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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 3 Documents
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Search results for , issue "Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering" : 3 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.

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