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International Journal of Education, Science, Technology, and Engineering (IJESTE)
Published by Lamintang Education & Training Centre
ISSN : 26851458     EISSN : 26849844     DOI : https://doi.org/10.36079/lamintang.ijeste
Core Subject : Science, Education, Engineering,
Computer Science & IT Education Engineering
International Journal of Education, Science, Technology, and Engineering (IJESTE) is a peer-reviewed journal that aims at the publication and dissemination of original research articles on the latest developments in all fields of Education, Science, Technology and Engineering.
Arjuna Subject : -
Articles 68 Documents
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Intelligent Learning Environments for 21st-Century Competencies: Connecting Engagement, Achievement, and Teamwork in Vocational Training Chui, Len Wan; Heng, Cheng Jie; Yang, Wang Li
International Journal of Education, Science, Technology, and Engineering (IJESTE) Vol 8 No 1: June 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijeste-0801.762

Abstract

This study investigates the integration of smart classrooms and collaborative learning to enhance 21st-century competencies in Taiwan’s vocational education. Using a mixed-methods approach, the research involved surveys with 300 students and qualitative data from classroom observations and teacher interviews in both urban and rural schools. Results showed significant improvements in student engagement, academic achievement, and soft skills such as teamwork (+27%), communication (+18%), and problem-solving (+25%). Smart classrooms also helped narrow the digital divide, yet challenges remain in rural areas, including limited teacher training (60%), inadequate internet access (35%), and infrastructure constraints (75%). Despite these issues, institutional readiness was relatively high—73% for training and 82% for infrastructure. The findings suggest that while smart classrooms positively impact learning outcomes, long-term success requires equitable access, sustained infrastructure support, and targeted professional development. Future research should explore their long-term effects on employability and the potential of adaptive learning technologies for personalized vocational training.
An Efficiency of Auxiliary Power Consumption in Substations Using Renewable Energy Potentials in East Kalimantan Province Silalahi, Chandra Lima; Setiawan, Eko Adhi
International Journal of Education, Science, Technology, and Engineering (IJESTE) Vol 8 No 1: June 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijeste-0801.840

Abstract

PT PLN (Persero) as the Indonesian state electricity supply company must carry out an energy transition in terms of generation, transmission and distribution. Substation in Indonesia’s electric power transmission sector have not yet incorporated the idea of green energy into their operation, despite the fact that there is a great potential for renewable energy in substations, such as the usage of solar and wind energy. The primary power source for the auxiliary substation is energy from the grid, with diesel power serving as a backup. However, the energy mix in the Kalimantan grid is still dominated by energy from coal (based on the Annual Operation Evaluation Report PLN UIP3B 2023). This research investigates the technical and economic viability of utilizing renewable energy in auxiliary substations to mitigate the company’s escalating financial burden. Technically, the study aims to determine the optimal PV panel capacity required for auxiliary power needs. Economically, it evaluates the total investment and operational costs, Internal Rate of Return (IRR), Return on Investment (ROI), payback periods, and Levelized Cost of Energy (LCOE). Unique to this study is the direct measurement of kWh consumption data from 23 substations in East and North Kalimantan, a method not previously employed. The findings provide recommendations on the appropriate PV capacity for installation in substations and present economic analysis.
Automated Defense Against Application-Layer Attacks on Windows Systems Using Wazuh and Shuffle Thakker, Aastha; More, Aditya; Kumar, Kapil
International Journal of Education, Science, Technology, and Engineering (IJESTE) Vol 8 No 1: June 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijeste-0801.842

