cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Dahlan Abdullah
Contact Email
dahlan@unimal.ac.id
Phone
+62811672332
Journal Mail Official
ijestyjournal@gmail.com
Editorial Address
Jl. Tgk. Chik Ditiro, Lancang Garam, Lhokseumawe, Aceh - Indonesia, 24351
Location
Kota lhokseumawe,
Aceh
INDONESIA
International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
ISSN : -     EISSN : 27752674     DOI : -
Core Subject : Science, Education, Social, Engineering,
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation
The journal covers all aspects of applied engineering, applied Science and information technology, that is: Engineering: Energy Mechanical Engineering Computing and Artificial Intelligence Applied Biosciences and Bioengineering Environmental and Sustainable Science and Technology Quantum Science and Technology Applied Physics Earth Sciences and Geography Civil Engineering Electrical, Electronics and Communications Engineering Robotics and Automation Marine Engineering Aerospace Science and Engineering Architecture Chemical & Process Structural, Geological & Mining Engineering Industrial Mechanical & Materials Science: Bioscience & Biotechnology Chemistry Food Technology Applied Biosciences and Bioengineering Environmental Health Science Mathematics Statistics Applied Physics Biology Pharmaceutical Science Information Technology: Artificial Intelligence Computer Science Computer Network Data Mining Web Language Programming E-Learning & Multimedia Information System Internet & Mobile Computing Database Data Warehouse Big Data Machine Learning Operating System Algorithm Computer Architecture Computer Security Embedded system Coud Computing Internet of Thing Robotics Computer Hardware Information System Geographical Information System Virtual Reality, Augmented Reality Multimedia Computer Vision Computer Graphics Pattern & Speech Recognition Image processing ICT interaction with society, ICT application in social science, ICT as a social research tool, ICT in education
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 40 Documents
 1   2   3   4 
Search results for , issue "vol 6, no 3 (2026)" : 40 Documents clear
Transforming Real-Time Payment Infrastructure with Cloud-Native Architecture Priyatham Nagaiya Seenu Naidu
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1823

Abstract

The global financial services sector is undergoing a profound transformation driven by the rapid adoption of digital payment technologies, increasing customer expectations for real-time transactions, and the growing demand for scalable and secure financial infrastructures. Traditional monolithic payment systems often struggle to meet these requirements due to limitations in scalability, operational flexibility, fault tolerance, and integration capabilities. Although cloud-native technologies have emerged as a promising solution for modern payment ecosystems, the scholarly literature has not adequately examined how cloud-native architectural patterns address enterprise integration, performance scalability, resilience, and regulatory compliance challenges in large-scale payment environments. This article presents a systematic analysis of cloud-native payment infrastructure by synthesizing architectural patterns, performance benchmarks, and industry case studies from both developed and emerging digital payment ecosystems. The findings demonstrate that event-driven cloud-native architectures can deliver substantial performance improvements, including a 94% reduction in transaction processing latency from 2,400 milliseconds to 128 milliseconds, near real-time fraud detection with response times of approximately 0.05 seconds, and fraud identification performance achieving 99% recall and 83% precision through hybrid deep learning models. Furthermore, production-scale validation is evidenced by successful implementations such as India’s Unified Payments Interface (UPI), which processes over 10 billion transactions monthly, and Brazil’s PIX platform, capable of handling up to 200,000 transactions per second. Security and compliance analyses reveal that immutable event-sourcing mechanisms, combined with comprehensive audit trails and encryption strategies, facilitate adherence to major regulatory frameworks, including PCI-DSS, GDPR, and anti-money laundering requirements. However, significant implementation challenges remain, particularly concerning legacy system integration, cross-jurisdictional regulatory fragmentation, organizational transformation, and shortages of cloud-native engineering expertise. Based on these findings, practical implementation guidelines emphasize microservice-based architectures, end-to-end event-driven processing, and zero-trust security principles. Future research directions include post-quantum cryptographic migration strategies, central bank digital currency interoperability standards, sustainable payment infrastructure design, and the application of artificial intelligence for autonomous financial operations and risk management.
Systematic Literature Review on AI-Enhanced Dual-Axis Solar Tracking Systems: Techniques and Performance Analysis Muhammad Basyir; Yuwaldi Away; Tarmizi Tarmizi; Ira Devi Sara
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1553

