Digital Licensing as Institutional Reconfiguration: Urban Spatial Planning Governance Beyond Legal Certainty Didi Nursidi, Taufik Hidayat, Roqiyul Ma’arif Syam International Research Journal of Multidisciplinary Scope, 2026 Digitalization is increasingly becoming an important strategy in reforming urban spatial planning permits, alongside efforts to modernize public administration and governance. However, existing studies generally still assess digital licensing primarily from the perspective of administrative efficiency and legal certainty, thus paying insufficient attention to its institutional implications for governance practices. This research examines digital licensing from an institutional perspective, emphasizing how digital systems and devices are changing governance practices in urban spatial planning. This study employs a qualitative methodology, focusing on institutional analysis of publicly accessible policy documents, institutional records, and digital licensing workflows. The research findings indicate that digital licensing serves as an institutional reconfiguration mechanism that influences the exercise of authority, interagency coordination patterns, and governance capacity through system-based workflows. These changes primarily occurred at the operational and institutional levels, although they were not always accompanied by explicit formal regulatory changes. This research contributes to the digital governance and urban governance literature by moving beyond approaches that solely emphasize legal certainty and positioning digital systems as active institutional structures that mediate administrative practices and decision-making processes. The proposed analytical framework is also relevant for other contexts that are implementing digital-based spatial planning licensing reforms in contemporary governance practices.
A NOVEL HYBRID MODEL FOR HIGH-ACCURACY MALWARE DETECTION IN THE INTERNET OF MEDICAL THINGS (IOMT) ENVIRONMENT Amarudin Daulay, Kalamullah Ramli, Dodi Sudiana, Ruki Harwahyu, Taufik Hidayat, Nurwan Reza Fachrurrozi Iium Engineering Journal, 2025 The Internet of Medical Things (IoMT) has revolutionized modern healthcare by enabling the collection and analysis of real-time data. However, this interconnected ecosystem also introduces significant security risks, particularly malware attacks that compromise patient safety and data privacy. Traditional security measures are often insufficient because of resource constraints and the real-time operational demands of IoMT devices. This research proposes an optimized hybrid machine learning framework that integrates convolutional neural networks (CNN), long short-term memory (LSTM), random forest (RF), and principal component analysis (PCA) to enhance malware detection in IoMT environments. The proposed method includes an adaptive feature selection mechanism, a resource-efficient architecture, and an ensemble learning model with machine learning capabilities. Validation through experimentation using the CIC-MalMem-2022 dataset, which comprises labeled memory dumps from benign and various malware processes, demonstrated that the proposed framework outperformed current hybrid models while reducing computational costs, achieving a detection accuracy of 99.59%. This study presents a scalable and efficient security solution designed to address the constraints of IoMT devices, addressing critical challenges in healthcare cybersecurity. ABSTRAK: Internet Benda Medikal (IoMT) telah merevolusikan penjagaan kesihatan moden dengan membolehkan pengumpulan dan analisis data masa nyata. Walau bagaimanapun, ekosistem saling berkaitan ini juga memperkenalkan risiko keselamatan yang ketara, terutamanya serangan perisian hasad yang menjejaskan keselamatan pesakit dan privasi data. Langkah keselamatan tradisional selalunya tidak mencukupi kerana kekangan sumber dan permintaan operasi masa nyata peranti IoMT. Penyelidikan ini mencadangkan rangka kerja pembelajaran mesin hibrid yang dioptimumkan dengan menyepadu Rangkaian Konvolusi Neural (CNN), Memori Jangka Panjang Pendek (LSTM), Rawak Forest (RF) dan Analisis Komponen Prinsipal (PCA) bagi meningkatkan pengesanan perisian Malware dalam persekitaran IoMT. Kaedah yang dicadangkan ini termasuk mekanisme pemilihan ciri penyesuaian, seni bina cekap sumber dan keupayaan pembelajaran mesin bersama model pembelajaran ansembel. Ujian melalui eksperimen menggunakan dataset CIC-MalMem-2022, yang terdiri dari pelupusan memori berlabel daripada proses tidak merbahaya dan pelbagai Malware, menunjukkan bahawa kajian yang dicadangkan mengatasi model Hibrid semasa, juga menurunkan kos pengiraan, mencapai ketepatan pengesanan 99.59%. Kajian ini menyumbang kepada penyelesaian keselamatan berskala dan cekap yang disesuaikan dengan kekurangan peranti IoMT, menangani cabaran kritikal dalam keselamatan siber penjagaan kesihatan.
