Garuda - Garba Rujukan Digital

Performance Analysis of Energy-Aware Multihop LEACH in Wireless Sensor Networks Muhlis Tahir; Dian Neipa Purnamasari; Evy Maya Stefany; Ifan Fauzi Firmansyah; Aristya Miftahun Nur Rizky; Aurellia Maharani Putri
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1560

Energy efficiency is a paramount challenge in Wireless Sensor Networks (WSNs), directly impacting their operational lifetime and reliability. The reliance on limited, non-rechargeable battery resources establishes energy conservation as a critical design consideration for any WSN communication protocol. Hierarchical routing protocols, such as clustering, have been widely recognized as an effective strategy to address this issue. This study presents a comprehensive comparative analysis of two foundational hierarchical routing protocols: the Low-Energy Adaptive Clustering Hierarchy (LEACH) and the Hybrid Energy-Efficient Distributed Clustering (HEED). As a pioneering protocol, LEACH utilizes a probabilistic Cluster Head (CH) rotation mechanism to balance the energy load. However, its drawback lies in neglecting critical parameters like the residual energy of nodes during CH selection. In contrast, HEED was introduced as an enhancement, employing a hybrid approach that considers residual energy as a primary parameter and intra-cluster communication cost as a secondary one to produce a more stable and energy-efficient topology. To evaluate the performance of both protocols, we conducted large-scale simulations focusing on key metrics, including network lifetime—measured by First Node Dies (FND), Half Nodes Die (HND), and Last Node Dies (LND)—total energy consumption, and data delivery performance. The results reveal a distinct trade-off between initial network stability and long-term resilience. HEED demonstrates superior performance in the initial phase, successfully delaying the first node's death (FND at round 286) by ensuring a more balanced energy load distribution. In contrast, LEACH provides a significantly longer overall network lifespan (LND at round 585) and achieves a higher data throughput, making it more resilient in the long term. The findings conclude that the choice between LEACH and HEED is application-dependent, highlighting the need to align the protocol's characteristics with specific network priorities, whether it be initial stability or maximum operational longevity

Supplier selection in modern supply chains using a hybrid Fuzzy BWM and Fuzzy CoCoSo model Mladen Boži?; Mia Poledica
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.v6i3.1857

Supplier selection represents one of the most significant decision-making problems in supply chain management, as supplier performance directly affects product quality, operational efficiency, delivery reliability, customer satisfaction, and overall organizational competitiveness. Modern supply chains are increasingly characterized by uncertainty, frequent disruptions, global competition, and growing requirements related to environmental and social responsibility. Under such conditions, the supplier selection process requires the simultaneous consideration of multiple, often conflicting criteria, including cost, quality, delivery reliability, flexibility, sustainability, technical capability, communication, reputation, and risk. Consequently, supplier selection can be regarded as a complex multi-criteria decision-making problem that requires a structured and transparent evaluation framework. This paper proposes a hybrid methodology based on the integration of the Fuzzy Best-Worst Method (BWM) and the Fuzzy Combined Compromise Solution (CoCoSo) method for selecting the most suitable supplier. The Fuzzy BWM is applied to determine the relative importance of the evaluation criteria while accounting for uncertainty and subjectivity in expert judgments. The Fuzzy CoCoSo method is subsequently used to assess, compare, and rank the available suppliers based on their overall performance. The proposed decision-making framework considers ten evaluation criteria and four supplier alternatives. The results indicate that delivery reliability and accuracy is the most important criterion in the supplier selection process, highlighting the critical role of timely and dependable deliveries in maintaining supply chain continuity. The findings also demonstrate that the proposed hybrid model enables a systematic, consistent, and reliable evaluation of suppliers under uncertain conditions. It can therefore serve as an effective decision-support tool for managers and practitioners and can be adapted for supplier selection problems across different industrial sectors

The Design of the CQR Information System: Predictive Quantitative Analytics Software for Research Nuur Wachid Abdul Majid; Muhammad Rafli; Pratama Benny Herlandy; Muhammad Nurtanto; Mohamed Nor Azhari Azman; Md Baharuddin Abdul Rahman; Khairul Azhar Mat Daud
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1541