Abstract

Recent advancements in artificial intelligence have significantly improved the performance of intelligent dual-axis solar-tracking systems, enabling more efficient photovoltaic (PV) energy harvesting under variable irradiance conditions, transient cloud cover, and mechanical uncertainties. This systematic literature review synthesizes 25 peer-reviewed studies (2018–2024) identified from Scopus and Web of Science using PRISMA 2020 procedures. We examine controller families (fuzzy logic, adaptive neuro-fuzzy interface system, deep reinforcement learning, and hybrid designs), sensing and actuation stacks (ephemeris, light sensors, inertial measurement, and computer-vision-based pose), and reported outcomes for tracking accuracy and energy gain. Across comparable conditions, AI-enabled controllers consistently reduce tracking error by ~10–35% and increase annual energy capture by ~8–25% relative to fixed-tilt or conventional rule-based/PID baselines, with the largest gains observed under partial shading and rapidly varying sky conditions. Validation is dominated by simulation, while prototype and hybrid (simulation plus field) evaluations—though fewer—provide stronger evidence of real-world robustness. Persistent challenges include computational cost on embedded hardware, sample-efficient learning and safety for field deployment, inconsistent reporting of metrics, and limited long-horizon testing. To address these gaps, we recommend (i) standardized benchmarking that reports tracking error, normalized energy yield, control latency and controller power, (ii) low-cost edge-AI implementations (model compression, quantization, and microcontroller-class deployment), and (iii) multi-season field trials with reproducible protocols across climates. The findings indicate a clear shift from static, hand-tuned control toward intelligent, adaptive methods. Hybrid designs emerge as a practical choice for deployment due to their combined stability and adaptability, whereas deep reinforcement learning shows state-of-the-art performance primarily in simulation and calls for careful simulation-to-real transfer. Overall, this review clarifies the evidence base and outlines priorities for fault-tolerant, low-cost, and real-time adaptive dual-axis solar tracking.
Integration of Agricultural Automation, and Sustainable Tourism Businesses to Strengthen the Green Economy, and Achieve Food Self-Sufficiency in Wonosalam Village Agus Sukoco; Achmad Muchayan; Fetty Asmaniati; Nuryadi Nuryadi; Muhammad Ikhsan Setiawan; Che Zalina Zulkifli; Fazilat Kodirova
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1826

Abstract

The transition toward a green economy requires rural communities to adopt innovative strategies that integrate sustainable agriculture, technological advancement, and tourism-based economic development. Wonosalam Village in Jombang Regency, Indonesia, possesses considerable potential to support this transformation due to its fertile agroforestry landscapes, abundant biodiversity, agricultural productivity, and rapidly growing eco-tourism sector. This study examines the synergistic integration of agricultural automation and sustainable tourism as a strategic approach to improving food self-sufficiency, increasing rural income, and strengthening the local green economy. The research employs a mixed-methods approach, combining field surveys, participatory rural appraisal, direct observation, and stakeholder interviews involving farmers, tourism operators, local government officials, and community leaders. In addition, pilot implementations of small-scale automation technologies were conducted in horticultural and livestock production systems to assess their operational effectiveness and socio-economic impact. The findings indicate that the adoption of smart farming technologies, including automated irrigation systems, precision nutrient delivery, environmental monitoring sensors, and solar-powered post-harvest processing facilities, significantly improves resource efficiency, productivity, and product quality while reducing production costs and environmental pressures. Furthermore, the integration of agricultural activities with tourism experiences creates diversified income opportunities through farm-based recreation, agrotourism attractions, local product marketing, and educational programs focused on sustainable farming practices. The model also supports the principles of a circular economy by utilizing agricultural by-products as resources for tourism activities, organic fertilizer production, and environmental conservation initiatives. Community participation and stakeholder collaboration were identified as key factors for successful implementation. Overall, the study demonstrates that the integration of agricultural automation and sustainable tourism can enhance rural economic resilience, promote environmental sustainability, and contribute to the development of secure and sustainable food systems. The proposed model offers a practical and replicable framework for other rural regions seeking to achieve green economic growth and long-term community prosperity
Token-Aware API Design Patterns for Model Context Protocol Integration in Enterprise Distributed Systems Nikhil Bharadwaj Ramashasthri
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1854

