Computer Engineering, General Decision Sciences, Modeling and Simulation
25
Scopus Publications
410
Scholar Citations
11
Scholar h-index
11
Scholar i10-index
Scopus Publications
Emissions Trading Scheme and Carbon Tax: Economic and Social Analysis with Dynamic System Modeling Review of Integrative Business and Economics Research, 2026
A Comparative Analysis of AI Maturity Frameworks: toward Transformative Applications in Higher Education Imam Asrowardi, Titik Khawa Abd Rahman, Septafiansyah Dwi Putra, Aedah Abd Rahman, Muhammad Faisal, Alde Alanda, Nizirwan Anwar International Journal on Informatics Visualization, 2026 This research analyzes the usefulness of three leading Artificial Intelligence (AI) maturity frameworks: Gartner AI Maturity Model, MITRE AI Maturity Framework, and Accenture AI Maturity Framework about higher education. Gaps in technology systems, AI-enabled education, innovation, governance, administrative functions, and stakeholder engagement were examined through comparative analysis of the frameworks in the industry. These models were picked based on their expected relevance in the higher education sector and increased popularity in other industries. The methodology consisted of comparing the stages and dimensions of each framework, as well as their strengths and weaknesses in resolving specific problems of academic institutions. Following the results, an AI maturity model was suggested, which consisted of 5 stages: awareness, experimentation, adoption, integration, and transformation. The purpose of the model is to allow institutions to assess their current AI maturity levels and devise appropriate measures for effective utilization of AI in teaching, research, and administration. The model expands the scope of existing frameworks primarily focused on AI training and adopting ethical measures by providing a solution to gaps in curriculum integration and stakeholder ethics governance. With the adoption of this framework, higher education institutions can increase AI adoption and overcome issues of limited resources and ethical challenges. More exploration is necessary to test the framework across multiple educational contexts and to examine how AI can be implemented in specific regions
Decision Support Systems for Real-Time Fraud Response Muhammad Faisal, Nizirwan Anwar, Tomy Ronaldi, Imam Asrowardi, Hani Dewi Ariessanti, Rudi Kurniawan, Tuti Hartati, Varsha Arya, Bayu Pamungkas Combatting Digital Arrest Fraud and Identity Theft with AI and Decision Support Systems, 2026 As digital transactions become integral to modern life, identity theft and advanced fraud schemes demand agile, intelligent countermeasures. This chapter explores the design of Decision Support Systems (DSS) for real-time fraud response, with emphasis on digital arrest fraud and identity-based attacks. AI-driven DSS leverage machine learning, behavioural analytics, and anomaly detection to enable rapid detection and response. The chapter discusses architectural elements such as data integration, real-time analytics, and decision automation, along with explainability and compliance. Case studies from finance and government show how adaptive risk strategies and feedback loops enhance resilience. Predictive models aid in identity theft prevention, while DSS support teams with evidence-based responses. The chapter offers a framework for embedding AI-powered fraud detection in DSS, uniting innovation, policy, and human-centred design to boost digital trust.
Role of Machine Learning in Identity Verification Nurdiansyah Nurdiansyah, Nizirwan Anwar, Linda Arisanty Razak, Nini Apriani Rumata, Muhammad Faisal, Muhammad Syafaat S. Kuba, Agung Mulyo Widodo, Sourasis Chattopadhyay, Abhay Ratnaparkhi Combatting Digital Arrest Fraud and Identity Theft with AI and Decision Support Systems, 2026 Machine Learning (ML) is a vital enabler in modern identity verification, combating digital arrest fraud and identity theft through supervised, unsupervised, and deep learning models. These systems analyse complex datasets to detect anomalies, behavioural inconsistencies, and authenticate identities with high accuracy, adapting to evolving threats while minimising false positives and negatives. ML-powered solutions integrate biometric authentication, behavioural analytics, and risk scoring to deliver real-time decision support that balances security, user experience, and regulatory compliance. This strengthens digital security across banking, e-commerce, government, and healthcare sectors where identity breaches are costly. With predictive capabilities enabling proactive fraud prevention, ML stands as a cornerstone of adaptive cybersecurity. This chapter explores core frameworks, applications, case studies, and emerging trends, showing how ML-driven identity verification mitigates risks while enhancing trust, efficiency, and scalability in safeguarding digital ecosystems.