In the current digital era, quantitative data analysis remains a critical yet demanding stage of the research process, particularly for researchers without a strong statistical background. Complex analysis procedures, inconsistent method selection, and manual data handling increase the risk of human error and can threaten the validity of research findings. This study addresses this problem by designing and developing the CQR (Calculation of Quantitative Research) Application, a web-based predictive quantitative-analytics system intended to simplify statistical data processing for researchers regardless of their statistical expertise. The system was developed using the Research and Development (RD) method combined with the Waterfall model, comprising problem identification, data collection, system design, implementation, and testing, and was built using the Laravel 11 framework with a MySQL database. System design was guided by an analysis of functional and non-functional requirements and modelled using Use Case and Activity Diagrams. The developed system was evaluated through black-box testing across four core modules-user authentication and three data-upload calculator modules for the System Usability Scale (SUS). All eleven scenarios (100%) produced the expected output. A preliminary usability evaluation of the CQR Application itself, using the System Usability Scale with 32 student respondents, yielded a mean score of 76.09 (SD = 9.67), corresponding to an “Acceptable” rating and a curved grade of B. These findings indicate that the CQR Application is functionally reliable and perceived as usable, while quantitative benchmarking of analytical accuracy and processing time against established statistical software, together with a complementary User Experience Questionnaire evaluation, are identified as the immediate next steps for strengthening the system's scientific and practical contribution. The CQR Application is expected to help researchers, particularly those without extensive statistical training, conduct quantitative analysis that is more consistent, efficient, and credible.

A Framework for Cross-Domain Integration and Validation in Software-Defined Vehicle Systems Sumaiyya Fatima
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.v6i3.1909

Modern telecommunications systems generate billions of events daily through billing, fraud detection, customer operations, network telemetry, and infrastructure management. Traditional batch-processing architectures rely on scheduled data-processing windows and are therefore inadequate for operational environments requiring continuous responsiveness, low latency, and real-time decision-making. This article examines scalable real-time streaming architectures, event-driven processing frameworks, intelligent runtime decision systems, and distributed operational models designed for large-scale telecommunications environments. It further investigates customer routing optimization, workload balancing, backpressure management, and fault-tolerant state synchronization to support national-scale telecommunications operations. A synthesis of distributed-systems literature and benchmarking studies is employed to evaluate streaming coordination models, stateful processing architectures, and migration synchronization strategies. Six analytical models are formalized to characterize throughput scaling, backpressure detection, end-to-end latency decomposition, routing assignment optimization, workload balance efficiency, and fault recovery time. The analysis indicates that parallel partition-based streaming frameworks can achieve near-linear throughput scaling when coordination overhead is effectively controlled. The proposed backpressure coefficient provides a quantitative indicator for identifying emerging capacity constraints before significant performance degradation occurs. Routing assignment and workload balancing models further enable continuous optimization under dynamic workload and service conditions. Fault-tolerant state synchronization mechanisms contribute to operational continuity by supporting consistent state recovery during component failures and migration processes. These analytical frameworks shift telecommunications streaming-system design from empirical performance tuning toward analytically grounded engineering practices. Overall, event-driven architectures, scalable streaming coordination, and intelligent runtime decision systems represent foundational capabilities for responsive customer operations, efficient resource utilization, resilient service delivery, and reliable telecommunications infrastructure at national scale

Digital-Based Instructional Media: Transforming Practical Learning for Aspiring Culinary Teachers Yeni Yulianti; Annis Kandriasari; Rina Febriana; Jarudin Wastira
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1514

The integration of digital-based instructional media into practical learning environments has the potential to transform culinary education, particularly in the preparation of future culinary teachers. This study examines the effectiveness of digital tools, including virtual reality (VR) simulations, video tutorials, and interactive learning platforms, in enhancing practical learning outcomes. Using a mixed-methods approach, the research combines quantitative data obtained through surveys with qualitative insights from interviews and focus group discussions to evaluate the effects of digital media on skill acquisition, learner engagement, and overall learning experiences. Participants consisted of aspiring culinary teachers, educators, and industry experts, providing a comprehensive perspective on the implementation and outcomes of digital instructional tools in culinary education. The findings indicate that digital-based instructional media significantly enhances practical learning by providing immersive, flexible, interactive, and accessible experiences that complement conventional instructional methods. Several factors were identified as important for successful implementation, including usability, accessibility, technological readiness, and alignment with pedagogical objectives. However, technical limitations, resource constraints, and resistance to technological change remain important challenges that may affect effective adoption. The study concludes that curriculum designers and educators should adopt systematic strategies for integrating digital technologies into culinary teacher training programs. Such strategies should ensure that technological applications support, rather than replace, essential hands-on culinary experiences. This research contributes to the growing body of knowledge on educational technology and its application in specialized vocational fields. It further highlights the transformative potential of digital media in addressing practical learning challenges while providing opportunities for future research involving emerging technologies, including artificial intelligence (AI) and augmented reality (AR), in culinary education