Abstract

Autonomous software agents operating through the Model Context Protocol (MCP) reveal a fundamental architectural mismatch between conventional REST API design and the finite context windows of Large Language Model (LLM) inference engines. Enterprise backend services originally optimized for browser-based applications typically return payloads enriched with deeply nested relational structures, verbose infrastructure metadata, and redundant serialization artifacts. While acceptable for human-operated interfaces, these responses unnecessarily consume LLM context capacity when delivered through MCP servers, increasing inference costs, reducing reasoning efficiency, and limiting the number of actionable interactions that autonomous agents can perform. This article investigates serialization-boundary optimization as a critical architectural concern for MCP-native systems and proposes four composable backend design patterns: Semantic Envelope, infrastructure metadata pruning, dynamic token-aware pagination, and GraphQL interface projections. Together, these patterns restructure API responses to maximize semantic density while minimizing token consumption without modifying underlying domain models or persistence layers. The implementation is demonstrated in enterprise environments built on Spring Boot and Hibernate, illustrating seamless integration with existing software architectures. Experimental evaluation using production entity structures from a peer-to-peer car-sharing marketplace processing millions of vehicle transactions annually shows token reductions ranging from 34% to 86% across the proposed patterns, a 40% decrease in API pagination cycles, and a 97% reduction in response latency through a two-tier semantic caching strategy deployed over an 11.5-million-row persistence layer sustaining approximately 48,900 read operations per minute. These findings demonstrate that context-aware serialization significantly improves LLM agent efficiency while preserving enterprise scalability, interoperability, and maintainability. The proposed framework provides a practical engineering vocabulary and reference architecture for designing token-efficient, MCP-native backend systems capable of supporting the next generation of autonomous AI agents in large-scale enterprise environments.
Structural Equation Modeling Analysis of Critical, Creative, and Metacognitive Thinking Skills as Predictors of Reflective Thinking in Science Problem Solving Diki Rukmana; Andi Suhandi; Achmad Samsudin
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1007

Abstract

This study aims to model the relationships between critical, creative, and metacognitive thinking skills in shaping reflective thinking abilities among prospective science teachers, particularly within the context of solving complex science problems. A quantitative research approach was employed using Structural Equation Modeling (SEM), with data analysis conducted through SmartPLS software. The sample consisted of 162 students enrolled in science education courses during the odd semester of the 2024/2025 academic year. Data were collected using a standardized questionnaire that had undergone systematic content validity and reliability testing to ensure measurement accuracy and consistency. The measurement model analysis demonstrated that all indicators satisfied established validity and reliability criteria, with factor loading values exceeding 0.70 and Average Variance Extracted (AVE) values above 0.50. Furthermore, the structural model analysis revealed that critical thinking skills exerted the strongest positive and significant influence on reflective thinking abilities (? = 0.401; p 0.001), followed by metacognitive skills (? = 0.337; p 0.001) and creative thinking skills (? = 0.178; p 0.05). The coefficient of determination (R² = 0.716) indicates that the proposed model explains 71.6% of the variance in reflective thinking ability, demonstrating substantial explanatory power. These findings confirm that reflective thinking develops through the synergistic interaction of multiple higher-order thinking competencies rather than through isolated cognitive processes. Critical thinking supports systematic evaluation of evidence and reasoning, metacognitive skills facilitate awareness and regulation of cognitive strategies, while creative thinking encourages flexible exploration of alternative solutions. This study contributes to the advancement of theoretical frameworks in science teacher education and provides practical implications for designing instructional strategies, problem-based learning activities, reflective assessments, and teacher preparation programs that systematically integrate critical, creative, and metacognitive competencies to strengthen prospective science teachers’ reflective thinking and professional decision-making capabilities
Integrated Smart Agriculture, and Sustainable Tourism Model for Advancing Green Economy And Food Self-Sufficiency In Wonosalam Village Nuryadi Nuryadi; Ony Thoyib Hadiwijaya; Koesriwulandari Koesriwulandari; Muhammad Ikhsan Setiawan; Che Zalina Zulkifli; Zulfiya Khabirova
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1855

