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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 16 Documents
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Search results for , issue "Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering" : 16 Documents clear
Efficient Temporal Segmentation And Classification Of Short-Form Video Content Using Lightweight CNN-LSTM Architecture Tan, Ben Liu; Liem, Chstina Angel; Amen, Mohamed
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.v5i1.441

Abstract

The exponential rise of short-form video platforms such as TikTok, Instagram Reels, and YouTube Shorts has transformed digital content consumption patterns, creating both opportunities and challenges in media analysis. One critical need is the efficient segmentation and classification of temporal segments within these videos to enable applications in content moderation, targeted advertising, and audience behavior research. This study proposes a lightweight deep learning architecture that integrates Convolutional Neural Networks (CNN) for visual feature extraction and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. The proposed CNN-LSTM framework is optimized for computational efficiency while maintaining high classification accuracy, making it suitable for deployment in resource-constrained environments. Experimental evaluations on a curated short-form video dataset show that the model achieves competitive performance compared with larger architectures, with significant reductions in memory usage and inference time. Furthermore, the temporal segmentation module effectively isolates meaningful visual-audio segments, enabling more precise classification outcomes. The results highlight the potential of lightweight architectures to address the scalability demands of modern video analysis systems without sacrificing accuracy. This research contributes to the growing discourse on efficient multimedia processing by bridging the gap between high-performance models and practical, real-time applications in the evolving short-form video ecosystem.
Privacy-Robust Incrementality Estimation in Cookieless Settings via Uplift Modeling: Reproducible Evidence from the Hillstrom E-Mail Experiment Bai, Jingwen; Wang, Haozhe; Wu, Qiyou; Zhang, Boning
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.v5i1.468

Abstract

Measuring advertising incrementality in the absence of user-level identifiers is increasingly constrained by platform policies and privacy regulations. In cookieless environments, practitioners often observe only aggregated or weak signals (e.g., cohort-level conversion counts) and must still estimate the causal lift of an intervention while quantifying uncertainty. This paper studies cookieless incrementality evaluation through the lens of uplift and individual treatment effect (ITE) modeling under explicit privacy constraints. We conduct full experimental evaluations on the MineThatData (Hillstrom) E-Mail Analytics Challenge dataset (64,000 customers in a randomized controlled experiment with three arms). We cast the task as a binary treatment problem—sending any e-mail campaign versus sending none—and compare six ITE estimators (S-, T-, X-, R-, and doubly robust learners, plus transformed-outcome regression) against cohort-only estimators that emulate cookieless measurement. The cohort estimator uses only aggregated counts and a Bayesian beta–binomial model to shrink noisy rates, and we evaluate robustness under k-anonymity thresholds and Laplace-noised differentially private aggregates. Across held-out test data, the best ID-level model (T-learner with logistic regression) achieves a Qini coefficient of 6.675 and improves the estimated policy conversion rate when targeting the top 20% of customers by predicted uplift. Cohort-only estimation retains a weaker and more variable signal; its point estimate is sensitive to privacy constraints but yields valid uncertainty intervals with 0.892 empirical coverage for a 95% interval in cohort-level validation. The results demonstrate that (i) causal lift is estimable without identifiers when randomized experimentation is available, (ii) doubly robust estimators provide strong performance and fast scoring, and (iii) privacy-preserving aggregation introduces an accuracy–privacy trade-off that can be quantified and monitored using bootstrap and Bayesian uncertainty.
A Comparative Study on Self-Organization in Wireless Sensor Networks Simon, Michael; Din, Salwa M.; Chib, Raja Jamal
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.v5i1.483

Abstract

Wireless sensor networks (WSNs) have emerged as a critical infrastructure for distributed sensing platforms in recent years. Their effective implementation requires self-organizing features that can adapt to rapidly changing ecological conditions. We have noticed in the comparative study that despite extensive research on individual self-organizing mechanisms, e.g., clustering, routing, and topology management. We believe there exists a significant analytical gap in systematically comparing these approaches across key performance metrics. Our study addresses this gap by conducting a comprehensive comparative analysis of four primary self-organization or autonomious mechanisms: clustering-based organization, dynamic routing protocols, topology adjustment strategies, and coverage reinforcement methods. In our work, using a simulation-based methodology with the NS-3 network simulator, we thoroughly tested these frameworks across networks with 50 to 500 nodes under varying traffic loads and mobility patterns. We assessed the performance using three key KPIs (key performance indicators). Reliability is measured by packet delivery ratio, scalability by convergence time, and energy efficiency by network lifetime parameters. Our results demonstrate that clustering approaches achieve 23% better energy efficiency in static deployments, whereas distributed routing protocols provide 34% better scalability in dynamic conditions. We also observed that topology adjustment mechanisms improve reliability by 18% under high node failure rates. These findings provide clear, evidence-based guidance for selecting the right self-organization technique for specific deployment scenarios and application requirements. We recommend that future research investigate hybrid mechanisms that combine multiple approaches and explore integrating machine learning to support adaptive strategy selection under heterogeneous network conditions.
LiDAR–Camera Object-Level Fusion for Multi-Target Tracking Using JPDA and EKF: A Reproducible Empirical Study on a PandaSet-Parameterised Five-Sequence Dataset Xin, Qi
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.v5i1.486