Engineering Analysis of Implementation and Cost of Parapet Precast with Parapet Conventional/In-situ Yoga Prasetya; Aditya Novendra Jaya; Razez Nugraha Erson; Yogi Permana; Mohamad Aripin Nurjanah; Siti Luthfiyah Permata Hadi
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1584

A parapet is a solid side barrier installed on the edge of elevated structures such as bridges, pile slabs, or BUP at toll roads/ freeways. One innovation that can be developed to overcome the problems of conventional (in-situ) methods is the use of precast parapets, which can be produced in workshops or fabrication sites. Therefore, it is necessary to analyze the structural design, implementation method, and cost aspects that will be presented in this research. This study uses precast parapet designs with 2 different dimensions, namely 2.5 m long in straight alignment and 1.5 m long in superelevation alignment, used in bridge structures or pile slabs in toll road construction. Engineering analysis of precast parapets is carried out to determine the performance of precast parapets against impact loads. Precast concrete is produced in a workshop location and then transported to the project site, and it has advantages over conventional methods. Based on engineering analysis, a precast parapet design calculation analysis has been carried out, which is expected to withstand an impact load of 21.5 tons. Assuming 100 m in terms of time, the manufacture of precast parapet is one day faster than the conventional/in situ method. In addition, less labor is required, where precast parapets require 23 people, while conventional parapets require 28 people. In terms of cost, with a length of one meter, the precast parapet is 26.41% more expensive than the conventional/in situ method. Therefore, future studies should combine life-cycle cost analysis and life-cycle assessment with full-scale lateral or impact testing in order to create a more thorough technical, economic, and environmental foundation for deciding between precast and cast-in-situ parapet systems

The Relationship Between Environmental Knowledge and Environmental Attitude on Green Consumer Behavior Cucu Cahyana; Guspri Devi Artanti; Ari Fadiati
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1581

This study aims to analyse the relationship between environmental knowledge and environmental attitude towards green consumer behaviour. The research was conducted from February to October 2024 using a survey method. The sample size in this study consisted of 86 students from the Culinary Art Education study program. In this study, there are three instruments developed and measured in the form of scales: Green Consumer Behavior (GCB), Environmental Knowledge (EK), and Environmental Attitude (EA). The calculation results were obtained regarding the level of knowledge showed a total of 79 respondents (91.9%) scored within the 75% - 100% interval, which is categorized as high. Furthermore, 7 respondents (8.1%) scored within the 56% - 74% interval, which is categorized as medium. Assessment Categories for Environmentally Conscious Consumer Behavior showed a total of 48 respondents (55.8%) scored within the 75% - 100% interval, categorizing them as having good attitudes. A further 34 respondents (39.5%) scored within the 56% - 74% interval, categorizing them as fairly good. Lastly, 4 respondents (4.7%) scored within the ? 55% interval, categorizing them as poor. The hypothesis results showed there is a significant relationship between environmental knowledge (EK), attitudes toward the environment, and environmentally conscious behavior. This is indicated by an F-value of 42.834 with a significance of ? 0.001, showing that the combination of knowledge and attitudes has a significant correlation with behavior. To further enhance awareness and commitment among students of the Culinary Arts Education Program (Pendidikan Tata Boga) at FT UNJ regarding environmental conservation, it is crucial to strengthen environmental knowledge. An inclusive and integrated educational approach is expected to help students not only gain knowledge but also actively engage in environmental preservation efforts

Real-Time Streaming and Intelligent Decision Systems for Telecommunications Infrastructure Suresh Tambe
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.v6i3.1910

