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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika TEPIAN
Santoso , Handri
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Computer Science & IT

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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
Wireless Computer Network (WLAN) Security Test at Pradita University Bagaskara, Banu; Santoso , Handri
TEPIAN Vol. 4 No. 4 (2023): December 2023
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v4i4.2910

Abstract

In this day and age, information technology has developed rapidly, one of which is wireless computer network technology called Wireless Local Area Network (WLAN). Agencies and individuals have widely used the use of wireless computer networks. Because with a wireless computer network, it is easy to connect the devices so that your work becomes quickly completed. Along with the development of wireless computer networks, problems will undoubtedly arise, one of which is about network security in wireless technology; therefore, to maintain network stability, it is necessary to conduct periodic evaluations. To analyze network security with the Penetration Testing method simulated attacks on the network, the operating system with the correct specifications is Kali Linux. Kali Linux is designed for network security testing, equipped with tools that support hacking activities, and as a tool to test network security. The results of this research can later be used to improve the security of wireless computer networks at Pradita University. This study concludes that during the design of WLAN Network Security Analysis using attacks (Cracking The Encryption, Denial of Service, and MITM) using Kali Linux at Pradita University, it can be concluded that the security owned by the Pradita University WLAN network still has many loopholes to exploit.
Co-Authors Bagaskara, Banu Karjadi, Daniel Avian Wedha, bayu Yasa