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All Journal Format : Jurnal Imiah Teknik Informatika Jurnal Teknik Mesin, Industri, Elektro dan Informatika
Avrijsto Amandri Achyar
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Identifikasi Ras Wajah dengan Menggunakan Metode Deep Learning Model Keras Avrijsto Amandri Achyar; Ali Muhammud Olow; Muhammad Rizky Perdana; Andika Sundawijaya; Aaqila Dhiyaanisafa Goenawan
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 1 No. 1 (2022): Maret : JURNAL TEKNIK MESIN, INDUSTRI, ELEKTRO DAN INFORMATIKA
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (155.597 KB) | DOI: 10.55606/jtmei.v1i1.779

Abstract

Humans have various types which are called races. The human race can be identified by many things, from the shape of the body to the color of the body. Of the many ways to identify the human race, the author also thinks whether a computer can identify the human race based on skin color. Nowadays, computers have developed in such a way that there are many possibilities that computers can also do this.
Penerapan Algoritma SVM pada Software Define Network untuk Mendeteksi dan Mitigasi Serangan DDOS pada Server Jaringan andi purnomo; Avrijsto Amandri Achyar
FORMAT Vol 14 No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2025.v14.i2.001

Abstract

Software Defined Network (SDN) is a network architecture that is very useful in the future where SDN can be used to manage network traffic on server networks. SDN can be implemented using a variety of controllers. In the controller the developer can configure it with various algorithms or other functions. At present, cyber crimes are increasingly numerous and dangerous. One of the most dangerous cyber attacks that is mostly carried out by both novice and professional hackers is the DDoS attack. DDoS attacks are aimed at crippling servers with server administration with multiple streams and packets. SDN as an architect for managing networks can be used to detect and counteract DDoS attacks so that servers are protected from these attacks. In this study researchers used SDN configured using the SVM algorithm to detect and mitigate DDOS attacks. In this study, the researchers obtained results where SDN with the SVM algorithm configuration obtained an accuracy rate of 99.67%. The SDN speed configured with the SVM algorithm does not exceed 0.30ms. Wireshark statistics show that SDN with the SVM algorithm configuration can stabilize and mitigate packets detected as DDOS.
Co-Authors Aaqila Dhiyaanisafa Goenawan Ali Muhammud Olow Andi Purnomo Andika Sundawijaya Muhammad Rizky Perdana