Abstract

Application-layer attacks targeting Windows systems remain a significant threat due to their ability to bypass traditional perimeter defenses. These attacks often exploit vulnerabilities listed in the OWASP Top 10 for desktop applications, demanding proactive defense mechanisms. This paper proposes a unified approach that combines SIEM and SOAR capabilities to detect and respond to Windows-based application-layer threats with increased efficiency and automation. The framework integrates the open-source SIEM platform Wazuh with the SOAR engine Shuffle to automate threat detection and incident response. A layered defense strategy is implemented, involving log correlation, rule-based policy enforcement, and playbook-driven response automation. The integration reduces manual triage overhead and enhances response time compared to traditional SOC patterns. This framework demonstrates a scalable, open-source-based solution for defending Windows environments at the application layer. It sets the groundwork for future integration of AI-driven analytics, multi-OS support, and tamper-proof event lo event logging using blockchain technologies.
Anomaly Detection Using Autoencoders for Household Electricity Meters Wongsuwan, Nattaporn; Srisawat, Somchai; Kittisak, Thanakorn; Boonmee, Anongrat; Sanna, Mirella
International Journal of Education, Science, Technology, and Engineering (IJESTE) Vol 8 No 2: December 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijeste-0802.777

Abstract

Household electricity consumption often exhibits sudden and unexplained spikes that typically go unnoticed until the monthly bill arrives. These anomalies may stem from equipment malfunction, inefficient appliance usage, or irregular electrical patterns that households cannot easily observe. This study proposes an unsupervised anomaly detection framework based on autoencoders to identify abnormal consumption behavior from high resolution household electricity meter data. The model learns normal consumption patterns through reconstruction and flags anomalies using a dynamic threshold derived from reconstruction error distribution. Experimental results demonstrate strong detection capability, particularly for sudden spikes, achieving a precision of 0.92, recall of 0.88, and F1 score of 0.90. The findings highlight the potential of deep learning–based unsupervised methods to support real time, edge deployable solutions for energy efficiency and early fault detection in residential environments.
Enhanced Techniques for Detecting Promiscuous Mode using Packet Fu and the Metasploit Framework Pandya, Partho; Joshi, Kashyap; Kumar, Kapil
International Journal of Education, Science, Technology, and Engineering (IJESTE) Vol 8 No 2: December 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijeste-0802.880

Abstract

This article argues that Thailand’s public-sector digitalisation has so far failed to realise the principles of Digital Era Governance (DEG) because it remains institutionally and politically anchored in New Public Management (NPM) logic. Rather than enabling platform-based integration and citizen-centric services, digital initiatives have often reproduced audit-centric, siloed practices that prioritise measurable outputs and compliance. Using a policy-analytic approach, document review of national strategies and agency plans, and synthesis of recent literature and sectoral case examples; the article identifies three mechanisms by which NPM logic is perpetuated in Thailand’s digital transition: (1) proliferation of discrete applications driven by performance reporting and agency visibility; (2) digital tools as instruments of control and compliance rather than coordination; and (3) governance fragmentation and weak interoperability governance. The paper concludes with targeted policy recommendations to reorient Thailand’s digitalisation toward DEG: consolidate digital architecture around shared platforms and standards, redesign performance regimes to reward integration and outcomes, and strengthen cross-agency data governance.
An Exploratory Data Analysis Approach for Tax Revenue Systems Isijola, Ayomitope; Adesioye, Eriitunu; Egwu, Mirabel; Asefon, Michael; Ojo, Abiola; Okafor, Chikwado; Adekoya, Azizat; Okoh, Samuel
International Journal of Education, Science, Technology, and Engineering (IJESTE) Vol 8 No 2: December 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijeste-0802.887

