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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Karjadi, Daniel Avian
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Computer Science & IT

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Objects Monitoring RADAR using Bluetooth Ultrasonic Karjadi, Daniel Avian; Haryono , Haryono
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11347

Abstract

Currently the development of information technology is very fast in its development. Up to all fields can not be separated from information technology. In the industrial era 4.0, there are also a lot of Internet of Things devices. Almost every field has used many IoT devices. The presence of IoT-based devices is very helpful in modern human life today. Moreover, with the development of microcontrollers and open source makes someone create a device quickly. One of them is making a prototype for sensing objects. By utilizing the Arduino Uno microcontroller and Ultrasonic sensors to create a sensing prototype. The results of this sensing can be displayed on notebooks and mobile phones. The microcontroller also sends sensing sensor data via Bluetooth as a wireless communication medium. Called Internet of Thing if a device can send signals to other devices. Various data communications can be via wireless or wire. Mobile app that is used to display sensing results from Ultrasonic sensors. Use of the App Inventor platform as a front end for mobile apps. This device is only limited to a short distance. Due to the limitations of the Ultrasonic sensing sensor it does not exceed 1 meter. This prototype can be developed using a longer distance and can be applied to remote sensing, if changing the sensing sensor with a remote sensing sensor.
Style Transfer Generator for Dataset Testing Classification Wedha, Bayu Yasa; Karjadi, Daniel Avian; Wedha, Alessandro Enriqco Putra Bayu; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11375

Abstract

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
Heavy-loaded Vehicles Detection Model Testing using Synthetic Dataset Karjadi, Daniel Avian; Wedha, Bayu Yasa; Santoso , Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11378

Abstract

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
Co-Authors Haryono , Haryono Santoso , Handri Santoso, Handri Wedha, Alessandro Enriqco Putra Bayu Wedha, bayu Yasa