Strategy for Precopy Live Migration and VM Placement in Data Centers Based on Hybrid Machine Learning Taufik Hidayat, Kalamullah Ramli, Ruki Harwahyu Informatics, 2025 Data center virtualization has grown rapidly alongside the expansion of application-based services but continues to face significant challenges, such as downtime caused by suboptimal hardware selection, load balancing, power management, incident response, and resource allocation. To address these challenges, this study proposes a combined machine learning method that uses an MDP to choose which VMs to move, the RF method to sort the VMs according to load, and NSGA-III to achieve multiple optimization objectives, such as reducing downtime, improving SLA, and increasing energy efficiency. For this model, the GWA-Bitbrains dataset was used, on which it had a classification accuracy of 98.77%, a MAPE of 7.69% in predicting migration duration, and an energy efficiency improvement of 90.80%. The results of real-world experiments show that the hybrid machine learning strategy could significantly reduce the data center workload, increase the total migration time, and decrease the downtime. The results of hybrid machine learning affirm the effectiveness of integrating the MDP, RF method, and NSGA-III for providing holistic solutions in VM placement strategies for large-scale data centers.
Novel Federated Graph Contrastive Learning for IoMT Security: Protecting Data Poisoning and Inference Attacks Amarudin Daulay, Kalamullah Ramli, Ruki Harwahyu, Taufik Hidayat, Bernardi Pranggono Mathematics, 2025 Malware evolution presents growing security threats for resource-constrained Internet of Medical Things (IoMT) devices. Conventional federated learning (FL) often suffers from slow convergence, high communication overhead, and fairness issues in dynamic IoMT environments. In this paper, we propose FedGCL, a secure and efficient FL framework integrating contrastive graph representation learning for enhanced feature discrimination, a Jain-index-based fairness-aware aggregation mechanism, an adaptive synchronization scheduler to optimize communication rounds, and secure aggregation via homomorphic encryption within a Trusted Execution Environment. We evaluate FedGCL on four benchmark malware datasets (Drebin, Malgenome, Kronodroid, and TUANDROMD) using 5 to 15 graph neural network clients over 20 communication rounds. Our experiments demonstrate that FedGCL achieves 96.3% global accuracy within three rounds and converges to 98.9% by round twenty—reducing required training rounds by 45% compared to FedAvg—while incurring only approximately 10% additional computational overhead. By preserving patient data privacy at the edge, FedGCL enhances system resilience without sacrificing model performance. These results indicate FedGCL’s promise as a secure, efficient, and fair federated malware detection solution for IoMT ecosystems.
Reinforcement Learning-Driven Hybrid Precopy/Postcopy VM Migration for Energy-Efficient Data Centers Taufik Hidayat, Kalamullah Ramli, Ruki Harwahyu, Muhammad Salman, Teddy Surya Gunawan IEEE Access, 2025 This study proposes the use of a hybrid precopy/postcopy virtual machine (VM) migration framework to aid an autonomous agent when making migration decisions to continuously optimize the balance between the migration time, downtime, and energy consumption. The data center state and the resource load, including the CPU, memory, and network, are represented in the agent's state space using a two-layer graph neural network (GNN), and the asynchronous advantage actor–critic (A3C) algorithm is employed to dynamically determine whether to continue the precopy phase or switch to postcopy, optimizing the trade-off among the total migration time, downtime, and energy consumption while adhering to the service-level agreement (SLA) constraints. An adaptive host selection policy ensures that VMs are migrated only to underloaded machines, preventing overload and ensuring system stability. A simulation evaluation that employed the VM workload from the GWA-Bitbrains dataset revealed that this framework achieved a total migration time of 45.5 s, with 30.1 s spent on the precopy phase and 15.4 s on the postcopy phase, resulting in a downtime of 15.4 s. Compared with previous approaches, this result represents an improvement of 12.5% in total migration time, reducing it from 52 s to 45.5 s; 23% in downtime reduction, from 20 s to 15.4 s; and 4.4% in energy efficiency, increasing from 87% to 91.4%. The SLA compliance remained stable at 92.8%, affirming that the service quality was preserved. This study demonstrates the effectiveness of integrating GNN-based embeddings and A3C scheduling in terms of reducing downtime and energy usage while maintaining reliable service delivery in data centers.
