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Dahlan Abdullah
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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 775 Documents
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Embedding Fraud Intelligence Across the FinTech Customer Lifecycle: A Unified Framework for Enrollment, Ongoing Transaction Monitoring, and Account Change Verification Using Agentic AI Kedarnath Goud Kothinti
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.v6i2.2065

Abstract

Financial technology (FinTech) fraud can occur throughout the customer lifecycle, including customer onboarding, payment transactions, and changes to account information. Fragmented fraud controls across these stages can limit the sharing of risk signals, increase duplicated verification activities, and delay coordinated responses to emerging threats. This paper proposes and evaluates a three-pillar fraud intelligence framework that integrates identity and document verification during onboarding, real-time behavioral and graph-based transaction monitoring, and anomaly-driven re-verification of account changes. The framework employs an agentic artificial intelligence orchestration layer to coordinate Know Your Customer (KYC) and Know Your Business (KYB) verification, device and session analytics, contact carrier validation, Automated Clearing House (ACH) micro-deposit verification, velocity-based rules, graph-network analysis, and multi-stage account-change protection with out-of-band re-verification. Evaluation was conducted using operational data from a production deployment at a mid-size FinTech platform serving 1.4 million enrolled customers across three independently measured operational windows. The evaluation measured processing latency, fraud-detection performance, account-takeover detection, and manual-review requirements before and after deployment. The system reduced enrollment processing latency by 99.3%, improved real-time transaction fraud detection by 60.9%, improved account takeover detection during account changes by 95.8%, and reduced monthly manual review staff hours by 76.5%. The results indicate that coordinating identity, behavioral, transactional, and account-change signals across the customer lifecycle can improve the efficiency of fraud detection while reducing operational review requirements. The framework also demonstrates how centralized AI orchestration can connect heterogeneous verification and monitoring controls without requiring each lifecycle stage to operate as an isolated fraud-control function. The proposed architecture, therefore, provides an integrated basis for continuous fraud intelligence across major FinTech customer lifecycle stages
A Comprehensive Literature Review on Skin Disease Identification Through Deep Learning-Based Bi-Directional Image-Text Matching Techniques Alfharizky Fauzi; Adang Suhendra; Aries Muslim; Emy Haryatmi
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.v6i4.1689

Abstract

Over the past few years, matching visual and textual data commonly known as image text matching has become increasingly important due to the rising demand for multimodal data understanding. ITM involves retrieving an image that matches a textual description, or vice versa, by evaluating the semantic similarity between visual and textual inputs. This task becomes particularly critical in medical applications such as skin disease identification, where accurate matching between clinical images and descriptive diagnostic text can aid in early detection and decision-making. One of the core challenges in this domain lies in bridging the semantic gap between visual features of skin lesions and medical terminology used in clinical notes or diagnostic reports. Despite various deep learning approaches developed for general ITM tasks, limited research has focused on domain-specific applications like dermatology. This paper provides a focused review of deep learning-based ITM frameworks, emphasizing their relevance to skin disease analysis. We explore model components such as image and text encoders, common embedding strategies (e.g., global, local, hybrid), and learning paradigms including adversarial, ranking, and pre-trained vision-language models. Additionally, benchmark datasets are discussed, including synthetic and real-world skin image–text pairs. The paper concludes by identifying key challenges such as dataset scarcity, domain adaptation, and explainability highlighting opportunities for advancing ITM in dermatological diagnostics.
Dynamic Condition Determination Using Artificial Intelligence in Enterprise SAP Sales Pricing Sirisha Ayyagari
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.v6i2.2021