Stacking architecture-endpoint detection: a hybrid multi-layered architecture for endpoint threat detection Abd Rahman Wahid, Desi Anggreani, Muhyiddin A. M. Hayat, Aedah Abd Rahman, Muhammad Faisal International Journal of Advances in Applied Sciences, 2025 Modern endpoint threat detection systems face persistent challenges in balancing detection accuracy, resilience against zero-day attacks, and the interpretability of artificial intelligence (AI) models. Although deep learning (DL) approaches often achieve high accuracy on benchmark datasets, they remain vulnerable to adversarial perturbations and operate as opaque “black boxes,” thereby reducing trust and limiting practical adoption in critical infrastructures. This research introduces stacking architecture-endpoint detection (STACK-ED), a hybrid multi-layered architecture for endpoint threat detection. STACK-ED integrates three complementary paradigms: supervised learning for known attack patterns, self-supervised Fgraph-based learning for structural relationships, and unsupervised anomaly detection for emerging or unknown threats. The outputs are consolidated by a meta learner, followed by a post-hoc correction (PHC) mechanism to minimize false negatives. The framework was evaluated on a combined benchmark dataset (CSE-CIC-IDS2018 and UNSW-NB15, hereafter referred to as HIDS-Set). Experimental results demonstrate state-of-the-art performance, achieving an F2-score of 98.89% after hybrid integration and active learning, with the primary optimization objective being the reduction of undetected attacks. Furthermore, the Shapley additive explanations (SHAP) method enhances interpretability by revealing feature contributions, while the PHC successfully recovered 62.64% of missed zero-day candidates. The findings position STACK-ED not only as a highly accurate detection model but also as an adaptive, resilient, and transparent framework, offering practical implications for enterprise-grade endpoint defense and future zero-trust cybersecurity systems.
A Machine Learning-Driven Framework for Evaluating and Clustering City-Level Carbon Trading Strategies Muhammad Faisal, Ery Muchyar Hasiri, . Darniati, Billy Eden William Asrul, Hamdan Gani, Sri Wahyuni, Nurul Aini, Respaty Namruddin, Faisal Akib International Journal of Mathematical Engineering and Management Sciences, 2025 The global urgency to combat climate change has led to the widespread adoption of carbon emission trading schemes as market-based instruments. This study introduces a scalable and interpretable machine learning-based framework for evaluating, clustering, and predicting city-level carbon trading strategies. This study fills a gap in localized policy evaluation by combining economic, social, environmental, and political factors to make carbon regulation more adaptive and based on evidence. The hybrid framework that was suggested combines Multi-Objective Optimization (MOO), Multi-Criteria Decision Making (MCDM), Particle Swarm Optimization—Self-Organizing Maps (PSOM), and Random Forest classification. Criteria are weighted using a combination of Fuzzy Delphi Method (FDM) and Stepwise Weight Assessment Ratio Analysis (SWARA), with prioritization executed via MABAC. Cities are clustered through PSOM based on weighted indicators, and policy predictions are generated using Random Forest trained on these clusters. The framework effectively demonstrated regional differences and putting cities into separate policy groups based on their social, economic, environmental, and institutional characteristics. It also demonstrated strong predictive accuracy in recommending feasible carbon trading strategies using the Random Forest classifier. Combining fuzzy logic and machine learning made it possible to deal with the unpredictability and non-linearity that are common in city-level statistics. This study introduces a new and adaptable decision support system based on machine learning that improves the accuracy and responsiveness of evaluations in the carbon market. The integrated methodology furnishes policymakers with an all-encompassing instrument to evaluate and project localized strategies, thereby promoting more equitable and effective carbon governance across heterogeneous urban environments.
The Role of Speech Processing in the Metaverse Nizirwan Anwar, Yuhefizar Yuhefizar, Muhammad Faisal, Raden Teddy Iswahyudi, Sulis Mariyanti, Euis Heryati, Imam Asrowardi, Lili Hastuti, Dini Chairunnisa Advancements in Speech Processing for Human Computer Interaction, 2025 The metaverse, characterized by its immersive and interactive digital landscapes, necessitates advanced speech processing technologies to enhance Human-Computer Interaction (HCI). This sub-chapter explores the pivotal role of speech processing in the metaverse, focusing on natural interaction, emotional recognition, and multi-user communication. By employing Automatic Speech Recognition (ASR) and real-time translation, these technologies facilitate seamless engagement across diverse linguistic and cultural backgrounds, promoting accessibility and inclusivity. Additionally, the integration of affective computing enables systems to tailor responses to users' emotional states, enriching social dynamics. However, challenges related to technical limitations and ethical considerations persist, urging the need for rigorous research and development. By investigating advancements, applications, and implications, this sub-chapter aims to provide a comprehensive understanding of how speech processing contributes to shaping the metaverse's evolving landscape.