Machine Learning to Estimate Workload and Balance Resources with Live Migration and VM Placement Taufik Hidayat, Kalamullah Ramli, Nadia Thereza, Amarudin Daulay, Rushendra Rushendra, Rahutomo Mahardiko Informatics, 2024 Currently, utilizing virtualization technology in data centers often imposes an increasing burden on the host machine (HM), leading to a decline in VM performance. To address this issue, live virtual migration (LVM) is employed to alleviate the load on the VM. This study introduces a hybrid machine learning model designed to estimate the direct migration of pre-copied migration virtual machines within the data center. The proposed model integrates Markov Decision Process (MDP), genetic algorithm (GA), and random forest (RF) algorithms to forecast the prioritized movement of virtual machines and identify the optimal host machine target. The hybrid models achieve a 99% accuracy rate with quicker training times compared to the previous studies that utilized K-nearest neighbor, decision tree classification, support vector machines, logistic regression, and neural networks. The authors recommend further exploration of a deep learning approach (DL) to address other data center performance issues. This paper outlines promising strategies for enhancing virtual machine migration in data centers. The hybrid models demonstrate high accuracy and faster training times than previous research, indicating the potential for optimizing virtual machine placement and minimizing downtime. The authors emphasize the significance of considering data center performance and propose further investigation. Moreover, it would be beneficial to delve into the practical implementation and dissemination of the proposed model in real-world data centers.
STRATEGIC EVALUATION AND ECONOMIC IMPACT OF FIBRE OPTIC INFRASTRUCTURE Nurwan Reza Fachrurrozi, Syifa Nurgaida Yutia, Yus Natali, Jose Januario dos Reis Costa, Taufik Hidayat Virtual Economics, 2024 Metrolink, LDA as the largest telecommunications company in Timor-Leste, is continuously investing to grow its company and serve the growing needs of its customers with a profit-oriented vision. This research aims to find out the viability of the investment plan that will be implemented at Metrolink, LDA. The investment plan is the construction of a project in Silawan-Batu Gade-Liquica-Dili that is to expand the network. With a total investment of USD 3,000,000 coming from equity at an interest rate of 9% in 2023. Capital budgeting is a process in which a company analyses a project and decides which projects will be included in its financial budget. The type of research that is conducted is descriptive research and this research will perform qualitative analysis to analyse the problems that occur. While the data is in the form of quantitative data. Evaluation of investment eligibility on the project This uses capital budgeting techniques which it uses to find out whether or not an investment proposal is eligible. With the payback period analysis tool, net present value. The two analytical tools are also used by Metrolink, LDA to measure whether the project is adequate or not. From the results of the analysis and the plan of the project Silawan-Batu Gade-Liquica-Dili. Obtained a payback period of 2 years 7 months from the target Metrolink, LDA is 5 years, Net Present Value of USD 3.358.050 of the target that is defined Metrolinks, the result is positive. It shows that the financial investment plan project is not worthy to be implemented.
