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International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
ISSN : -     EISSN : 27752674     DOI : -
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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 775 Documents
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Predicting Timely Graduation for Evidence-Based Higher Education Management: An Explainable Machine Learning Approach Sunda Ariana; Devi Udariansyah; Bosya Perdana; Rosmiani Aprian
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.1977

Abstract

Higher education institutions increasingly use predictive analytics to identify students who may require academic support, yet predictive accuracy alone does not determine how model outputs should inform responsible management action. This study evaluates an explainable machine-learning framework for timely graduation decision support using routine academic records through Semester 4 and proposes a Role-Differentiated Action Translation Layer (R-ATL). Historical academic records were reconstructed into student-level trajectories, with timely graduation defined as completion by the end of the eighth semester. The analytical sample comprised 1,711 students from the 2019–2021 cohorts. Models were developed using the 2019–2020 cohorts and temporally tested on the untouched 2021 cohort. Random Forest achieved a ROC-AUC of 0.755, balanced accuracy of 0.670, and Brier score of 0.190, while XGBoost achieved a ROC-AUC of 0.761, with the small difference not statistically resolved. TreeSHAP identified a mixed performance–progression profile dominated by weighted IPS, Semester-4 IPS, early credit accumulation, and Semester-3 IPS. Informative missingness in the 2024 cohort limited complete-trajectory scoring to 438 of 604 students; these results were therefore treated as a current-cohort scoring demonstration rather than external validation. R-ATL translates explanation patterns into proposed role-specific information for students, academic advisors, program managers, and institutional leaders while retaining human review. The framework received complete content validity agreement from six experts. External validation, end-user usability and actionability testing, and intervention-outcome evaluation remain necessary for further empirical assessment and confirmation of the framework’s practical effectiveness across diverse higher education contexts and student populations
Operational Resilience Engineering for Security-Critical Data Platforms: Ensuring Continuous Protection Through Graceful Degradation and Failure Isolation Rahul Kizhakkepachilamakol
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.2043

Abstract

 Security-critical data platforms must maintain defensive visibility despite infrastructure failures, traffic surges, and adversarial pressure. Unlike conventional distributed systems, degradation of these platforms can create monitoring gaps where security threats and compliance violations go undetected. This article aims to develop a structured framework for designing operational resilience specifically for security-critical data platforms. The approach integrates principles of resilience engineering, chaos engineering, and site reliability engineering into a conceptual preservation model organized around four concerns: failure isolation through bulkhead and circuit-breaker boundaries; degradation hierarchies that classify platform functions according to security consequence; priority-aware load shedding that preserves high-consequence findings under resource constraints; and graduated recovery that reconciles findings generated during outages without causing secondary failures. The framework is analytically evaluated against established resilience and security principles for distributed systems, cloud environments, microservice observability, zero-trust architectures, and automated security compliance. The analysis produces a structured mapping between platform capabilities, security-oriented degradation tiers, and corresponding architectural controls. A complementary chaos-engineering validation catalog specifies failure scenarios to evaluate isolation, degradation, load shedding, and recovery behavior under controlled operational stress, including component failures, workload saturation, communication disruptions, and recovery transitions that can affect the continuity and integrity of security monitoring. The analytical results indicate that the proposed preservation model also clarifies operational dependencies between security visibility, resource availability, and recovery sequencing. The results indicate that resilience in security-critical data platforms requires explicitly incorporating security consequences into degradation and recovery decisions, rather than relying solely on conventional high-availability mechanisms. The resulting framework provides a systematic basis for preserving critical defensive visibility during operational disruption and for assessing whether security functions remain available at levels appropriate
CONTESSA: A Continuous Testing and Security Assurance Framework for Enterprise Microservices in DevSecOps Pipelines Pratik Dinkar Rane
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.v6i1.2030