Abstract

Tax collation is an important part of any company's revenue system. Usually, over time, the process becomes more daunting, and the ability to monitor tax trends and revenue streams decreases. Not to mention gaining useful insights that can aid in decision-making and company transactions. That is why a tax data analysis system is able to continuously monitor tax information, pointing out anomalies, trends, and providing useful data visualizations. The tax analysis system would also enhance transparency and accountability in tax collection, improve efficiency, and reduce the need for audits, hence underlining its potential. Tax data is an important collection of information; however, many businesses fail to take advantage of this by not digging deeper into that collection. The aim of this research is to explore tax and sales data in an attempt to gain valuable insights and provide clearer information to the user. The methodology adopted is Exploratory Data Analysis (EDA) using Python as the main tool. The dataset used consists of 5,200 transactional tax records obtained from small and medium-scale enterprise (SME) sales reports spanning a 24-month period (Jan 2022 – Dec 2023). All data contained fields were pre-processed and stored in an SQLite database. Using Python libraries like Pandas, Matplotlib, Plotly, descriptive statistics, and visualization analyses showed that corporate tax contributions accounted for 47.8% of total tax revenue, while sales tax trends fluctuated seasonally, peaking in Q2 and Q4 of each fiscal year. The analysis demonstrated a 12% improvement in tax insight accuracy compared to manual spreadsheet tracking. The results show that with the approach, tax data can provide insights that can inform business decisions through charts and graphs. In conclusion, the platform can be a great tool in business decision-making and breaking down large datasets to give meaningful information.
The Persistence of New Public Management Logic in the Digital Government Transition Kittisak, Anucha Vanchai; Chalidabhongse, Kanokwan; Chanin, Chaiyasut; Jirasak, Woraphon; Thanaporn, Sirilak
International Journal of Education, Science, Technology, and Engineering (IJESTE) Vol 8 No 2: December 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijeste-0802.957

Abstract

This article argues that Thailand’s public-sector digitalisation has so far failed to realise the principles of Digital Era Governance (DEG) because it remains institutionally and politically anchored in New Public Management (NPM) logic. Rather than enabling platform-based integration and citizen-centric services, digital initiatives have often reproduced audit-centric, siloed practices that prioritise measurable outputs and compliance. Using a policy-analytic approach, document review of national strategies and agency plans, and synthesis of recent literature and sectoral case examples. the article identifies three mechanisms by which NPM logic is perpetuated in Thailand’s digital transition: (1) proliferation of discrete applications driven by performance reporting and agency visibility; (2) digital tools as instruments of control and compliance rather than coordination; and (3) governance fragmentation and weak interoperability governance. The paper concludes with targeted policy recommendations to reorient Thailand’s digitalisation toward DEG: consolidate digital architecture around shared platforms and standards, redesign performance regimes to reward integration and outcomes, and strengthen cross-agency data governance.
IoT-Based Monitoring System for Smart Agriculture to Enhance Crop Yield Efficiency Pham Van Anh; Tuan, Tran Minh; Thang, Nguyen Duc; Tuan, Quang Anh; Fujimura, Kentaro
International Journal of Education, Science, Technology, and Engineering (IJESTE) Vol 8 No 2: December 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijeste-0802.960

Abstract

This study investigates an IoT architecture for smart agriculture that combines event-triggered sensing with edge-level multi-sensor fusion to reduce energy consumption across distributed sensor networks. While prior research has largely focused on optimizing individual node efficiency, our findings reveal that the primary source of energy savings arises from systemic behavioral changes within the network’s communication ecology. Real-world experiments on a multi-node deployment show that edge fusion reduces redundant transmissions, stabilizes medium-access contention, and significantly extends sleep intervals. Collectively, these effects produce an average 30% reduction in wake-up frequency, even in relay nodes that do not perform fusion. The results indicate that the underlying mechanism is ecological rather than local: by lowering network-wide communication turbulence, the system achieves a more stable, low-activity state, allowing overlapping dormancy clusters to form naturally. This challenges the long-standing assumption that energy efficiency must be pursued primarily at the node level. Limitations include the controlled experimental environment, moderate network scale, and potential latency risks in time-critical scenarios. The study’s theoretical contribution lies in reframing energy-efficient IoT design as a complex adaptive systems problem, where efficiency emerges from interactions across the network rather than isolated node behavior. This ecosystem-centric perspective opens new directions for sustainable IoT architectures. Future research should focus on designing protocols, topology strategies, and fusion mechanisms that deliberately shape systemic behavior in IoT networks, aiming to achieve greater efficiency, resilience, and longevity than node-centric approaches alone.

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