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All Journal TEKNIK INFORMATIKA Teknika: Jurnal Sains dan Teknologi JOIV : International Journal on Informatics Visualization JRSI (Jurnal Rekayasa Sistem dan Industri) Academica : Journal of Multidisciplinary Studies Jurnal Pendidikan dan Konseling Jurnal Varian eProceedings of Engineering SmartComp The Indonesian Journal of Computer Science
Dita Pramesti
Universitas Telkom
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Students Demography Clustering Based on The ICFL Program Using K-Means Algorithm Andreswari, Rachmadita; Fauzi, Rokhman; Izzati, Berlian Maulidya; Widartha, Vandha Pradwiyasma; Pramesti, Dita
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1916

Abstract

Independent Campus, Freedom to Learn (ICFL) Program is one of the manifestations of student-centered learning. This program can help students reach their full potential by allowing them to pursue their passions and talents. This study aims to see how the segmentation of students participating in the ICFL program is based on demographic data. This research is based on survey responses from students participating in the ICFL program. The method used in this study is input data preparation, pre-processing, data cleansing, and data analysis. The information will be pre-processed before being utilized and evaluated. To help produce better outcomes in data clustering, the K-Means clustering approach is used, which is processed using the Python computer language. The data is clustered using the K-Means clustering approach based on gender characteristics, Grade Point Average (GPA), university entrance selection, ICFL category, and year or semester when participating in ICFL. This study resulted in three clusters with each of its criteria. The dominant gender is found in clusters 2 (100% female) and 3 (100% male). Software Development was the most popular ICFL category among students in cluster 1, accounting for 67%, while Design and Analysis Information Systems was the most popular in clusters 2 and 3. The most dominant ICFL program is found in three clusters. ICFL - Internship program in which at least 40% of participants come from each cluster. The research results are expected to assist stakeholders in evaluating the implementation of the ICFL program.  
Identification of Mirai Botnet in IoT Environment through Denial-of-Service Attacks for Early Warning System Rahmatulloh, Alam; Muhammad Ramadhan, Galih; Darmawan, Irfan; Widiyasono, Nur; Pramesti, Dita
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.3.1262

Abstract

The development of computing technology in increasing the accessibility and agility of daily activities currently uses the Internet of Things (IoT). Over time, the increasing number of IoT device users impacts access and delivery of valuable data. This is the primary goal of cybercriminals to operate malicious software. In addition to the positive impact of using technology, it is also a negative impact that creates new problems in security attacks and cybercrimes. One of the most dangerous cyberattacks in the IoT environment is the Mirai botnet malware. The malware turns the user's device into a botnet to carry out Distributed Denial of Service (DDoS) attacks on other devices, which is undoubtedly very dangerous. Therefore, this study proposes a k-nearest neighbor algorithm to classify Mirai malware-type DDOS attacks on IoT device environments. The malware classification process was carried out using rapid miner machine learning by conducting four experiments using SYN, ACK, UDP, and UDPlain attack types. The classification results from selecting five parameters with the highest activity when the device is attacked. In order for these five parameters to be a reference in the event of a malware attack starting in the IoT environment, the results of the classification have implications for further research. In the future, it can be used as a reference in making an early warning innovative system as an early warning in the event of a Mirai botnet attack.
Co-Authors Aditya Zhafir Dhiaulhaq Alam Rahmatulloh Alfis Salam Azwar Farrel Wirasena Berlian Maulidya Izzati Fa'rifah, Riska Yanu Fakhrurroja, Hanif Fauzan , Daffa Surya Fauzi, Rokhman Fitria Dewi Wulandari Ilham Perdana Indah Nur Fitri Astuti Intan Akbar Rusmana Irfan Darmawan Karina M., Rahma Kusuma, Kemal Indra M. Rizal Bimantoro Maharani, Anggi Kusuma Maulana Purbaya, Andre Mufti, Afrizal Muhammad Ramadhan, Galih Muhammad Rizal Bimantoro Natasya Kusuma Putri Nur Widiyasono, Nur Pariswara, Endar Poedjimartojo, Naufal Avilandi Pradita Cahyani Pratama, M Fajri Bryan Rachmadita Andreswari Rahmat Fauzi Rahmat Fauzi Taufik Nur Adi Vandha Pradwiyasma Widartha