THE ANALYSIS OF DATA PREPARATION TO VALIDATE MODEL VALUES OF INFORMATION TECHNOLOGY Taufik Hidayat, Rahutomo Mahardiko, Ali Miftakhu Rosyad Virtual Economics, 2023 Currently, there are some methods of preparing data for validating an IT value model correctly. One challenge in applying data mining to validate model values is to convert data into an appropriate form for this activity. Data mining algorithms can then be applied using the prepared data. The adequacy of data preparation often determines whether this data mining is successful or not. This study aims at creating a method for preparing the data during validation. The basic method used for data preparation is the Returns to Scale (RTS) method because it is easy to use and can be combined with further validation results. This method was applied by employing two models: two-factor and three-factor models. Both models are then compared to see the difference between them. The developed model is then tested on Branchless Banking (BB) and Downstream Petroleum (DP) industries. The results show that the method is applicable to prepare the data for validation. In addition, the results also demonstrate that both industries, DP and BB, have different result on data preparation, meaning that DP and BB have different ITs. This research contributes not only to a technique of preparing data for validating an IT value model by the RTS method but also can be a basis to work for data validation because it can give a result with the behaviour of the industry.
Forecasting of Indonesian Digital Economy based on Available New Start-up , Taufik Hidayat, Rahutomo Mahardiko, Ali Miftakhu Rosyad International Journal of Information Engineering and Electronic Business, 2023 Since the last 5 years, digital economy is growing steadily in Indonesia. Right now, the digital economy faces some potential problems and Covid-19 pandemic. This paper presents current data of the national Gross Domestic Product (GDP) and other GDPs (billion IDR) and the number of start-up, and predicts near some categories of future GDP and numbers of available new start-up for the next few years. The forecast will use Markov chain analysis. The results indicate that, while there are problems faced by the digital economy industry, the GDP and numbers of start-up are significantly increasing.
Digital Forensics for Cyberattack Detection in VM Migration: A Conceptual Framework T Hidayat, N Ibrahim Jurnal Komputer dan Elektro Sains 4 (1), 1-5 , 2026 2026
Digital Signal Feature Extraction for Graph-Based Host Classification in VM Placement T Hidayat, LM Silalahi, A Hamid JOURNAL ZETROEM 8 (1), 17-25 , 2026 2026
Strengthening Civic Awareness and Religious Moderation among Millennials Through a Participatory Approach Based on Local Wisdom AM Rosyad, T Hidayat, Z Zaenudin, A Khoiriyah, NO Adelakun Jurnal Pengabdian Masyarakat Sultan Indonesia 3 (1), 23-35 , 2026 2026 Citations: 4
Strengthening the Internalization of Pancasila Values in the Millennial Generation Through Character Education in Indramayu Regency AM Rosyad, T Hidayat, N Nurchamidah, M Baedowi, M Hamsah Jurnal Pengabdian Masyarakat Sultan Indonesia 3 (1), 1-11 , 2026 2026 Citations: 4
Digital Licensing as Institutional Reconfiguration: Urban Spatial Planning Governance Beyond Legal Certainty D Nursidi, T Hidayat, RM Syam International Research Journal of Multidisciplinary Scope (IRJMS) 7 (1), 944-953 , 2026 2026
SUBSTANTIVE JUSTICE IN COASTAL SPATIAL LICENSING: A RAWLSIAN PERSPECTIVE ON INDONESIA’S ONE SPATIAL PLANNING POLICY D Nursidi, T Hidayat, RM Syam Veredas do Direito 23 (2), e234315-e234315 , 2026 2026
