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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer)
Teguh Rijanandi
Institut Teknologi Telkom Purwokerto, Purwokerto
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Pengembangan dan Evaluasi Sistem Presensi Pegawai dengan Data Geolocation Menggunakan Metode Prototipe Ariq Cahya Wardhana; Ananda Rifkiy Hasan; Teguh Rijanandi
JURIKOM (Jurnal Riset Komputer) Vol 9, No 5 (2022): Oktober 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i5.4920

Abstract

The Ma'arif NU Ajibarang Education Organizing Foundation (YYPMNU) is a foundation engaged in education in the Ajibarang area. There are 4 types of UPT operating under the auspices of the foundation, including SMK Ma'arif NU 1 Ajibarang, SMK Ma'arif NU 2 Ajibarang, STIKES Ibnu Sina Ajibarang, and Modern Islamic Boarding School Ibnu Sina Ajibarang. The management of the foundation's personnel attendance has been carried out by utilizing information technology but it is still not effective and efficient because the presence using fingerprints causes long queues when they come to work, so it is necessary to develop an employee attendance system that can be accessed online by all employees without having to queue. The employee attendance system has been successfully developed based on Responsive Web Apps (RWA) which features presence location coverage between employees and UPT locations using geolocation data available on employee smartphones. The results of the performance evaluation of the employee attendance system using google lighthouse resulted in an average performance of 41.5.
Analysis of Distributed Denial of Service Attacks Using Support Vector Machine and Fuzzy Tsukamoto Paradise Paradise; Wahyu Adi Prabowo; Teguh Rijanandi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5199

Abstract

Advances in technology in the field of information technology services allow hackers to attack internet systems, one of which is the DDOS attack, more specifically, the smurf attack, which involves multiple computers attacking database server systems and File Transfer Protocol (FTP). The DDOS smurf attack significantly affects computer network traffic. This research will analyze the classification of machine learning Support Vector Machine (SVM) and Fuzzy Tsukamoto in detecting DDOS attacks using intensive simulations in analyzing computer networks. Classification techniques in machine learning, such as SVM and fuzzy Tsukamoto, can make it easier to distinguish computer network traffic when detecting DDOS attacks on servers. Three variables are used in this classification: the length of the packet, the number of packets, and the number of packet senders. By testing 51 times, 50 times is the DDOS attack trial dataset performed in a computer laboratory, and one dataset derived from DDOS attack data is CAIDA 2007 data. From this study, we obtained an analysis of the accuracy level of the classification of machine learning SVM and fuzzy Tsukamoto, each at 100%.
Co-Authors Ananda Rifkiy Hasan Ariq Cahya Wardhana Paradise Paradise Wahyu Adi Prabowo