Abstract

Enterprise microservice architectures pose simultaneous quality-assurance challenges across delivery velocity, functional contract integrity, security, resilience, and regulatory compliance. Existing DevSecOps practices commonly address these concerns through separate pipeline stages, which can fragment findings and limit the traceability, consistency, and completeness of compliance evidence. This study introduces CONTESSA (Continuous Testing and Security Assurance), an integrated framework that coordinates contract enforcement, static security analysis, dynamic security analysis, resilience and chaos testing, and compliance evidence generation within a unified CI/CD pipeline. The framework employs a domain profile system in which a single YAML configuration parameterizes the five testing and assurance layers for enterprise and regulated healthcare deployment contexts. A Compliance Evidence Ledger generates structured evidence records linked to applicable control identifiers and protected through SHA-256 chaining to support evidence integrity, traceability, and auditability. Prototype evaluation was conducted using an 11-service microservice application to assess the framework's ability to identify defects and to produce traceable evidence of compliance during automated pipeline execution. The evaluation identified 13 HIGH and CRITICAL vulnerabilities across the five testing layers, including four authorization-related defects detected by contract enforcement before service deployment to the test environment. The resulting compliance report contained 42 evidence records covering eight SOX ITGCs and PCI DSS controls, providing complete control coverage for the ENTERPRISE profile. These results demonstrate that CONTESSA can coordinate functional contract verification, security testing, resilience assessment, and compliance evidence generation within a single pipeline architecture. The framework establishes compliance evidence as a first-class pipeline output while enabling security contract requirements to be verified alongside functional contract requirements. This integration provides a structured approach for improving security assurance, evidence traceability, and compliance readiness in enterprise microservice environments
Enterprise Copilot Architecture for Legacy Systems: A Framework for Extending AI Capabilities Across Mission-Critical Enterprise Platforms Muneer Basha Shaik
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.v6i2.2063

Abstract

Enterprise AI adoption is increasingly constrained by the difficulty of integrating AI capabilities with legacy platforms that continue to support critical business operations. Mainframe environments, SAP deployments, and proprietary enterprise applications contain established business logic, transactional data, and operational knowledge, but often lack standardized interfaces for direct interaction with modern AI systems. This article develops an Enterprise Copilot Architecture for Legacy Systems that enables AI copilot services to interact with legacy applications without requiring their replacement. The proposed architecture introduces an Intelligence Integration Layer that combines layered integration, service-oriented and API-driven communication, event-driven processing, agent-based intelligence, human-in-the-loop governance, and security-by-design. The approach further incorporates hybrid processing separation, multi-layer caching, context-aware retrieval, containerized deployment, and adaptive compute scaling to reduce processing overhead and minimize disruption to legacy platforms. The architectural analysis indicates that the proposed mechanisms provide a structured pathway to connect AI services to heterogeneous legacy environments while maintaining controlled access to enterprise data and business functions. The architecture also supports separating AI processing from core transactional workloads, allowing computational demands to be managed without directly modifying legacy applications. Context-aware retrieval facilitates access to institutional knowledge embedded in legacy systems, while governance mechanisms maintain human oversight over AI-generated actions and recommendations. The resulting framework demonstrates how contemporary AI capabilities can be introduced incrementally into established enterprise environments while preserving existing business logic, operational continuity, security controls, and architectural sustainability. It therefore provides a non-invasive architectural approach for extending legacy enterprise systems with scalable and governable AI copilot capabilities
Causal Explainable Machine Learning for Identifying Causal Factors in Tourist Decisions at Sustainable Travel Destinations Nita Syahputri; Fithry Tahel; Ermayanti Astuti; M Safii
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.1903

Abstract

This research aims to identify the factors that causally influence tourists' decisions at sustainable tourism destinations using a Causal Explainable Machine Learning approach. The research uses a synthetic dataset of 50,000 respondents with 31 variables representing demographic characteristics, travel behavior, environmental awareness, perceptions of destination sustainability, service quality, social influence, digital media exposure, eco-friendly promotions as treatment, perceived value and trust in the destination as mediators, and visit intention as the outcome. The research stages include data pre-processing, descriptive analysis, predictive modeling using Random Forest, propensity score estimation, and Causal Machine Learning using Causal Forest–Double Machine Learning (DML) to estimate the Average Treatment Effect (ATE) and Conditional Average Treatment Effect (CATE). The SHAP (SHapley Additive exPlanations) method is employed to explain feature contributions to tourists' decision predictions and compare predictive importance with factors associated with causal effect heterogeneity. This dataset also provides the original CATE as a reference for evaluating the accuracy of causal effect estimates with results of MAE=0.142061, RMSE=0.171324, and R²=0.712434. The results show that the Causal-XAI approach can distinguish factors that are important for prediction from factors related to the variation in the causal effects of eco-friendly promotions on tourists' decisions. CATE analysis shows that different tourist groups respond differently to sustainable tourism promotion interventions. These findings show that environmental awareness and destination sustainability are important characteristics related to different treatment effects. The proposed approach provides a more transparent, interpretable, and accountable analytical framework to support targeted marketing strategies, improve sustainable tourism experiences, and facilitate data-driven decision-making for sustainable tourism destinations
Model of the Influence of Road Pavement Conditions on Traffic Performance and Urban Driver Behavior Alexander Tuahta Sihombing; Dedy Rusmiyanto
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.2018