Abstract

This study explores the integration of ecotourism and smart agriculture as a strategic approach to strengthening the green economy and achieving local food sovereignty in Wonosalam Village, Jombang Regency, Indonesia. The research develops and validates a solar-powered smart durian farming system that integrates renewable energy, precision irrigation, organic fertilization, environmental sensing, Internet of Things (IoT) technology, and artificial intelligence (AI)-based decision support. The proposed system continuously monitors key environmental parameters, including soil moisture, temperature, humidity, solar radiation, and nutrient conditions, enabling real-time recommendations for irrigation, fertilization, and crop management. A mixed-methods approach combining field observations, stakeholder engagement, system validation, and performance evaluation was employed to assess the technological, environmental, and socio-economic impacts of the integrated model. The findings demonstrate that the implementation of intelligent farming technologies significantly improves durian productivity, optimizes water and energy utilization, reduces dependence on chemical fertilizers through organic nutrient management, and enhances farm operational efficiency. Simultaneously, the integration of interactive ecotourism activities, environmental education, and digital monitoring platforms creates additional income opportunities for local communities while increasing public awareness of sustainable agriculture and biodiversity conservation. The proposed model establishes a circular rural economy by connecting agricultural production, renewable energy utilization, environmental stewardship, tourism services, and local entrepreneurship within a single integrated ecosystem. Furthermore, the system strengthens community participation, promotes technology adoption among farmers, and enhances resilience against climate variability. The study concludes that combining smart agriculture with ecotourism provides a scalable and replicable rural development framework capable of supporting national green economy strategies, decentralized food sovereignty initiatives, sustainable tourism development, and long-term environmental sustainability in agricultural regions
Revolutionizing SQL Server Administration with dbatools: The Power of Automation in Modern DBA Practices Siva Kumar Raju Bhupathiraju
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1862

Abstract

The systematic nature of database administration in a large-scale SQL Server environment, along with the need for repeatability and accuracy, makes manual operational processes impractical. dbatools, an open-source PowerShell module for SQL Server administration, was born. It encodes best practices for managing all aspects of a SQL Server deployment and administration into a suite of composable, scriptable commands. In function, dbatools exposes many common or requested management processes through the SQL Server Management Objects layer, allowing instance migrations, backup and restores, Always On Availability Groups, security policy compliance, and performance audit checks to be completed without the use of fragile GUI interfaces or one-off scripting. The dbatools cmdlets are fully compatible with PowerShell pipeline composition, common parameters, and structured exception handling, and therefore compatible with enterprise automation solutions such as CI/CD and configuration management. Backup integrity checks that are typically deferred to manual procedures due to their resource-intensive nature are implemented as a schedulable, repeatable automated restore test command that tests recoverability against test instances. Security administration is improved by commands to audit permissions, enforce encryption policy, and generate reproducibly accurate compliance reports. With declarative commands and built-in parameter validation, increasingly common high-availability production configurations that previously required a series of manual commands that risked sequencing errors can now be created with a single command. This positions dbatools as a building block of modern SQL Server deployments, where the DBA evolves from reactive manual execution of admin tasks to policy-defined infrastructure governance as scale and deployment topology and complexity grow with the organization
Improving Sentiment Classification of Indonesian Skincare Reviews through Fine-Tuned IndoBERT and Data Augmentation Nadia Thahira; Ar Razi
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1835

Abstract

This study analyzes sentiment in customer reviews of local skincare serum products on the Tokopedia e-commerce platform using a fine-tuned IndoBERT model enhanced with data augmentation techniques. A total of 5,000 reviews were collected from ten local skincare brands through web scraping and labeled according to star ratings into three sentiment classes: Positive, Neutral, and Negative. The dataset exhibited extreme class imbalance, with the Positive class representing 94.66% of all observations, creating substantial challenges for minority-class recognition. The data were divided through stratified sampling into 70% training, 15% validation, and 15% test sets to preserve class distributions. To mitigate imbalance, back-translation from Indonesian to English and back to Indonesian, together with synonym replacement, was applied exclusively to minority classes within the training set. The IndoBERT-base-p1 model was subsequently fine-tuned using focal loss combined with class weighting and compared against a baseline model trained without augmentation. Experimental results show that the proposed model achieved 94.40% accuracy, a Macro F1-score of 61.43%, and a Weighted F1-score of 95.14%. Although the baseline model obtained higher overall accuracy of 97.47%, it completely failed to identify the Neutral class, producing an F1-score of 0.00%. In contrast, the proposed approach increased the Neutral F1-score to 23.53% and improved the Macro F1-score by 2.30 percentage points, demonstrating more balanced performance across sentiment classes. The resulting model was deployed as SerumSense, a web-based application developed using Streamlit and SQLite, supporting both single-review and batch sentiment analysis. Black-box testing across 20 functional scenarios confirmed that all application features operated successfully as intended. These findings demonstrate that combining IndoBERT fine-tuning, targeted data augmentation, focal loss, and class weighting offers a practical approach for improving minority-class recognition in highly imbalanced Indonesian e-commerce review datasets.
A Deterministic Framework for Incremental System Isolation: Securing Financial Integrity and Public Safety in Distributed Platform Architectures Sowjanya Puligadda
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1856

