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Arif Ridho Lubis
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AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal
Published by Yayasan Asmin Intelektual Berkah
ISSN : -     EISSN : 30628555     DOI : 10.62123/aqila
Core Subject : Economy, Science, Engineering,
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance
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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Jaringan Komputer dan Komunikasi
Articles 28 Documents
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Internet of Things for Urban Infrastructure: Applications, Challenges, and Future Directions – A Review Alif Noorachmad Muttaqin; Alaric Rasendriya Aniko; Muhamad Ivan Fadilah; Fauzi Hizbullah; Sahidan Abdulmana
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 Cut Try Utari; Indri Sulistianingsih; 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.
IoT-Enabled Smart Waste Sorting System Using Proximity and Ultrasonic Sensors for Campus Environments Donny Sanjaya; Rio Herlambang; Heykel Prayogi Timanta G.S; Muhammad Khoiril Amri
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 3 No. 1 (2026): VOLUME 3, NO 1: JUNE 2026
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v3i1.125

Abstract

The rapid increase in population and urbanization has led to significant challenges in waste management, especially in developing countries like Indonesia. According to the World Bank, global waste generation reached 2.56 billion metric tons in 2022, and under a business-as-usual scenario, this figure is projected to rise to 3.86 billion metric tons by 2050. At Politeknik Negeri Medan, a growing concern has emerged over the inefficiency of traditional waste disposal systems, which often result in environmental pollution and ineffective sorting processes. This paper proposes the design of an IoT-based Smart Waste Sorting System tailored for campus environments as a pilot model for broader smart city implementation. The proposed system integrates IoT technologies such as sensors, microcontrollers, and wireless communication to automatically detect, identify, and sort waste into appropriate categories: organic, inorganic, and recyclable. Data from smart bins are transmitted in real-time to a central monitoring dashboard, enabling efficient waste collection scheduling and reducing overflow incidents. The system also includes a fire detection feature for safety and a data analytics module to forecast waste generation trends. By implementing this system at Politeknik Negeri Medan, we aim to enhance environmental awareness, optimize waste handling processes, and support sustainable campus initiatives. The results demonstrate that IoT integration in waste sorting contributes significantly to improving operational efficiency and can serve as a scalable model for smart waste management in urban areas.
Sentiment Analysis of Fintech Application Users in Indonesia Using Machine Learning Algorithms Made Marshall Vira Deva; Lukman Abdurrahman; Hanif Fakhrurroja
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 3 No. 1 (2026): VOLUME 3, NO 1: JUNE 2026
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v3i1.171

Abstract

This study focuses on Indonesian users' sentiments regarding 9 fintech apps based on their Google Play Store reviews. The rapid growth of the fintech industry in Indonesia makes it crucial to understand user perceptions and satisfaction. Around 2,554 reviews from users of Kredivo, ShopeePay, Dana, GoPay, LinkAja, Bareksa, Flip, Jenius, and OVO were analyzed. The user review text and data were preprocessed using text cleaning, slang normalization, stopword removal, stemming, and the Sastrawi library and were moved through the TF-IDF vectorizer (term frequency-inverse document frequency). The four algorithms were Naive Bayes, Logistic Regression, Support Vector Machine (SVM), and Random Forest. The results showed that SVM (Linear) achieved the best overall balanced performance with an accuracy of 80.23%, precision of 77.79%, recall of 80.23%, and the highest F1-score of 78.53%, outperforming Naive Bayes (accuracy 81.21%, F1-score 78.32%), Logistic Regression (accuracy 80.43%, F1-score 77.81%), and Random Forest (accuracy 78.08%, F1-score 75.81%). While Naive Bayes recorded the highest raw accuracy, SVM was selected as the best model due to its superior F1-score, which provides a more balanced evaluation across all sentiment classes. Machine learning provided a snapshot of the reviews’ sentiments, with 42.4% positive, 51.4% negative, and 6.1% neutral. Kredivo and ShopeePay had the most favorable sentiments of 72.4% and 70.9%. The most salient sentiment indicators include 'bagus' (good) and 'bantu' (help) as top positive classifiers, while 'buruk' (bad) and 'kecewa' (disappointed) emerged as the most prominent negative classifiers, with 'mudah' (easy) and 'cepat' (fast) also strongly associated with positive sentiment. The results of this study give fintech firms a better grasp of user satisfaction, and fintech user positive sentiments.
Application In Distinguishing Artificial Intelligence-Makened Images And Original Images With Visual Feature Extraction Using Ensemble Learning Algorithm Moh Hafiz Naufal; Al-Khowarizmi
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 3 No. 1 (2026): VOLUME 3, NO 1: JUNE 2026
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v3i1.174

