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Journal of Technology Informatics and Engineering
Published by Universitas Sains Dan Teknologi Komputer
ISSN : 29619068     EISSN : 29618215     DOI : 10.51903
Core Subject : Science,
Computer Science & IT
Power Engineering Telecommunication Engineering Computer Engineering Control and Computer Systems Electronics Information technology Informatics Data and Software engineering Biomedical Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 189 Documents
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Bias and Hallucination Evaluation in LLMs Sathiyaseelan, R; Reshma, A. B.; Ganga, P.
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.476

Abstract

The largest failure modes of LLMs to-date, bias and hallucination, have measurable harms in contexts where factuality and fairness are paramount. Both areas have experienced significant research growth; however, prior work on each generally operates as a disparate body of research, and there is a gap in a methodological framework for jointly measuring, tracing, and reducing both under the same experimental conditions. We provide that framework through an empirical evaluation (not a survey) of bias propagation and hallucination generation on four illustrative domains (medical, legal, finance, human resources) through a framework that addresses the three research questions: how can bias and hallucination be measured simultaneously through a replicable, domain-specific protocol; which techniques yield statistically meaningful improvements and a consistency of effectiveness; and how do causally informed methods fare against retrieval methods when tested for factual error reduction. We report new experiments using the GPT-4, LLaMA-2, and Falcon-7B models on the MIMIC-III, CrowS-Pairs, Yahoo Finance Q3 and XNLI-HR benchmarks while keeping our prompts uniform and our random seeds fixed. Methods included structural causal modeling, retrieval-augmented generation, uncertainty-aware RLHF, and hallucination-specific fine-tuning, with experiments on each method separately before merging them into combined frameworks. We observe that RAG achieved a 45% reduction in hallucination rates and that our causally guided active learning method reduced bias disparity by 25%; together, they substantially outperform either method alone. This contributes to a repeatable method for auditing bias and hallucinations, helping ensure alignment with EU AI Act standards and similar requirements.
Intelligent Image Rights Protection System Using Machine Learning, Cloud Services and IoT Alerts Yaamini, S. P.; Sivaranjani, K.; Nujithra, B.; Anitha, V.
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.477

Abstract

The rapid growth of digital media platforms has intensified the misuse of images through unauthorized manipulation, morphing, and non-consensual redistribution, posing significant threats to individual privacy and intellectual property rights. Despite the availability of reporting and takedown mechanisms, their effectiveness remains limited due to procedural complexity, delayed response times, and concerns regarding user anonymity. This paper presents the Smart Image Rights Protection (SIRP) system, a user-centric framework designed to detect, monitor, and respond to unauthorized use of images in online environments. The proposed system utilizes perceptual hashing to generate resilient digital fingerprints for registered images, enabling accurate identification after common transformations such as resizing, cropping, and minor visual alterations. A cloud-ready similarity analysis module is designed to support scalable matching in future deployments, while the current evaluation is conducted on a controlled dataset. An IoT-enabled hardware interface provides real-time alerts to users upon detection of potential misuse. Experimental results on controlled manipulation scenarios show that SIRP achieves detection accuracy of 95.6% for resized images and 94.3% for cropped images, outperforming traditional pixel-based comparison methods. Furthermore, automated evidence logging and instant notifications substantially reduce the latency between detection and user response actions. By combining robustness under common transformations, cloud-assisted processing, and timely user engagement, SIRP offers a practical solution for protecting digital image ownership and personal privacy.
Automation in Cybersecurity using Machine Learning: A CaseStudy on Anomaly Detection with Isolation Forest Hassan S., Noorul; L., Sandhiya; S., Kavya; E., Priyadharshini; T., Vanmathi
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.478

Abstract

The escalating sophistication of cyber threats necessitates advanced anomaly detection techniques that transcend traditional signature-based methods. This paper presents an automated cybersecurity framework leveraging the Isolation Forest algorithm for unsupervised anomaly detection in network traffic. Using the NSL-KDD dataset, we demonstrate that Isolation Forest achieves 95.2% detection accuracy with a 4.7% false-positive rate, outperforming conventional methods such as One-Class SVM (88.1% accuracy) and Local Outlier Factor (82.3% accuracy) in both computational efficiency and precision. Key advantages include: (1) real-time processing capability (8.2s training time, 4× faster than density-based approaches), (2) effective identification of rare attack types (U2R/R2L), and (3) elimination of dependency on labeled training data. The proposed system integrates dynamic threshold tuning and SHAP-based feature weighting to enhance detection stability and reduce false alarms. The results validate Isolation Forest as a scalable and reliable solution for modern intrusion detection systems, with strong implications for SIEM integration and real-time cybersecurity automation. Challenges in parameter tuning and encrypted traffic analysis are discussed, alongside future directions involving hybrid deep learning architectures.
An IoT-Based Smart Feeding System for Koi Fish Using Mamdani Fuzzy Logic Daru, April Firman; Hirzan, Alauddin Maulana; Putra, Galuh Ardiansyah; Christianto, Paminto Agung
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.480