Abstract

As digital platforms continue to expand in scale, complexity, and regulatory oversight, maintaining strict separation between experimental development environments and live production systems has become both a technical requirement and a governance imperative. In highly regulated sectors, including financial services, transportation, healthcare, and critical national infrastructure, inadequate isolation between non-production and production environments can lead to regulatory violations, financial losses, security breaches, and service disruptions affecting end users. This paper proposes the Multi-Tiered Incremental Isolation Framework (MTIIF), a deterministic architectural model designed to establish verifiable physical and logical segregation between development, testing, staging, and production environments. The framework integrates three complementary architectural mechanisms: cryptographic tenancy propagation embedded within distributed tracing headers to ensure end-to-end environment identity, physical segregation of storage and event-stream infrastructures for high-risk services, and autonomous regulatory guardrails implemented through CI/CD-integrated linting, policy validation, and deployment verification tools. Unlike conventional isolation strategies that rely primarily on operational procedures, the proposed incremental adoption model enables organizations to prioritize protection of high-risk service domains while progressively extending isolation across the entire service mesh with minimal operational disruption. Evidence from industrial deployments demonstrates that the framework reduces testing-related production incidents to statistically negligible levels, eliminates cross-environment data contamination responsible for financial integrity failures, and provides cryptographically verifiable audit trails supporting compliance with international regulatory standards. Furthermore, the framework improves operational resilience, deployment confidence, and software governance by transforming environmental isolation into a mathematically enforceable architectural invariant rather than a procedural practice dependent on human discipline. These findings establish MTIIF as a scalable reference architecture for secure, compliant, and resilient cloud-native enterprise platforms supporting continuous software delivery
Industry-Specific Applications of Digital Technologies: Frameworks, Sector Adaptations, and Operational Implications Across Contemporary Industrial Domains Kishore Kolipaka
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1864

Abstract

Industry-specific applications have transformed general information technology capabilities into specialized digital solutions tailored to the operational, regulatory, and strategic requirements of individual industries. As organizations accelerate digital transformation, technology strategies have evolved from conventional business–IT alignment toward an integrated digital business strategy in which domain-specific adaptation is considered a fundamental design principle. This article examines how industry-oriented digital technologies create organizational value by addressing sector-specific workflow architectures, compliance requirements, and risk profiles. Using a comprehensive literature review, the study analyzes the implementation of cyber-physical systems in manufacturing, clinical decision support systems in healthcare, distributed ledger technology in financial services, smart grid technologies in energy management, and continuous delivery systems in information technology. The findings demonstrate that industry-specific technological configurations significantly improve operational efficiency, decision quality, regulatory compliance, organizational resilience, and competitive advantage when aligned with the unique characteristics of each sector. Furthermore, successful implementation depends not only on technological capabilities but also on organizational factors, including evidence-based decision-making, knowledge management, interdisciplinary collaboration, and continuous organizational learning. These capabilities enable organizations to effectively integrate digital innovation into business processes while adapting to evolving regulatory and market environments. The review also identifies persistent challenges related to interoperability, governance, cybersecurity, workforce readiness, and technology standardization that continue to hinder large-scale implementation across industries. Despite substantial progress, research gaps remain in developing design frameworks and implementation methodologies that systematically integrate technological innovation with industry-specific operational contexts. This study concludes that future research should focus on adaptive, scalable, and sector-oriented design approaches capable of addressing the diverse requirements of contemporary industries. Such frameworks will support sustainable digital transformation while enhancing long-term organizational performance, innovation capacity, and strategic competitiveness across multiple industrial domains

Page 1 of 4 | Total Record : 40

 1   2   3   4 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol 6, No 3 (2026) Vol 6, No 2 (2026) Vol 6, No 1 (2026) Vol 5, No 4 (2025) Vol 5, No 3 (2025) Vol 5, No 2 (2025) Vol 5, No 1 (2025) Vol 4, No 4 (2024) Vol 4, No 3 (2024) Vol 4, No 2 (2024) Vol 4, No 1 (2024) Vol 3, No 4 (2023) Vol 3, No 3 (2023) Vol 3, No 2 (2023) Vol 3, No 1 (2023) Vol 2, No 4 (2022) Vol 2, No 3 (2022) Vol 2, No 2 (2022) Vol 2, No 1 (2022) Vol 1, No 4 (2021) Vol 1, No 3 (2021) Vol 1, No 2 (2021) Vol 1, No 1 (2021) More Issue