Assessing AI-Driven Personalization in Smart Cities Using Hybrid Machine Learning and MCDM Approach Muhammad Faisal, Sahabuddin, Ery Muchyar Hasiri, Darniati, Titik Khawa Abd Rahman, Ida Mulyadi, Billy Eden William Asrul, Sri Wahyuni, I Dewa Made Widia Hightech and Innovation Journal, 2025 This study aims to assess AI-driven personalization strategies in smart cities, focusing on promoting digital inclusion across diverse urban populations. As artificial intelligence becomes increasingly central to urban service delivery, ensuring equitable and effective personalization is critical to preventing the amplification of digital inequality. To address this challenge, a hybrid evaluation framework is proposed, integrating Multi-Criteria Decision Making (MCDM) techniques, specifically Step-wise Weight Assessment Ratio Analysis (SWARA), Linguistic q-Rung Orthopair Fuzzy Numbers (Lq-ROFNs), and the Multi-Attributive Border Approximation Area Comparison (MABAC) with a Machine Learning (ML) classification model based on Random Forest. The framework is applied to stakeholder input from ten Indonesian smart cities, evaluating personalization readiness across five dimensions: accessibility, affordability, user engagement, privacy, and personalization effectiveness. The results indicate that accessibility and user engagement are the most influential criteria, while affordability and privacy are areas requiring strategic policy focus. The integrated model classifies cities by readiness level and identifies sensitivity patterns relevant to inclusive digital policy-making. The novelty of this research lies in its synthesis of MCDM and ML approaches to produce a transparent, scalable, and data-driven tool for evaluating AI personalization. This contributes to inclusive smart city development by aligning AI implementation with broader social equity objectives.
Machine Learning for Threat Detection and Intrusion Prevention Muhammad Faisal, I Dewa Made Widia, Nizirwan Anwar, Titik Khawa Abdul Rahman, Aedah Abd Rahman, Alders Paliling, Raden Teddy Iswahyudi, Anshika Sharma Ensuring Secure Connectivity Through AI Powered Wireless Systems, 2025
Secure AI-Enabled Communication in Smart Cities and Industry 4.0 Musdalifa Thamrin, Nizirwan Anwar, Nurdiansyah Nurdiansyah, Imam Asrowardi, Muhammad Faisal, Sahabuddin Sahabuddin, Tomy Ronaldi, Alok Kumar, Shubham Bajpai Ensuring Secure Connectivity Through AI Powered Wireless Systems, 2025
Student Emotion Recognition from Low-Quality Videos Using Multimodal Deep Learning AMTM TAIBA, RY Bakti, M Faisal, MSS Kuba, L Anas, FI Rahman JURNAL INFOTEL 18 (1), 157-179 , 2026 2026
Pemodelan dan Prediksi Kunjungan Pasien di Puskesmas Menggunakan Hidden Markov Model M Faisal, FI Rachman, T Wahyuni Arus Jurnal Sains dan Teknologi 4 (1), 81-87 , 2026 2026
Analisis Hubungan Obesitas dan Diabetes Melitus Berdasarkan Usia dan Jenis Kelamin Menggunakan Algoritma Apriori EY Kotte, FI Rachman, M Faisal, T Wahyuni Arus Jurnal Sains dan Teknologi 4 (1), 71-80 , 2026 2026 Citations: 1
PERBANDINGAN CNN DAN YOLO PADA SISTEM PENGENALAN WAJAH BERBASIS PRESENSI RY Bakti, T Wahyuni, M Faisal Jurnal Informatika Progres 18 (1), 93-101 , 2026 2026
PENERAPAN ALGORITMA MOBILENETV2 UNTUK KLASIFIKASI HURUF HIJAIYAH BERBASIS GESTUR TANGAN M Riswan, T Wahyuni, C Danuputri, EAH Talib, M Faisal, L Anas, A Agung Jurnal Informatika Progres 18 (1), 102-110 , 2026 2026
PENERAPAN MODEL ESRGAN UNTUK UPSCALING CITRA DAN VIDEO DIGITAL S Suhardi, EAH Talib, FI Rachman, T Wahyuni, M Faisal, MSS Kuba Jurnal Informatika Progres 18 (1), 119-125 , 2026 2026
MONITORING DAN NOTIFIKASI REAL-TIME PERUBAHAN FILE PADA WEB SERVER MENGGUNAKAN WATCHDOG DAN TELEGRAM BOT SEBAGAI SISTEM PERINGATAN DINI S Hasbir, EAH Talib, FI Rachman, T Wahyuni, M Faisal, MSS Kuba Jurnal Informatika Progres 18 (1), 126-131 , 2026 2026