Abstract

Road pavement damage is a major concern in urban road networks because it affects traffic operations and may influence driver behavior. This study aims to examine the effects of pavement conditions on traffic performance and urban driver behavior through an integrated analytical approach. The study was conducted on four main roads in Tanjungbalai City, Indonesia: Jalan A. Rahman, Jalan SMA 3, Jalan Prof. Dr. Ir. Sutami, and Jalan Husni Thamrin. Pavement conditions were assessed using the Pavement Condition Index (PCI). At the same time, traffic performance was evaluated based on vehicle speed, traffic volume, and level of service (LOS) in accordance with the Indonesian Road Capacity Manual (MKJI) 1997. Driver behavior data were collected using a structured questionnaire and analyzed through Structural Equation Modeling (SEM). The PCI results ranged from 18 (very poor) to 100 (very good). Road sections with poorer pavement conditions exhibited lower average vehicle speeds than sections with better pavement conditions, although all observed sections were classified as LOS A. This finding suggests that LOS classification alone may not adequately capture the operational effects of pavement deterioration. SEM results indicated that pavement conditions had the strongest significant influence on driver behavior, with a t-value of 9.809 and p 0.001. Driver discipline attitudes also had a significant influence, whereas driver knowledge did not demonstrate a statistically significant effect. The model explained 68.5% of the variation in urban driver behavior (R² = 0.685). These findings highlight the importance of integrating pavement condition assessment with traffic safety management and behavioral considerations in urban transportation planning. Regular pavement maintenance and targeted safety measures may help improve traffic operations and support safer driving behavior.
Road User Satisfaction Based on Integrated Pavement Condition and Traffic Load in North Toraja, Indonesia Rilva Toding Bua'; Azril Tangke Sombolinggi; Abdias Tandy Arrang; Zilvany Payung
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.1894

Abstract

Road service quality assessment requires consideration of both objectively measured infrastructure conditions and users’ perceptions. This study developed an integrated road-service evaluation framework by combining pavement condition, traffic intensity, and user perceptions to examine their relationship with road user satisfaction. The study was conducted on four road sections forming an education–commercial corridor in North Toraja Regency, Indonesia, from May to July 2026. A quantitative explanatory design was applied using questionnaire data from 277 road users and objective data from pavement and traffic surveys. Pavement condition was measured using the Pavement Condition Index (PCI), while traffic intensity was represented by peak-hour traffic volume expressed in passenger car units per hour (PCU/h). The objective indicators were converted to a five-point scale and integrated with corresponding perception scores to construct Integrated Pavement Condition (X?) and Integrated Traffic Load (X?) using a 50:50 weighting scheme. Multiple linear regression was used to examine their relationships with Road User Satisfaction (Y). The regression models explained 39.6%–58.1% of the variation in satisfaction. X? showed a positive and significant relationship with satisfaction across all four road sections, whereas X? was significant on three sections but not on Rantepao–Sa’dan Road. The Customer Satisfaction Index ranged from 67.4% to 73.4%, indicating satisfied users across all sections. Importance–Performance Analysis identified X? as a priority for improvement on Rantepao–Bolu Road. The findings demonstrate that integrating objective road conditions with user perceptions provides a comprehensive basis for evaluating road service quality and identifying context-specific improvement priorities
Traffic Operational Performance Assessment of Bebora Road in Dili City, Timor-Leste: Capacity, Saturation, Speed, and Level of Service Joao Antonio Guterres; Erminia de Jesusu
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.1940

