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All Journal The Asian Journal of Technology Management (AJTM) Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
Arif, Hamdani
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering

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Workshop Cybersecurity Awareness Meningkatkan Literasi Keamanan Digital di Wilayah Suburban Kepulauan Riau Nelmiawati, Nelmiawati; Fani, Maidel; Arif, Hamdani; Khaira, Hajrul; Ramadhan, Gilang Bagus; Afif, Iqbal
Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Vol 5 No 2 (2023): Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/abdimaspolibatam.v5i2.6821

Abstract

Phishing attack works by deceiving the victim so that they give their asset such as credential information to the attacker. The high level of cybercrime in form of phishing after COVID-19 pandemic has increasingly affected. It reaches new quarterly high in late 2022, where there was more than 4.7 million of phishing attacks has been launched, based on Anti-Phishing Work Group (APWG) Phishing Activity Trends Report. The importance of knowledge about phishing awareness is very necessary to minimize the affected victim. In order to decrease the issue, a Cybersecurity Awareness workshop has been conducted at several schools in Batam suburban area; SMK Negeri 1 Batam, SMK Multistudi High School (MHS) Batam, and SMK Negeri 1 Tanjung Pinang. The aim of these activity is to increase cybersecurity public awareness of phishing attack from an early age, especially at schools. As an approach, several activities related to phishing awareness have been executed in a workshop, including phishing simulation as well as socialization. Based on these activities, results shown that phishing awareness has been achieved in average 70% currently at young age.
An Extreme Gradient Boosting Approach for Classification and Sentiment Analysis Kairupan, Indah Yessi; Angdresey, Apriandy; Arif, Hamdani; Emor, Kenshin Geraldy
The Asian Journal of Technology Management (AJTM) Vol. 16 No. 3 (2023)
Publisher : Unit Research and Knowledge, School of Business and Management, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12695/ajtm.2023.16.3.5

Abstract

Since 2020, when the coronavirus epidemic was at its peak, the Indonesian Ministry of Health's social media accounts have been constantly followed by a big number of individuals. The Indonesian Ministry of Health account is a fantastic resource for social media users, particularly Twitter users. The Republic of Indonesia's Ministry of Health's Twitter account publishes a wide range of content at random. As a result, it is usually difficult for Twitter users to determine the type of information provided by the Ministry of Health of the Republic of Indonesia.  The positive and negative responses of Twitter users to material released by the Indonesian Ministry of Health's Twitter account are frequently noted.  The decision tree algorithm is tree-based, similar to the extreme gradient boosting method (XGBoost). The extreme gradient boosting approach has been successfully implemented with high performance in the classification process. This classification is separated into two primary categories: general and essential information categorization and sentiment analysis, which is classified into three classes: positive, neutral, and negative. Both the classification work and the sentiment analysis produced outstanding accuracy levels. Based on 2243 tweets, an accuracy rate of 89.35% has been achieved for classification, supported by a precision of 88.76% and a recall value of 88.58% when using 80 data training and 20 data testing.  Similarly, the maximum accuracy in sentiment analysis was achieved utilizing the same 80-20 data partitioning, with a 91.22% accuracy rate. Using 304 comments data, accuracy was calculated to be 89.17% and recall was calculated to be 89.06%.  It's worth noting that an 80-20 split for training and testing consistently produced the best results for both the sentiment analysis and classification tasks.
Co-Authors Afif, Iqbal Emor, Kenshin Geraldy Fani, Maidel Kairupan, Indah Yessi Khaira, Hajrul Nelmiawati, Nelmiawati Ramadhan, Gilang Bagus