Abstract

The development of generative Artificial Intelligence (AI) technology enables computer systems to produce highly realistic images that closely resemble real photographs. This condition creates challenges in distinguishing AI-generated images from real images visually. This research aims to develop an image classification system capable of distinguishing AI-generated images and real images using visual feature extraction and ensemble learning algorithms.The research method consists of several stages including image preprocessing by resizing images to 256 × 256 pixels, visual feature extraction including RGB color histogram, grayscale intensity distribution, texture features using Gray Level Co-occurrence Matrix (GLCM), and edge features using the Canny Edge Detection method. The extracted features are then used as input for several classification algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest. Furthermore, model combination is performed using an ensemble learning method with a hard voting technique.The experimental results show that the Random Forest model achieved an accuracy of 65.71%, while the ensemble learning method achieved an accuracy of 65.00% with an F1-score of 0.6918. The developed system is also implemented as a web-based application using the Streamlit framework, allowing users to upload images and obtain prediction results directly. The results indicate that the combination of visual feature extraction and ensemble learning can be used as an approach to help identify AI-generated images and real images.
Code Plagiarism Detection Using Graphic Neural Network Based On Abstract Syntax Tree Fitra Affandi Hasibuan; Al-Khowarizmi
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 3 No. 1 (2026): VOLUME 3, NO 1: JUNE 2026
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v3i1.177

Abstract

Code plagiarism is a common issue in education and software development, which is difficult to detect accurately using text-based approaches. Conventional methods such as Term Frequency–Inverse Document Frequency (TF-IDF) and cosine similarity tend to focus only on token similarity, making them less effective in handling structural changes in code. Therefore, this study aims to develop a structure-based code plagiarism detection system using Abstract Syntax Tree (AST) and Graph Neural Network (GNN). The proposed method involves parsing source code into AST, representing it as a graph, and processing it using a GNN model in a pairwise scheme. In addition, a comparison is conducted with a baseline method based on TF-IDF and cosine similarity to evaluate model performance. The dataset used consists of both synthetic and real data, which are divided into training and testing sets. The results show that the GNN model achieves excellent performance with an accuracy of 0.9946, precision of 0.9949, recall of 0.9974, and F1-score of 0.9962, while the baseline method only achieves an accuracy of 0.7392 and a recall of 0.6343. These results indicate that the GNN model is more effective in detecting plagiarism, especially in handling structural code modifications. Therefore, it can be concluded that the structure-based approach using AST and GNN outperforms text-based approaches in code plagiarism detection.
Deepfake Technology: Ethical Issues and Legal Gaps in Indonesian Cyber Law Made Marshall Vira Deva; irfan venny rahmayanti; Intan Giri Anjani; Sutan Faiz Rasyid; Muharman lubis
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 3 No. 1 (2026): VOLUME 3, NO 1: JUNE 2026
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v3i1.181

Abstract

The rapid advancement of artificial intelligence (AI) has enabled the emergence of deepfake technology, which allows the manipulation of images, audio, and video to produce highly realistic yet fabricated content. In Indonesia, the proliferation of deepfakes poses significant ethical and legal challenges. This study examines the ethical implications of deepfake technology and identifies gaps in Indonesian cyber law, specifically within the Electronic Information and Transactions Law (UU ITE No. 11/2008 as amended by UU No. 19/2019 and UU No. 1/2024), the Pornography Law (UU No. 44/2008), and the Personal Data Protection Law (UU PDP No. 27/2022). Using a normative juridical research method with qualitative analysis of primary legal sources and secondary literature, this study finds that existing Indonesian legislation does not explicitly regulate deepfakes, creating a legal vacuum that leaves victims predominantly women without adequate legal protection. The findings highlight the urgent need for specific regulatory provisions addressing deepfake creation, distribution, and non-consensual intimate imagery (NCII). This paper concludes by proposing recommendations for legislative reform and ethical frameworks to guide both policymakers and technology users in Indonesia.
Customizing Odoo CRM for Pipeline, Opportunity, Project Delivery, and After-Sales Management in a Project-Based Service Company: A Case Study of PT XYZ Ferri Fatra; Fadhil Muhammad Akbar; Rohimin Imani Arti; M. Galih Fikran Syah
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 3 No. 1 (2026): VOLUME 3, NO 1: JUNE 2026
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v3i1.194

Abstract

Project-based service companies manage long sales cycles, multi-channel prospects, quotation revisions, project handover, delivery monitoring, and after-sales opportunities. This study designs and demonstrates a customized Customer Relationship Management (CRM) artifact using Odoo CRM for PT XYZ, an anonymized project-based service company, following Design Science Research. The artifact instantiates an end-to-end pipeline (New Lead, Qualified, Needs Analysis, Proposal/Quotation Sent, Negotiation, Won/Contract Signed, Handover to Delivery, Project Delivery, After Sales/Retention, and Lost) together with opportunity fields, stage-gated documents, activity-based follow-up, quotation conversion, lost-reason capture, and reporting dashboards. Rather than treating the configuration as the result, the study abstracts it into six transferable design principles: stage completeness, stage-gated evidence, mandatory next action, commercial-to-delivery continuity, loss-as-learning, and decision-support reporting. The associated benefits, such as follow-up discipline, quotation traceability, handover quality, delivery visibility, and forecasting support, are presented as design propositions with proposed measurement indicators rather than empirically demonstrated outcomes, and the artifact is evaluated ex ante against defined criteria. The contribution is twofold: a replicable Odoo configuration and a nascent set of design principles for project-based service CRM. Because the evaluation is ex ante, future work should validate the artifact through user acceptance testing and longitudinal performance data.

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