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All Journal INTEGER: Journal of Information Technology Jurnal Indonesia Sosial Teknologi
Prisscilya, Veren
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Computer Science & IT Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Implementasi Keamanan Jaringan Menggunakan Intrusion Detection System (IDS) Pada PT. Mega Esa Farma Santoso, Tri; Prisscilya, Veren
INTEGER: Journal of Information Technology Vol 6, No 1: Mei 2021
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2021.v6i1.1205

Abstract

In the current global era, Information Technology (IT) has developed rapidly, especially with the existence of an internet network that makes it easy to communicate and exchange data with other parties. Because of the easy access to this information, security issues have become a major focus in the world of computer networks. This causes a new problem that is important information or data can be used by parties who are not responsible for their own benefits. So that a network security system becomes an important aspect. PT. Mega Esa Farma is an industrial company engaged in the field of Pharmacy. This company is one of the many companies in Indonesian that has obstacles in network security, the absence of monitoring on a company’s computer network is very bad for data security. Therefore, we need a system that can be used to monitor and proctect the network from threats that will occur. By using the Intrusion Detection System (IDS) with snort to follow up on the generated snort alerts. A DDOS attack and port scanning attempt has been conducted on a computer that has a snort installed and the results are obtained that the snort is able to detect the attack and directly send alert to the administrator.
Classification of Indonesia False News Detection Using Bertopic and Indobert Prisscilya, Veren; Girsang, Abba Suganda
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 8 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i8.1310

Abstract

In the current global era, the development of technology and information is very rapid, so it is very easy to get information/news from the internet. Because of the ease of getting this information, there is a lot of circulating fake news (hoaxes), the news is not filtered so anyone can spread news that is not clear in content. This can lower a person's credibility in the professional world, cause division, threaten physical and mental health, and can also result in material losses. Based on this, to stop the spread of hoaxes is to detect them as early as possible and block them. This detection can use deep learning methods which are also one of the architectures of transformers, namely a combination of BERTopic which is used to find important words from the news narrative, then the words are combined into the narrative and classified using Indo Bidirectional Encoder Representation from Transformer (IndoBERT). For experiments, the author uses a dataset taken from the kaggle.com website entitled Indonesia False News (HOAX) dataset. This study uses a learning rate of 1e-5, a batch size of 16 and using 5 epochs so that the f1-Score results are 92% for validation data and 91% for testing data.
Co-Authors Abba Suganda Girsang, Abba Suganda Tri Santoso