Enhancing Network Security and Performance using DNS Sinkhole and QoS: A Practical ISO/IEC 27001: 2022 Implementation B Wibowo, T Hidayat, A Yuswanto, A Nurrohman International Journal of Management Science and Application 4 (2), 56-66 , 2025 2025
Cybersecurity Education Strategies Based on Open-Source Intelligence (OSINT) to Enhance Public Awareness T Hidayat, B Wibowo, A Yuswanto, AF Jannah International Journal of Science Education and Cultural Studies 4 (2), 1-9 , 2025 2025 Citations: 2
Perancangan Aplikasi Sistem Informasi di Objek Wisata Ciperna Golf Course MK Dhafin, ZS Ali, WP SJ, T Hidayat Jurnal Pengabdian Masyarakat Sultan Indonesia 2 (2), 9-16 , 2025 2025 Citations: 1
Reinforcement Learning-Driven Hybrid Precopy/Postcopy VM Migration for Energy-Efficient Data Centers T Hidayat, K Ramli, R Harwahyu, M Salman, TS Gunawan IEEE Access , 2025 2025 Citations: 3
Smart Aquaculture: Automated Fish Feeding with Blynk Alerts B Wibowo, NP Ramadhani, F Alfianza, FA Rizky, T Hidayat Jurnal Komputer dan Elektro Sains 3 (2), 10-13 , 2025 2025 Citations: 5
Design and Evaluation of an Automatic Roof Drive Prototype in Optimizing Tomato Cultivation B Wibowo, AN Tanjung, N Afandi, TD Ananpasha, T Hidayat Jurnal Komputer dan Elektro Sains 3 (2), 1-4 , 2025 2025
A Novel Hybrid Model for High-Accuracy Malware Detection in The Internet of Medical Things (IoMT) Environment. A Daulay, K Ramli, D Sudiana, R Harwahyu, T Hidayat, NR Fachrurrozi IIUM Engineering Journal 26 (3), 304-319 , 2025 2025
Novel Federated Graph Contrastive Learning for IoMT Security: Protecting Data Poisoning and Inference Attacks A Daulay, K Ramli, R Harwahyu, T Hidayat, B Pranggono Mathematics 13 (15), 2471 , 2025 2025 Citations: 2
Strategy for Precopy Live Migration and VM Placement in Data Centers Based on Hybrid Machine Learning T Hidayat, K Ramli, R Harwahyu Informatics 12 (3), 71 , 2025 2025
Strategic Scheduling of a Live Migration Virtual Machine using Machine Learning: A Review T Hidayat, PA Roozbahani International Journal of Management Science and Application 4 (1), 46-54 , 2025 2025 Citations: 3
Cyber Resilience to Digital Threats for Education Institutions 4.0 B Wibowo, A Yuswanto, T Hidayat, N Ibrahim International Journal of Management Science and Application 4 (1), 35-45 , 2025 2025 Citations: 10
Unveiling the Cybercrime Ecosystem: Impact of Ransomware-as-a-Service (RaaS) in Indonesia B Wibowo, L Hafiz, T Hidayat International Journal of Science Education and Cultural Studies 4 (1), 11-21 , 2025 2025 Citations: 5
Strategi Efektif dalam Meningkatkan Kesadaran Keamanan Siber terhadap Ancaman Phishing di Lingkungan Perusahaan PT. XYZ B Wibowo, T Hidayat Jurnal Pengabdian Masyarakat Sultan Indonesia 2 (1), 1-9 , 2025 2025 Citations: 12
Machine Learning to Estimate Workload and Balance Resources with Live Migration and VM Placement T Hidayat, K Ramli, N Thereza, A Daulay, R Rushendra, R Mahardiko Informatics 11 (3), 50 , 2024 2024 Citations: 13
MOST CITED SCHOLAR PUBLICATIONS
Analisa Prediksi Pertumbuhan Start-up Di Era Industri 4.0 Menggunakan Metode Markov Chain T Hidayat, DY Sari, Y Azzery Teknokom 3 (2), 1-7 , 2020 2020 Citations: 379
A Systematic Literature Review Method On AES Algorithm for Data Sharing Encryption On Cloud Computing T Hidayat, R Mahardiko International Journal of Artificial Intelligence Research 4 (1), 49-57 , 2020 2020 Citations: 65
Internet of Things Smart Agriculture on ZigBee: A Systematic Review T Hidayat IncomTech, Jurnal Telekomunikasi dan Komputer, vol. 8, no.1, Juni 2017 , 2017 2017 Citations: 42