Modern telecommunications systems generate billions of events daily through billing, fraud detection, customer operations, network telemetry, and infrastructure management. Traditional batch-processing architectures rely on scheduled data-processing windows and are therefore inadequate for operational environments requiring continuous responsiveness, low latency, and real-time decision-making. This article examines scalable real-time streaming architectures, event-driven processing frameworks, intelligent runtime decision systems, and distributed operational models designed for large-scale telecommunications environments. It further investigates customer routing optimization, workload balancing, backpressure management, and fault-tolerant state synchronization to support national-scale telecommunications operations. A synthesis of distributed-systems literature and benchmarking studies is employed to evaluate streaming coordination models, stateful processing architectures, and migration synchronization strategies. Six analytical models are formalized to characterize throughput scaling, backpressure detection, end-to-end latency decomposition, routing assignment optimization, workload balance efficiency, and fault recovery time. The analysis indicates that parallel partition-based streaming frameworks can achieve near-linear throughput scaling when coordination overhead is effectively controlled. The proposed backpressure coefficient provides a quantitative indicator for identifying emerging capacity constraints before significant performance degradation occurs. Routing assignment and workload balancing models further enable continuous optimization under dynamic workload and service conditions. Fault-tolerant state synchronization mechanisms contribute to operational continuity by supporting consistent state recovery during component failures and migration processes. These analytical frameworks shift telecommunications streaming-system design from empirical performance tuning toward analytically grounded engineering practices. Overall, event-driven architectures, scalable streaming coordination, and intelligent runtime decision systems represent foundational capabilities for responsive customer operations, efficient resource utilization, resilient service delivery, and reliable telecommunications infrastructure at national scale

A Web-Based Interactive Guidance Application for Guiding Vocational Students to Access the Labor Market: An Expert-Based Evaluation Imam Mahir; Soenarto Soenarto; Slamet Slamet; Thomas Köhler
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1579

Unemployment remains a global challenge, particularly among vocational school graduates who are expected to have job-ready skills yet continue to record the highest unemployment rates. This study evaluates a web-based interactive vocational guidance application designed to integrate student career portfolios, vocational guidance and counseling, industry job opportunities, and labor market access within a school based digital ecosystem. The study addresses a persistent gap in vocational education: career guidance, student records, and employment services are often managed separately, limiting the continuity between students' demonstrated competencies and current industry requirements. The application was developed through planning, development, preliminary testing, field testing, and finalization, followed by expert and user evaluation. Four experts and seven vocational practitioners reviewed the prototype, while 100 students from a public vocational high school in Jakarta participated in the operational test. A 19-item, five-point Likert questionnaire assessed usability, correctness, and portability. The analysis is descriptive, using aspect means and a predefined qualitative conversion scale. Expert ratings produced overall means of 4.83 for content and 4.74 for media, while student ratings produced an overall mean of 4.26. The application's distinctive contribution is the integration of school verified student profiles, counselor-mediated guidance, industry vacancy, internship, or training information, and direct opportunity matching in a vocational school centered workflow. The findings indicate that the prototype is feasible and positively evaluated as a digital support system for career guidance and labor-market access. Further studies are required to test matching accuracy, longitudinal employment outcomes, scalability, and effectiveness across multiple vocational schools

Human-Centered Artificial Intelligence Framework for Adaptive Inventory Decision Support under Supply Chain Uncertainty Arlis Dewi Kuraesin
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.v6i3.1905

Supply chain uncertainty has increased the complexity of inventory decision-making due to demand fluctuations, supplier disruptions, and rapidly changing operational conditions. Although artificial intelligence (AI) has demonstrated strong potential in improving supply chain analytics, existing approaches often investigate prediction, explainability, optimization, and human involvement as separate components. This study proposes a Human-Centered Artificial Intelligence (HCAI) framework for adaptive inventory decision support under supply chain uncertainty by integrating AI-based demand prediction, Explainable Artificial Intelligence (XAI), adaptive inventory optimization, and human decision support within a unified framework. The Design Science Research Methodology (DSRM) was adopted to develop and evaluate the proposed framework using empirical inventory data collected from food distributors and rice warehouses in Pidie Regency and Bireuen Regency, Aceh, Indonesia. Forecasting models, including ARIMA, Exponential Smoothing, Random Forest, LSTM, and XGBoost, were evaluated using RMSE, MAE, and MAPE, with the best-performing model subsequently integrated with SHAP-based explanations and a Mixed-Integer Programming (MIP) optimization model. The evaluation results showed that XGBoost achieved the highest forecasting performance with a MAPE of 7.40%. The proposed framework reduced total inventory cost by 22.7%, improved service level from 88.5% to 96.8%, and decreased stockout rate from 11.5% to 3.2% compared with the conventional inventory policy. Furthermore, user evaluation involving 20 inventory practitioners indicated positive acceptance of the Human Decision Support Layer, with high scores for perceived usefulness, trust, and decision confidence. These findings demonstrate that integrating predictive intelligence, explainable AI, adaptive optimization, and human oversight enable transparent and adaptive inventory decision support under uncertain supply chain conditions. This study contributes to advancing Human-Centered AI applications in supply chain management by providing an integrated framework that bridges AI capability with human operational expertise

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

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

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

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

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