Abstract

Increasing traffic demand has become an important factor affecting the operational performance of Bebora Road in Dili City, Timor-Leste. The continuous growth of traffic volume may influence traffic flow characteristics, road capacity, vehicle speed, degree of saturation (DS), and level of service (LOS). This study aims to assess the operational performance of Bebora Road by examining road capacity, traffic volume, vehicle speed, degree of saturation, and level of service based on the Indonesian Highway Capacity Manual (MKJI) 1997. Field observations were conducted on Wednesday, Thursday, and Saturday to collect traffic volume, vehicle speed, and road geometric characteristics at selected observation points and traffic directions. The analysis indicates that the calculated road capacity of Bebora Road is 2,140.20 passenger car units (pcu) per hour. The highest degree of saturation was 0.837, recorded on Thursday in the direction of Mauloa Bridge, while the lowest degree of saturation was 0.654, observed on Wednesday in the direction of the traffic signal. All observed DS values remained below 1.00, indicating that the traffic demand during the observation periods was still within the road capacity available. However, the variation in traffic conditions resulted in Level of Service categories C and D, indicating moderate to relatively high traffic operational conditions. These findings demonstrate that although Bebora Road can currently accommodate the observed traffic demand, increasing traffic volume may gradually reduce operational performance and traffic mobility. Therefore, appropriate traffic management strategies, including improved traffic control, monitoring of traffic growth, and potential capacity improvements, are necessary to maintain acceptable operational conditions and support future transportation needs in Dili City
Closing the Regression Blindness Gap: Production Deployment of an AI-Grounded Enforcement Framework for Regression Coverage Assurance in Enterprise Change Control Vijay Kumar Kola
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.v6i3.2036

Abstract

Regression testing addresses a costly failure mode in enterprise change management, in which a change introduced for one purpose disrupts unrelated functionality. In large-scale SAP environments, regression-scope reference data may already exist as an organizational artifact, yet conventional change preparation does not necessarily consult it systematically, creating regression blindness and exposing production systems to avoidable defects. This study presents Rule R8, an enforced regression cross-check framework embedded within a rules-governed Retrieval-Augmented Generation architecture. The framework integrates regression-scope determination into the same processing pipeline used to generate primary test-case mapping recommendations, ensuring that every SAP Change Request receives an explicit regression determination and a contemporaneous audit record. The framework was deployed in production within a Quality Assurance Center of Excellence, processing live Change Requests across the SD, MM, FICO, TMS, and SCM modules. Evaluation demonstrated 100% coverage of regression determination across processed Change Requests, compared with a manual baseline enforcement rate of zero. An explicit no-match constraint prevented unsupported negative conclusions by flagging objects without corresponding regression-reference records for analyst review. The regression cross-check introduced zero additional analyst overhead while operating within a workflow that achieved an overall 84.6% reduction in analyst effort. During the deployment period, regression-related production defects attributable to scope gaps were eliminated, corresponding to documented annual prevention savings of USD 300,000 in defect remediation costs. The findings indicate that embedding regression verification as an unconditional pipeline control can transform a recurring process omission into an auditable compliance mechanism. However, the effectiveness and completeness of the control remain dependent on the accuracy, coverage, and ongoing maintenance of the underlying regression reference data
Multivariable Internet of Things Monitoring for Off-Grid Photovoltaic Operational Diagnostics Feby Ardianto; Eko Ariyanto; Bengawan Alfaresi; Muhammad Syaukani; Wira Hidayat Bin Mohd Saad
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.1892

Abstract

Off-grid photovoltaic (PV) systems require low-cost monitoring that captures environmental conditions and energy flows for operational diagnosis. This study evaluates an Internet of Things monitoring framework using 2,500 synchronized observations recorded at 10-minute intervals in May 2025 at a laboratory of Universitas Muhammadiyah Palembang, Indonesia. The objective was to assess whether component-resolved multivariable monitoring could characterize environmental–electrical coupling, identify interpretable operating states, and support operational screening before labeled fault data are available. The monitored system comprised a 1,200-Wp PV array, a 12-V 400-Ah battery bank, two 60-A MPPT controllers, a 3,000-W/VA inverter, and a 350-W water-pump load. Descriptive statistics, Pearson correlation, HC3-robust multiple linear regression, K-means clustering, and power-balance residual analysis were applied. Mean panel temperature, estimated solar irradiance, PV power, charging power, discharging power, and load power were 27.75 °C, 219.77 W/m², 190.14 W, 93.99 W, 98.74 W, and 198.56 W, respectively. PV power correlated strongly with estimated irradiance (r = 0.999) and charging power (r = 0.963), and negatively with discharging power (r = -0.533). The thermal and daytime PV-power models explained 97.7% and 99.5% of within-sample variance, respectively. Residual distributions varied systematically among operating states, supporting their use as internal-consistency and disturbance-sensitive screening indicators rather than definitive fault labels. Because irradiance was estimated from BH1750 illuminance using a published empirical relation without on-site pyranometer calibration, the strong irradiance–PV power correlation indicates internal association rather than validation of absolute irradiance accuracy. The framework therefore provides interpretable operational diagnostics before labeled fault data are available for off-grid PV systems

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