Abstract

Koi feeding management requires precision in both feeding timing and feed quantity to maintain fish health and reduce mortality rates. Manual feeding practices are often inconsistent due to human limitations, leading to overfeeding or underfeeding. This study proposes an IoT-based smart feeding system for koi fish that integrates Mamdani fuzzy logic to determine adaptive feeding durations based on feed stock conditions. The system employs a NodeMCU ESP8266 microcontroller, an ultrasonic sensor for feed-level monitoring, a servo motor for feed dispensing, and the Blynk platform for real-time remote monitoring and control over the internet. Mamdani fuzzy inference is utilized to classify feed levels into linguistic variables (low, medium, and high) and generate appropriate feeding actions. Experimental results demonstrate that the proposed system operates reliably, with an average measurement error of 1.59%, indicating high accuracy in feed-level detection. The fuzzy logic controller effectively adjusts feeding duration according to feed availability, enabling consistent and controlled feeding schedules. The proposed system offers a practical and low-cost solution for intelligent koi fish feeding management and can be extended to broader applications in smart aquaculture systems.
Optimizing Regional Financial Management through the Transformation of the Digital Financial Information System in the Bekasi City Government Anwar, Dian Mohamad; Emita, Isyana; Melyani, Melyani; Rahadjeng, Indra Riyana; Indrarti, Wahyu; Rafik, Ahmad; Sari, Dian Indah
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.495

Abstract

This research adopts an applied research approach conducted in November 2023 at the Bekasi City Government. Data were collected through observation, interviews, and literature study. The system development methodology follows the System Development Life Cycle (SDLC), including system analysis, system design, implementation, and maintenance. The proposed digital financial information system is developed in Java and integrated with a MySQL database to manage financial data, including revenue and expenditure, SPP (Payment Request Letters), and SPJ (Accountability Reports). The system design incorporates tools such as Context Diagram (CD), Data Flow Diagram (DFD), Entity Relationship Diagram (ERD), and flowcharts to ensure systematic and structured development. The implementation of the digital system is expected to improve the accuracy, timeliness, and relevance of financial information, facilitate faster report generation, and enhance financial transparency and accountability in regional governance. The findings indicate that the transformation of a digital financial information system significantly supports the optimization of regional financial management processes, minimizes reporting errors, accelerates data processing, and strengthens decision-making processes within the Bekasi City Government. Therefore, the digital transformation of financial systems is essential in supporting good governance and sustainable regional development.
A Decision-Support Analytics Framework of Strategic HR Practices and Employee Performance in Islamic Banking Melyani, Melyani; Yulianto, Yulianto; Nikmah, Wasilatun; Armaniah, Henny; Subariyanti, Herudini; Yulianto, Andri Rizko; Daniel, Daniel
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.496

Abstract

This research analyzes Islamic banking's strategic human resource strategies by examining how compensation, work incentives, and job satisfaction affect employee performance. 210 Islamic finance personnel were quantitatively surveyed and evaluated using Partial Least Squares–Structural Equation Modeling (PLS-SEM) using SmartPLS. Bootstrapping. 5,000 subsamples were used to evaluate direct and indirect impacts. Research indicates that financial remuneration and work motivation negatively affect employee performance (β = −0.280 and β = −0.182, respectively). Job happiness has a low but positive impact on employee performance (β = 0.087). Minimal indirect effects and variation accounted for (VAF) values below 20%. Job satisfaction does not moderate the links between financial compensation and employee performance, nor between work motivation and performance.  The findings suggest that direct processes, rather than emotional measures such as job happiness, affect the performance of Islamic banking employees. The Islamic banking strategic human resource management literature is enhanced by emotional assessments, such as job satisfaction. Strategic human resource management in Islamic banking is informed by research, with managerial implications for fair remuneration systems and sustainable motivational techniques that reflect Islamic ideals.
Off-Policy Evaluation and Conservative Policy Selection for Slot-Level Dynamic Bidding and Ranking on the Open Bandit Dataset (Small) Ye, Tong; Mu, Jinyi; Hunter, James
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.503