KLASIFIKASI TANAMAN OBAT TRADISIONAL BERBASIS CITRA BUAH DAN DAUN N Kusumawardani, C Danuputri, M Faisal, MAM Hayat, MSS Kuba, ... Jurnal Informatika Progres 18 (1), 79-92 , 2026 2026
IMPLEMENTASI SISTEM DETEKSI PRODUK BOIKOT BERBASIS WEBSITE REAL-TIME MENGGUNAKAN METODE YOLOv10 AN Rahman, EAH Talib, FI Rachman, RY Bakti, M Faisal, MSS Kuba Jurnal Informatika Progres 18 (1), 47-53 , 2026 2026
T4EDR: Hybrid Threat Detection Framework for EDR Based on Semantic Rule Embedding and Contextual Network Flow Analysis MDA Nuzul, D Anggreani, M Faisal, AR Wahid, TK Abd Rahman Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI 15 (1) , 2026 2026
Integrating multi-criteria decision making and public sentiment analysis for sustainable urban green space planning MSS Kuba, M Faisal, N Nurnawaty, TKA Rahman, AM Syamsuri, ... Bulletin of Electrical Engineering and Informatics 15 (2), 1786-1802 , 2026 2026
A Comparative Analysis of AI Maturity Frameworks: Toward Transformative Applications in Higher Education I Asrowardi, TK Abd Rahman, SD Putra, A Abd Rahman, M Faisal, ... JOIV: International Journal on Informatics Visualization 10 (2), 727-738 , 2026 2026
PENINGKATAN KETERAMPILAN MENDESAIN FOTO PRODUK UNTUK MENINGKATKAN POTENSI PENJUALAN M Rofi'i, PR Fadhilah, L Deviastri, IT Annisa, AN Gani, M Faisal Martabe: Jurnal Pengabdian Kepada Masyarakat 9 (3), 1190-1198 , 2026 2026
DESIGN AND IMPLEMENTATION OF AN INTERACTIVE WEB-BASED DATA MINING SYSTEM USING KNN, SVM, AND RANDOM FOREST WITH STREAMLIT M Faisal, K Maulana CODEX: Journal of Software Engineering 1 (01), 11-19 , 2026 2026 Citations: 1
Pembuatan Briket Berbahan Arang Bambu dan Batok Kelapa sebagai Energi Alternatif M Faisal, H Assiddiq, J Anggara, H Mubarak, W Khawirian Infotekmesin 17 (1), 119-125 , 2026 2026
Pengukuran Tingkat Kepuasan Dalam Implementasi Ar Pada Aplikasi I-Colour Menggunakan Metode Sus AD Prastyo, W Widyastuty, M Faisal Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) 8 (1 … , 2026 2026
Role of Machine Learning in Identity Verification N Nurdiansyah, N Anwar, LA Razak, NA Rumata, M Faisal, MSS Kuba, ... Combatting Digital Arrest Fraud and Identity Theft With AI and Decision … , 2026 2026
Decision Support Systems for Real-Time Fraud Response M Faisal, N Anwar, T Ronaldi, I Asrowardi, HD Ariessanti, R Kurniawan, ... Combatting Digital Arrest Fraud and Identity Theft With AI and Decision … , 2026 2026
The Role of Speech Processing in the Metaverse N Anwar, Y Yuhefizar, M Faisal, RT Iswahyudi, S Mariyanti, E Heryati, ... Advancements in Speech Processing for Human-Computer Interaction, 233-266 , 2026 2026
Machine Learning for Threat Detection and Intrusion Prevention M Faisal, IDM Widia, N Anwar, TKA Rahman, A Abd Rahman, A Paliling, ... Ensuring Secure Connectivity Through AI-Powered Wireless Systems, 1-48 , 2026 2026
MOST CITED SCHOLAR PUBLICATIONS
Utilising hybrid machine learning to identify anomalous multivariate time series in geotechnical engineer PP Anand, G Jayanth, KS Rao, P Deepika, M Faisal, M Mokdad AVE Trends In Intelligent Computing Systems 1 (1), 32-41 , 2024 2024.0 Citations: 47
Faster R-CNN Algorithm for Detection of Plastic Garbage in the Ocean: A Case for Turtle Preservation M Faisal, S Chaudhury, KS Sankaran, S Raghavendra, RJ Chitra, ... Mathematical Problems in Engineering 2022, 11 , 2022 2022.0 Citations: 47
Determining rural development priorities using a hybrid clustering approach: a case study of South Sulawesi, Indonesia M Faisal, TKA Rahman International Journal of Advanced Technology and Engineering Exploration 10 … , 2023 2023.0 Citations: 19