Abstract

SAP S/4HANA condition determination traditionally relies on static and deterministic access sequences that evaluate condition records in a predefined order and apply the first eligible match. This mechanism provides predictable execution but does not dynamically account for demand context, customer behavior signals, or competitive pricing conditions. This paper proposes an AI-driven condition determination architecture for SAP S/4HANA that introduces a machine-learning prediction layer to select eligible condition types and determine their values while preserving standard pricing governance. The proposed architecture uses the PRCG_DOC_CONDITION_AMOUNT Business Add-In as the injection point, a Business Technology Platform-hosted API proxy as the integration layer, and a supervised learning model trained exclusively on SAP order-context data. Model features are derived from transaction and pricing objects, including XKOMV, KOMK, KOMP, and PRCD_ELEMENTS, enabling predictions from the live order context available during condition determination without external data enrichment. A confidence-gated governance mechanism controls the use of model predictions by applying validation and fallback rules when prediction confidence or eligibility criteria are not satisfied. Implementation evidence from IBM's Blue Harmony global enterprise program was evaluated across multi-country pricing scenarios to assess the accuracy of condition-type prediction and the reliability of condition-type determination. The results demonstrate measurable improvements in condition-type accuracy and reductions in pricing-determination errors compared with the conventional deterministic process. The architecture also maintains traceability of AI-influenced decisions and provides a controlled fallback to standard SAP determination when required. The findings indicate that machine-learning augmentation can extend SAP S/4HANA condition determination from a purely rule-based mechanism toward context-aware pricing decisions while maintaining auditability, operational safeguards, and clean-core compliance
AI-Driven Test Automation in Distributed Microservices Architecture Supratim Dutta
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.v6i1.2056

Abstract

Distributed microservices architectures have shifted a substantial share of software quality effort from code development toward validating interactions among independently deployed services. As continuous integration pipelines expand to tens of thousands of test cases, traditional automation faces increasing execution costs, flaky tests, and brittle locators that require extensive engineering maintenance. This paper examines how Artificial Intelligence (AI) techniques can restructure software testing across distributed microservices environments. The study maps six classes of AI techniques to the testing phases they most effectively support and develops a reference pipeline that integrates AI-assisted test generation, test selection, failure analysis, test-data management, and self-healing automation. Comparative measurements are synthesized from production engineering data and peer-reviewed empirical studies to assess changes in execution efficiency, defect detection, and automation stability. The results indicate that AI-driven testing reduces the mean test execution time per commit by 74% and the escaped-defect rate by 63% relative to traditional testing baselines. Self-healing locator strategies achieve 95–98% stability when user-interface structure changes, reducing failures caused by locator degradation. The analysis further indicates that sustained performance depends on human-AI collaboration, particularly for validating generated tests, interpreting ambiguous failures, and governing test priorities. Test-data engineering emerges as the principal constraint limiting further improvements because AI-based generation and execution remain dependent on representative, valid, and sufficiently diverse data. The findings demonstrate that AI-driven test automation can be implemented as a measurable engineering practice with identifiable performance benefits rather than treated solely as an emerging technological concept. The proposed framework provides a structured basis for integrating AI techniques into continuous testing workflows while retaining human oversight for quality assurance and decision-making
Physical AI in Heavy Equipment: A Review of Perception, Safety, Simulation, and Validation Architectures for Autonomous Off-Highway Machines Shyam Laheri Chunduri
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.v6i3.2041

Abstract

Off-highway equipment is undergoing a transition toward autonomous and AI-enabled operation under demanding duty cycles, limited connectivity, environmental variability, and persistent labor shortages. This review examines the engineering requirements for deploying physical AI in heavy equipment, with particular emphasis on perception, functional safety, validation, and autonomous operation. The review synthesizes recent literature published from 2020 onward across five interconnected domains: multi-sensor perception and scene understanding; functional safety and AI-related standards; simulation and digital twin infrastructure; autonomous haulage in surface and underground mining; and autonomous navigation in agricultural machinery. The analysis identifies a growing gap between the capabilities of field-oriented perception and control architectures and the validation provisions required to demonstrate safe and reliable AI behavior. Across the reviewed domains, simulation, digital twins, model-in-the-loop, software-in-the-loop, and hardware-in-the-loop testing increasingly constitute essential infrastructure for evaluating safety-relevant AI behavior before field deployment. The review further indicates that validation capacity is becoming a critical engineering constraint for scaling physical AI, particularly when perception uncertainty, environmental variability, sensor degradation, communication limitations, and hardware–software interactions must be assessed under representative operating conditions. These findings show that autonomous capability development alone is insufficient to support operational deployment without a corresponding validation architecture. The review therefore proposes a validation-centric sequencing framework that connects perception development, simulation, model- and software-in-the-loop testing, hardware-in-the-loop verification, safety assurance, and field validation. The framework provides an engineering basis for organizing progressive validation activities, establishing traceability between development and verification, and reducing the gap between AI capability development and reliable operational deployment of autonomous off-highway equipment

Page 78 of 78 | Total Record : 775

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