Abstract

Multi-target tracking in cluttered scenes is essential for automated driving, where downstream planning requires stable object identities and accurate state estimates. This paper provides a fully reproducible empirical and sensitivity study of a classical object-level LiDAR–camera fusion tracker that combines Joint Probabilistic Data Association (JPDA) with an Extended Kalman Filter (EKF) under a constant-velocity state model. Because the MathWorks PandaSet subset is distributed as a ZIP archive that cannot be ingested into our execution environment, we generate a PandaSet-parameterised five-sequence synthetic dataset with explicitly specified sampling rates, measurement noise, detection probabilities, and Poisson clutter, and report end-to-end results with fixed random seeds. Using sequential fusion (LiDAR JPDA–EKF update followed by a camera bearing update), we obtain a mean MOTA of 0.880 and a mean position RMSE of 0.361 m, compared with LiDAR-only JPDA–EKF MOTA of 0.883 and RMSE of 0.395 m. Fusion, therefore, improves localization accuracy while sometimes reducing MOTA due to additional association ambiguity introduced by camera clutter; this trade-off is discussed in terms of downstream use cases that prioritize state accuracy. Sensitivity sweeps show that probabilistic association degrades more gracefully than hard nearest-neighbor assignment as clutter increases and delineate regimes where camera information is beneficial. A camera-only bearing tracker is included as a diagnostic baseline (not as a competitive approach); as expected, given the observability limits, it is not reliable under the studied clutter conditions. The dataset specification, parameters, and reporting artefacts form a reproducible template for diagnosing JPDA/EKF tracking and object-level fusion.
IoT-Driven in the Banking Application Platforms Using a Real-Time SQL Injection Mitigative Measures Ngozi, Amaka Eugenia; Kalu, Oji Victor; Ikechukwu, Ezea Jonathan; Lilian, Okpalla Chidimma; Ezeh, Gloria Ngozi
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.v5i1.481

Abstract

The Internet of Things (IoT) integration into banking systems has revolutionized banking operations while also posing threats, including SQL injection (SQLi) attacks. Thus, the defenses of the existing system, such as access control mechanisms, firewalls, and signature-based Intrusion Detection Systems (IDSs), failed to detect both novel and obfuscated SQLi attempts. Hence, this research developed a machine-learning-based detection framework capable of identifying SQLi attacks on IoT-driven banking platforms. The model was trained on a Random Forest (RF) classifier and evaluated in a Python environment. Streamlit was used to deploy the model for real-time prediction, while performance visualization was through the Power BI dashboard. However, the results from the model’s evaluation were highly impressive, with 99.53% accuracy, 99.96% precision, and 98.78% recall. This demonstrated the model's ability to detect both known and unknown SQL patterns. However, the research concluded that combining behavioural analytics with a machine-learning approach is highly effective for securing IoT banking platforms and recommended periodic retraining using a deep-learning approach.
Ranking AODV Routing Characteristics in MANETs: Fuzzy Delphi Method Application Njoroge, Patrick; Ndia, John; Makupi, 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.v5i1.494