Method of Systematic Literature Review for Internet of Things in ZigBee Smart Agriculture T Hidayat, R Mahardiko, ST Franky D 2020 8th International Conference on Information and Communication … , 2020 2020 Citations: 38
An Analysis of Online Transportation Applications Between Gojek and Grab for Students M Marwiyah, PP Arti, T Hidayat International Journal of Science Education and Cultural Studies 1 (1), 52-64 , 2022 2022 Citations: 31
Load Balancing Network by using Round Robin Algorithm: A Systematic Literature Review T Hidayat, Y Azzery, R Mahardiko Jurnal Online Informatika 4 (2), 85-89 , 2019 2019 Citations: 31
A Review of Detection of Pest Problem in Rice Farming by using Blockchain and IoT Technologies T Hidayat, R Mahardiko Journal Of Computer Networks, Architecture and High-Performance Computing 3 … , 2021 2021 Citations: 28
A SWOT (Strength Weakness Opportunity and Threat) Analysis as a Strategy to Enhance Competitiveness M Mardiyana, M Ihsan, A Adrial, H Parida, S Sidiq, T Hidayat International Journal of Management Science and Application 1 (1), 18-27 , 2022 2022 Citations: 25
Forecast Analysis of Research Chance on AES Algorithm to Encrypt During Data Transmission on Cloud Computing T Hidayat, ST Franky D, R Mahardiko 2020 2nd International Conference on Broadband Communications, Wireless … , 2020 2020 Citations: 24
Mobile Cellular Technology Forecast for the Indonesian Telecommunications Industry T Hidayat, R Mahardiko, M Alaydrus Journal of Telecommunications and the Digital Economy 8 (1), 37-48 , 2020 2020 Citations: 23
Encryption Security Sharing Data Cloud Computing by Using AES Algorithm: A Systematic Review T Hidayat Teknokom 2 (2), 11-16 , 2019 2019 Citations: 18
Peranan Penggunaan Internet dan Sosial Media dalam Meningkatkan Kegiatan Produktif Bagi Masyarakat JFN Wulan, T Hidayat Jurnal Pengabdian Masyarakat Sultan Indonesia 1 (2), 25-30 , 2024 2024 Citations: 17
Data Encryption Algorithm AES by Using Blockchain Technology: A Review T Hidayat, R Mahardiko BACA: Jurnal Dokumentasi dan Informasi , 2021 2021 Citations: 17
The Analysis of Data Preparation to Validate Model Values of Information Technology T Hidayat, R Mahardiko, AM Rosyad Virtual Economics 6 (2), 23-34 , 2023 2023 Citations: 15
Machine Learning to Estimate Workload and Balance Resources with Live Migration and VM Placement T Hidayat, K Ramli, N Thereza, A Daulay, R Rushendra, R Mahardiko Informatics 11 (3), 50 , 2024 2024 Citations: 13
A Model Statistical during Covid-19 Future E-Commerce Revenue for Indonesia Aviation T Hidayat, R Mahardiko, AM Rosyad International Journal of Information Engineering and Electronic Business 15 … , 2023 2023 Citations: 13
Analisa Keamanan E-commerce Menggunakan Metode AES Algoritma L Sodikin, T Hidayat Teknokom 3 (2), 8-13 , 2020 2020 Citations: 13
Strategi Efektif dalam Meningkatkan Kesadaran Keamanan Siber terhadap Ancaman Phishing di Lingkungan Perusahaan PT. XYZ B Wibowo, T Hidayat Jurnal Pengabdian Masyarakat Sultan Indonesia 2 (1), 1-9 , 2025 2025 Citations: 12
Users' Intentions and Behaviors Toward Portable Scanner Application - Do Education and Employment Background Moderates the Effect of UTAUT Main Theory IS Permana, T Hidayat, R Mahardiko 2nd International Conference on Enhanced research and Industrial Application … , 2021 2021 Citations: 12
Performance Analysis and Mitigation of Virtual Machine Server by using Naive Bayes Classification T Hidayat, M Alaydrus 2019 Fourth International Conference on Informatics and Computing (ICIC), 1-5 , 2019 2019 Citations: 12
