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All Journal QARDHUL HASAN: MEDIA PENGABDIAN KEPADA MASYARAKAT Teknika
Mustika Ulina
Universitas Mikroskil
Agriculture, Biological Sciences & Forestry Computer Science & IT Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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INTRODUCTION OF DEVICES AND ASSEMBLING COMPUTERS TO STUDENTS Apriyanto Halim; Mustika Ulina; Joosten Joosten
Qardhul Hasan: Media Pengabdian kepada Masyarakat Vol. 9 No. 2 (2023): AGUSTUS
Publisher : Universitas Djuanda Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30997/qh.v9i2.8148

Abstract

SMK Swasta Methodist Tanjung Morawa is a private school under the auspices of the Kasih Imanuel Indonesia Methodist Foundation, which was established in 2008. SMK Swasta Methodist Tanjung Morawa has various majors, one of which is Network and Computer Engineering (TKJ). Assembling computer equipment is one of the subjects that can help students become more familiar with computer equipment and know how to fix it and of course this lesson is already a lesson that is in accordance with the majors of SMK Swasta Methodist Tanjung Morawa students, namely Computer Network Engineering (TKJ). The students have studied theoretically related to assembling computer equipment, therefore, the Faculty of Informatics, Universitas Mikroskil offers activities in the form of training in assembling computer equipment to improve students' ability to have good knowledge in computer equipment and how to assemble it. This training activity lasted for 2 days and was carried out in the accounting laboratory at Universitas Mikroskil. During this training activity the students were given pre-test questions, materials and case studies, post-tests and final feedback.
Optimizing Deep Neural Networks Using ANOVA for Web Phishing Detection Wulan Sri Lestari; Mustika Ulina
Teknika Vol 13 No 1 (2024): Maret 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i1.758

Abstract

Phishing attacks are crimes committed by sending spoofed Web URLs that appear to come from a legitimate organization in order to obtain another party's sensitive information, such as usernames, passwords, and other confidential data. The stolen information is then used to commit fraud, such as identity theft and financial fraud, and can cause reputational damage to the party that is the victim of the phishing attack. This can cause great harm to the victimized individual or organization. To overcome these problems, this research uses feature selection using ANOVA and Deep Neural Networks (DNN) to detect web phishing attacks. Feature selection is used to optimize the performance of the DNN model to achieve more accurate results. Based on the results of feature selection using ANOVA, there are 52 attributes that have a significant impact on web phishing attack detection. The next step is to implement DNN to build a web phishing attack detection model. The results of testing the web phishing detection model show that in the training phase, the accuracy value increased by 17.51% for the 80:20 dataset and 18.39% for the 70:30 dataset. During the testing phase, the accuracy value increased by 17.8% for the 80:20 dataset and 18.58% for the 70:30 dataset. The resulting recognition model shows consistent and reliable results not only during training, but also during testing in situations closer to real-world conditions. Conclusively, the use of ANOVA proves effective in mitigating less relevant features and contributing to the optimization of web phishing detection models.
Co-Authors Apriyanto Halim Joosten Joosten Wulan Sri Lestari