Article Per Year (5 Year)
p-Index From 2021 - 2026
3.096
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization INVOTEK: Jurnal Inovasi Vokasional dan Teknologi Journal of Information Technology and Computer Engineering JITSI : Jurnal Ilmiah Teknologi Sistem Informasi International Journal of Advanced Science Computing and Engineering
Alde Alanda, Alde
Politeknik Negeri Padang, Indonesia
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Transportation

Published : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 23 Documents
Search

 1   2   3 
COVID-19 Social Distancing Tracking and Monitoring System (SDMOS-19) Binti Abdullah, Nurafrina Arrysya; Kamarudin, Nur Diyana Binti; Makhtar, Siti Noormiza; Mat Jusoh, Ruzanna; Alanda, Alde
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1199

Abstract

The Coronavirus disease (COVID-19), stemming from the SARS-CoV-2 virus, has garnered global concern as a virulent infectious ailment. Recognized as an epidemic by the World Health Organization (WHO), the persistently mutating virus sustains its transmission within communities. Individuals have been advised to uphold a safe interpersonal distance, notably around five feet, to mitigate its spread during social interactions. Addressing this imperative, an innovative automated social distancing detection system is conceived, leveraging the Convolutional Neural Network (CNN) algorithm. This system operates on two distinct input modes: static images and recorded videos recorded on closed-circuit television (CCTV). Remarkably, the proposed automated system adeptly quantifies and surveils the extent of social distancing among individuals in densely populated settings. A sophisticated framework accurately discerns social distancing compliance, delineating between hazardous and secure intervals via distinct red and green bounding box indicators. The culmination of this endeavor reveals an impressive 90% detection accuracy for both input modes. Notably, this proposed system holds substantial promise for implementation within sprawling premises such as expansive shopping malls or recreational parks. Seamlessly enforcing automated safety distance assessment expedites real-time insights to security departments and other relevant authorities. Consequently, the efficacy of citizens in upholding safe interpersonal distances can be promptly evaluated and, if necessary, corrective measures can be expeditiously instituted. This automated system ensures public health and safety maintenance, particularly in difficult circumstances.
Combining Deep Learning Models for Enhancing the Detection of Botnet Attacks in Multiple Sensors Internet of Things Networks Hezam, Abdulkareem A.; Mostafa, Salama A.; Baharum, Zirawani; Alanda, Alde; Salikon, Mohd Zaki
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.733

Abstract

Distributed-Denial-of-Service impacts are undeniably significant, and because of the development of IoT devices, they are expected to continue to rise in the future. Even though many solutions have been developed to identify and prevent this assault, which is mainly targeted at IoT devices, the danger continues to exist and is now larger than ever. It is common practice to launch denial of service attacks in order to prevent legitimate requests from being completed. This is accomplished by swamping the targeted machines or resources with false requests in an attempt to overpower systems and prevent many or all legitimate requests from being completed. There have been many efforts to use machine learning to tackle puzzle-like middle-box problems and other Artificial Intelligence (AI) problems in the last few years. The modern botnets are so sophisticated that they may evolve daily, as in the case of the Mirai botnet, for example. This research presents a deep learning method based on a real-world dataset gathered by infecting nine Internet of Things devices with two of the most destructive DDoS botnets, Mirai and Bashlite, and then analyzing the results. This paper proposes the BiLSTM-CNN model that combines Bidirectional Long-Short Term Memory Recurrent Neural Network and Convolutional Neural Network (CNN). This model employs CNN for data processing and feature optimization, and the BiLSTM is used for classification. This model is evaluated by comparing its results with three standard deep learning models of CNN, Recurrent Neural Network (RNN), and long-Short Term Memory Recurrent Neural Network (LSTM–RNN). There is a huge need for more realistic datasets to fully test such models' capabilities, and where N-BaIoT comes, it also includes multi-device IoT data. The N-BaIoT dataset contains DDoS attacks with the two of the most used types of botnets: Bashlite and Mirai. The 10-fold cross-validation technique tests the four models. The obtained results show that the BiLSTM-CNN outperforms all other individual classifiers in every aspect in which it achieves an accuracy of 89.79% and an error rate of 0.1546 with a very high precision of 93.92% with an f1-score and recall of 85.73% and 89.11%, respectively. The RNN achieves the highest accuracy among the three individual models, with an accuracy of 89.77%, followed by LSTM, which achieves the second-highest accuracy of 89.71%. CNN, on the other hand, achieves the lowest accuracy among all classifiers of 89.50%.
Web Application Penetration Testing Using SQL Injection Attack Alanda, Alde; Satria, Deni; Ardhana, M.Isthofa; Dahlan, Andi Ahmad; Mooduto, Hanriyawan Adnan
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.470

Abstract

A web application is a very important requirement in the information and digitalization era. With the increasing use of the internet and the growing number of web applications, every web application requires an adequate security level to store information safely and avoid cyber attacks. Web applications go through rapid development phases with short turnaround times, challenging to eliminate vulnerabilities. The vulnerability on the web application can be analyzed using the penetration testing method. This research uses penetration testing with the black-box method to test web application security based on the list of most attacks on the Open Web Application Security Project (OWASP), namely SQL Injection. SQL injection allows attackers to obtain unrestricted access to the databases and potentially collecting sensitive information from databases. This research randomly tested several websites such as government, schools, and other commercial websites with several techniques of SQL injection attack. Testing was carried out on ten websites randomly by looking for gaps to test security using the SQL injection attack. The results of testing conducted 80% of the websites tested have a weakness against SQL injection attacks. Based on this research, SQL injection is still the most prevalent threat for web applications. Further research can explain detailed information about SQL injection with specific techniques and how to prevent this attack.
Co-Authors - Liliwarti A. Mostafa, Salama Ab Razak, Mohd Faizal Abdullah, Zubaile Ade Irma Suryani Ahmad Firdaus, Ahmad Aldo Erianda Aldo Erianda Aldo Erianda, Aldo Alfajri Maulana Ibrahim Anthony, Tumusiime Ardhana, M.Isthofa Armoogum, Sheeba Armoogum, Vinaye Baharum, Zirawani Bappoo, Soodeshna Binti Abdullah, Nurafrina Arrysya Chandra, Anurag Dahlan, Andi Ahmad Deddy Prayama Defni, - Deni Satria Dewi, Deshinta Arrova Dwiny Meidelfi Erwadi Bakar Gunasekaran, Saraswathy Shamini Hadelina, Rizka Hamzah, Khairatun Hisan Hanriyawan Adnan Mooduto Hezam, Abdulkareem A. Hidra Amnur Humaira, Humaira Huriati, Putri Indri Rahmayuni Indri Rahmayuni Ismail, Mohd Arfian Iswandi Saputra Kamarudin, Nur Diyana Binti Kamarudin, Nur Khairani Khaleefah, Shihab Hamad Khattak, Umar Farooq Kurniawan, Tri Basuki Makhtar, Siti Noormiza Mat Jusoh, Ruzanna Meri Azmi Mohd Salikon, Mohd Zaki Mooduto, H.A Mooduto, Hanriyawan Adnan Mostafa, Salama A. Osman, Mohd Zamri Rahmat Hidayat Rasyidah Rasyidah, - Rizka Hadelina Rohayanti Hassan, Rohayanti Ronal Hadi Salamat, Mohamad Aizi Salikon, Mohd Zaki Sarah Latifah Marif Satwarnirat Satwarnirat Shahreen Kasim, Shahreen Sujon, Khaled Mahmud Yaacob, Siti Salwani Yance Sonatha Yuhefizar Yuhefizar Zainodin, Muhammad Edzuan