cover
Contact Name
Arif Ridho Lubis
Contact Email
aqila@yasib.com
Phone
+6285373332208
Journal Mail Official
editor.enigma@yasib.com
Editorial Address
Jalan Pasar III Tapian Nauli, Komplek White House Garden Blok B No 12, 20128, Medan
Location
Kota medan,
Sumatera utara
INDONESIA
AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal
ISSN : 30628555     EISSN : -     DOI : 10.62123/aqila
Acceleration, Quantum, Information Technology and Algorithm Journal (AQILA)is open to researchers and experts in the fields of computer science, information engineering, quantum computing, and information systems. Serving as a platform for scholars and practitioners, this journal facilitates the dissemination of research findings pertaining to cutting-edge advancements in Communication Engineering, Computer Science and Information Systems, Signal, Image and Video Processing, Electrical Power Engineering, Instrumentation and Control Engineering, Computer Network and System Engineering, Machine Learning, AI and Soft Computing, Electronics Engineering and Internet of Things (IoT). The publication schedule of the journal comprises two periods: June and December. Upon submission, manuscripts undergo a rigorous check for similarities utilizing the Turnitin application. The review process entails two rounds of evaluation. AQILA Journal welcomes submissions addressing the latest technological innovations and emerging issues within its thematic scope. Prospective authors are required to meticulously review and adhere to the submission guidelines and templates provided. Manuscripts that fail to meet the stipulated writing guidelines are subject to rejection by the editorial team. AQILA Journal invites manuscripts exploring topics such as signal processing, electronics, electricity, telecommunications, instrumentation & control, computing, and informatics.
Articles 22 Documents
Internet of Things for Urban Infrastructure: Applications, Challenges, and Future Directions – A Review Noorachmad Muttaqin, Alif; Rasendriya Aniko, Alaric; Ivan Fadilah, Muhamad; Hizbullah, Fauzi; Abdulmana, Sahidan
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 2 No. 2 (2025): VOLUME 2, NO 2: DECEMBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v2i2.102

Abstract

The Internet of Things (IoT) has emerged as a transformative technology in the development of urban infrastructure, enabling real-time data collection, intelligent decision-making, and integrated service delivery. This study explores the implementation of IoT in various urban domains, including transportation management, environmental monitoring, smart parking, structural health surveillance, and smart city integration. The findings highlight significant improvements in operational efficiency, system resilience, and environmental sustainability. However, large-scale adoption still encounters challenges such as cybersecurity risks, interoperability issues, device reliability, and maintenance demands, along with socio-economic barriers including high implementation costs, limited technical expertise, and complex regulatory frameworks. To address these challenges, the study recommends adopting advanced technologies such as edge computing, artificial intelligence, and blockchain, establishing global interoperability standards, and fostering cross-sector collaborations. Furthermore, innovative financing models and inclusive public policies are essential to ensure secure, efficient, and sustainable IoT deployment. The research contributes to a deeper understanding of the role of IoT in shaping future smart cities, providing a framework for policymakers, urban planners, and technology developers.
Cyber Attack Prediction Using Machine Learning: A Comparative Study of Bayesian Network and Support Vector Machine Try Utari, Cut; Sulistianingsih, Indri; Diva Rofsyahfitri; Nurul Rizkina Kalsum Batubara; Wizdanil Yumna Nawar
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 2 No. 2 (2025): VOLUME 2, NO 2: DECEMBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v2i2.123

Abstract

Cybersecurity is becoming a critical issue with the increasing reliance on digital systems that are vulnerable to attacks. Proactive cyberattack prediction is one of the main approaches in early detection systems, where machine learning plays a strategic role. This research compares two popular machine learning algorithms, namely Bayesian Network and Support Vector Machine (SVM), to determine the most effective algorithm in predicting cyberattacks. This research uses two benchmark datasets, namely UNSW-NB15 and KDD99, as well as real attack data from Elazığ, Turkey. The analysis shows that the Bayesian Network implemented through the MCVAE_PBNN approach achieves up to 96% accuracy on the UNSW-NB15 dataset, with the advantage of detecting distributed and uncertain attacks. On the other hand, the SVM linear (SVML) algorithm showed a prediction accuracy of 95.02% in attack method classification, excelling in the case of data with clearly defined features. This study also analyzes the advantages and limitations of both algorithms, and provides implementation recommendations based on the needs of the detection system. The findings reinforce the urgency of developing adaptive predictive models in modern cybersecurity.

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