Abstract

Optimization of Mobile Ad hoc Networks (MANET) routing has the potential to improve performance. Enhancing the Ad-hoc On-demand Distance Vector (AODV) protocol, which is predominantly used in MANET routing, would lead to performance improvements. This study sought to provide a structured approach to identifying which AODV protocol characteristics should be prioritized for modification to improve performance in MANETs. This would provide guidance for future research on AODV prioritization. Thirteen AODV characteristics were identified through a systematic review. The Fuzzy Delphi Method was employed to analyze and prioritize the study characteristics. Route discovery characteristics were prioritized and ranked as number one, with a threshold value of 0.097, 100% agreement among experts, and a fuzzy score of 0.72. The experts unanimously agreed that modifying the route discovery characteristic has the greatest potential to improve performance in MANET. This was because a large amount of control traffic in the network is generated during the route discovery process. The other characteristics were route selection, route maintenance, route table management, routing updates, number of routes discovered, hop count, time to live, and use of hello messages. However, some characteristics were perceived to be structurally critical for AODV operation but less responsive to performance-optimization modifications, namely hop-by-hop communication, identification of route request messages, sequence numbers, and neighbor lists. Future studies should complement the expert-based prioritization of AODV characteristics with experimental and simulation evaluations to quantify performance gains.
IoT-Enabled Multimodel Emotion Detection and Assistive Smartwatch for Special-Needs Children Samrin, A.; Saidharshini, S.; Shalima, S.; Moohambiga, B.
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.v5i1.471

Abstract

Children with neurodevelopmental and communication challenges often find it difficult to express emotional discomfort, stress, or urgent needs, which can delay timely support and increase anxiety. To address this issue, this paper presents an IoT-enabled assistive smartwatch designed to support near real-time emotion inference and basic communication for children with special needs. The proposed system integrates physiological sensing, including heart rate variability, galvanic skin response, and skin temperature, with camera-based facial expression analysis using a multimodal data fusion approach. In addition to emotion-aware monitoring, the smartwatch provides a simple icon-based communication interface and a routine reminder module to support daily activities. The framework enables caregiver-side visualization of emotional trends, communication events, and adherence to routines through a cloud-based dashboard. The system is evaluated through a design-level feasibility assessment using simulated experiments and analysis of benchmark datasets. Overall, this work presents a technically feasible and ethically conscious assistive framework that highlights the potential of combining IoT and emotion-aware computing for supportive care applications.
Self-Supervised Representation Learning for Criminology: Detecting Anomalies, Classifying Reports, and Mapping Networks Hassan S., Noorul; S., Sivalakshmi; M., Janani; A., Fouziya; S., Thirisha
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.v5i1.473

Abstract

Crime analysis using various types of data, such as video surveillance, crime reports, and criminal networks, has been widely investigated in digital criminology. Most of the available data are unlabelled. In this work, we introduce a self-supervised learning framework for multimodal criminology, which enables the fully automatic learning of effective features for unlabelled video, text, and graph datasets and the completion of crime analysis tasks, including anomaly detection, crime report classification, and high-risk node prediction via contrastive learning, masked prediction, and graph self-supervised learning. The experimental results show that our SSL model learns high-quality features and achieves better performance than its supervised counterpart and baseline models. Unlike traditional deep learning-based models that require large amounts of labeled data, our proposed SSL model is label-efficient, scalable, and robust to artificial or anonymous data. Our work aims to develop an AI-based multimodal self-supervised learning approach for efficient, accurate, reliable, and safe crime analysis
AI-Driven Multi- Modal Fake Content Detection System Using Audio-Text Fusion and Transformer Network Jeeva, S.; Trisha, J. S.; Keerthana, S.
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.v5i1.475

Abstract

The rapid proliferation of AI-generated synthetic media has posed substantial threats to digital trust, particularly through audio deepfakes and manipulated text. Existing unimodal detection systems that analyze either audio or text in isolation remain insufficient to counter advanced generative attacks that exploit both modalities simultaneously. This paper proposes an AI-driven multimodal fake content detection framework that jointly leverages acoustic and linguistic signals to enable robust deepfake identification. Mel-Frequency Cepstral Coefficients (MFCCs) and Mel-Spectrograms are extracted from raw audio to capture spectral and temporal vocal patterns. At the same time, BERT-based transformer embeddings encode semantic and contextual information from transcripts generated via Automatic Speech Recognition (ASR). An attention-based fusion layer dynamically weights and integrates both feature streams, and a Random Forest–XGBoost ensemble classifier performs the final authenticity prediction. Experiments conducted on the ASVspoof 2019 benchmark demonstrate a classification accuracy of 95%, with precision of 93%, recall of 94%, and F1-score of 95%, outperforming standalone audio-only and text-only baselines by approximately 4–7%. These findings confirm that cross-modal feature fusion substantially reduces false-detection rates and improves generalization over single-modality approaches. The proposed system offers practical applicability in cybersecurity, voice biometrics, and digital forensics.
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.

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