Optimally Enhancement Rural Development Support Using Hybrid Multy Object Optimization (MOO) and Clustering Methodologies: A Case South Sulawesi-Indonesia. M Faisal, TKA Rahman International Journal of Sustainable Development & Planning 18 (6) , 2023 2023.0 Citations: 19
A Hybrid MOO, MCGDM, and Sentiment Analysis Methodologies for Enhancing Regional Expansion Planning: A Case Study Luwu - Indonesia M Faisal, Irmawati, TKA Rahman, Jufri, Sahabuddin, Herlinah, I Mulyadi International Journal of Mathematical, Engineering and Management Sciences … , 2025 2025.0 Citations: 18
A Novelty Decision-Making Based on Hybrid Indexing, Clustering, and Classification Methodologies: An Application to Map the Relevant Experts Against the Rural Problem M Faisal, TKA Rahman, I Mulyadi, K Aryasa, Irmawati, M Thamrin Mak. Appl. Manag. Eng 7 (2), 132-171 , 2024 2024.0 Citations: 17
Klasifikasi penyakit diabetes menggunakan algoritma decision tree N Nurussakinah, M Faisal Jurnal Informatika 10 (2), 143-149 , 2023 2023.0 Citations: 17
Analyzing the effectiveness of collaborative filtering and content-based filtering methods in anime recommendation systems HD Putri, M Faisal Jurnal Komtika (Komputasi dan Informatika) 7 (2), 124-133 , 2023 2023.0 Citations: 17
Analisis perbandingan kecepatan algoritma selection sort dan bubble sort N Mahrozi, M Faisal Scientica: Jurnal Ilmiah Sains dan Teknologi 1 (2), 89-98 , 2023 2023.0 Citations: 16
Design and Implementation of Plantation Commodities Price Information Broadcaster via Autoreply Short Message Service on Smartphone M Faisal, F Shabir 2018 2nd East Indonesia Conference on Computer and Information Technology … , 2018 2018.0 Citations: 13
Perbandingan analisis sentimen PLN Mobile: Machine learning vs. deep learning I Akbar, M Faisal JOINTECS (Journal of Information Technology and Computer Science) 8 (1), 1-10 , 2024 2024.0 Citations: 11
Microsoft Copilot training for monitoring student learning: a case study vocational high school Makassar-Indonesia D Moeis, N Usman, M Faisal, A Harmin, I Mulyadi, M Thamrin I-Com: Indonesian Community Journal 4 (3), 1911-1922 , 2024 2024.0 Citations: 9
A hybrid hue saturation lightness, gray level co-occurrence matrix, and k-nearest neighbour for palm-sugar classification M Jumarlis, I Mulyadi, Mirfan, Irmawati, Mardiah, M Faisal, H Anisa Int J Artif Intell ISSN 2252 (8938), 2935 , 2024 2024.0 Citations: 8
Analisis Kinerja Website Parama Pelindo Menggunakan Pingdom Tools Dan Pagespeed Insights NQ Haeruddin, MR Faizal, SH Baharuddin Jurnal Informatika Progres 15 (1), 33-40 , 2023 2023.0 Citations: 8
Utilizing Machine Learning-Based Decision-Making to Align Higher Education Curriculum with Industry Requirements M Faisal, TKA Rahman, D Zainal, H Mubarak, F Shabir, N Anwar, ... International Journal of Modern Education and Computer Science (IJMECS) 17 … , 2025 2025.0 Citations: 7
A hybrid model for palm sugar type classification: Advancing image-based analysis for industry applications I Mulyadi, M Thamrin, M Faisal, S Yunarti, A Abd Djalil, S Mallu Ingenierie des Systemes d'Information 29 (5), 1937 , 2024 2024.0 Citations: 7
Perancangan Desain 3D Modelling Sebagai Media Ilustrasi Pada CV. Pacific Alumunium M Faisal, WS Utami, R Supriati, K Kunci MAVIB Journal 3 (1), 2022 , 2022 2022.0 Citations: 7
Implementasi Model Interoperabilitas pada sistem informasi akademik berbasis multi platform M Faisal Jurnal Informatika Progres 6 (2), 59-68 , 2014 2014.0 Citations: 6
Irmawati, & Thamrin, M.(2024). A novelty decision-making based on hybrid indexing, clustering, and classification methodologies: an application to map the relevant experts … M Faisal, TKA Rahman, I Mulyadi, K Aryasa Decision Making: Applications in Management and Engineering 7 (2), 132-171 , 0 Citations: 6
Advanced Clustering TeoridanAplikasi I Edy, M Faisal Sleman: Deepublish , 2005 2005.0 Citations: 5
