results of the task are carried out by taking selfies as a report to superiors. The purpose of this research is to solve the problem of the existence of employees or the position of employees in the project, to be absent at the project site, to take selfies on the progress of daily work, and to hold meetings with the application. This application is built using technology based on the Android system using Flutter.
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