Abstract

Dynamic bidding and ranking systems must improve revenue or engagement while avoiding harmful regressions during deployment. This paper presents an end-to-end offline OPE and conservative policy-selection workflow for slot-level contextual bandit approximations of ranking decisions. Using the small Open Bandit Dataset (OBD-small) from ZOZOTOWN (ZOZO, Inc.), each logged row is treated as a context-dependent choice among discrete actions (items), with binary click rewards and logged propensity. This formulation is suitable at the slot level but does not capture full listwise ranking or multi-step offline reinforcement learning. Dynamic bidding and ranking systems must improve revenue or engagement while avoiding harmful regressions during deployment. This paper presents an end-to-end offline OPE and conservative policy-selection workflow for slot-level contextual bandit approximations of ranking decisions. Using the small Open Bandit Dataset (OBD-small) from ZOZOTOWN (ZOZO, Inc.), each logged row is treated as a context-dependent choice among discrete actions (items), with binary click rewards and logged propensity. This formulation is suitable at the slot level but does not capture full listwise ranking or multi-step offline reinforcement learning. Empirically, highly deterministic evaluation policies exhibit extreme variance under sparse clicks, while the logistic reward model remains weak (ROC-AUC ≈ 0.5), limiting DM/DR interpretability. Clipped-DR mixing yields only limited certified improvements: in the women’s campaign, gains appear only at moderate confidence (δ=0.10) and for caps up to M=5, whereas stricter or looser settings revert to baseline; in the men’s campaign, certification is largely absent. These findings demonstrate that OPE diagnostics and conservative mixing enable reproducible offline selection under uncertainty, but do not indicate deployment-ready improvements.
Explainable Multi-Hop Question Answering for QA Assistants: Two-Hop Evidence Retrieval, Sentence-Level Supporting Facts, and Explicit Reasoning Paths Luo, Xiaofei
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.504

Abstract

Multi-hop question answering (QA) for customer-facing assistants requires not only accurate answers but also an auditable evidence trail that explains how the system arrived at each answer. We present a fully interpretable multi-hop QA pipeline that decomposes inference into three explicit modules—Retriever → Evidence Selector → Reasoner—and produces an explanation consisting of sentence-level supporting facts and an explicit two-hop evidence path. The retriever ranks candidate paragraphs using lexical IDF-weighted token overlap; the evidence selector chooses a small set of high-scoring sentences; and the reasoner extracts a final answer using weighted candidate phrase scoring and deterministic rules for date/number and constrained yes/no comparisons. We conduct full experimental evaluations on the complete development splits of HotpotQA (7,405 questions, distractor setting) and 2WikiMultihopQA (12,576 questions). On HotpotQA, sentence-level evidence selection improves Supporting Fact F1 from 0.334 to 0.419, and adding an explicit two-hop retrieval path further increases Supporting Fact F1 to 0.426 while raising paragraph recall@2 to 0.603. Answer F1 increases from 0.084 to 0.088. On 2WikiMultihopQA, evidence selection improves Supporting Fact F1 from 0.328 to 0.429 and Answer F1 from 0.071 to 0.075. These results quantify the contribution of explicit evidence selection and path-constrained retrieval to explainability and provide a practical, reproducible baseline for knowledge assistants that must justify answers with supporting facts.
Natural-Language Policy Reasoning with Proof Generation: Turning Platform Rules into Verifiable Knowledge Luo, Xiaofei
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.505

Abstract

Policy and compliance systems increasingly express rules in natural language, yet enforcement requires deterministic decisions and auditable explanations. This paper studies a practical pipeline that converts natural-language facts and rules into a verifiable knowledge base, answers queries with three-valued semantics (True/False/Unknown), and produces machine-checkable proofs. The contribution is system-level rather than a new reasoning formalism: we integrate controlled-language parsing, symbolic proof extraction, independent proof checking, and proof-based supervision in a single auditable framework. We evaluate the pipeline on two natural-language rule-reasoning benchmarks: (i) a balanced subset of ProofWriter’s open-world-assumption tasks (360 train, 360 test), and (ii) a RuleTaker-style dataset generated from its grammar and label semantics (1800 train, 900 test), both balanced across reasoning depths 0–5. We compare a text-only logistic regression baseline, a retrieval-based “proof” baseline, a symbolic forward-chaining reasoner with proof extraction, and a proof-trained classifier using generated proofs. To ensure fairness, LR-text and LR-proof share the same TF-IDF/logistic-regression setup, and the retrieval baseline uses the same representation with a fixed top-4 configuration. On ProofWriter-Balanced, the symbolic reasoner achieves 0.803 accuracy (0.808 macro-F1), while proof-trained classification reaches 0.825 accuracy (0.825 macro-F1). On RuleTaker-Rep, both methods achieve 1.000 accuracy. Proof verifiability clearly separates faithful from post-hoc explanations: symbolic proofs are verifiable for all predictions, whereas retrieval-based proofs are verifiable for only 31.4%. Sensitivity analyses varying reasoning depth, distractors, and proof corruption show that proof-based methods remain robust to noise but depend on proof integrity. These findings demonstrate the feasibility of auditable natural-language policy reasoning in controlled settings, while highlighting limitations in parser coverage and benchmark regularity.

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