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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 239 Documents
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Few-Shot Cold-Start Workload Forecasting for New AI Inference Tenants with Time-Series Foundation Models Shilu He; Chengliang Li; Hengning Rao
Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): 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.v4i1.546

Abstract

This paper presents a reproducible empirical study of few-shot cold-start workload forecasting for new AI inference tenants using the Alibaba GPU-disaggregated DLRM serving trace. Instance lifecycles are transformed into hourly active-demand series, and resource reservations are normalized into capacity units to evaluate 24-hour forecasting under zero-shot, 5-shot, 10-shot, and full-history settings. Seven forecasting methods are compared: archetype mean prior, persistence, moving average, linear trend, seasonal naive, global residual ridge, and CT-TSFM, a compact cross-tenant time-series foundation model. The cold-start evaluation uses 46 held-out tenants, with 110 source tenants for pretraining and calibration. Results show that hourly demand is strongly persistence-dominated. Zero-shot forecasting yields a mean absolute error (MAE) of 326.26 normalized capacity units, whereas only five observations reduce MAE to 4.00 for persistence, global residual ridge, and CT-TSFM. Validation consistently selects a residual gate of 0.0 for CT-TSFM, indicating that retaining the persistence prior and rejecting cross-tenant residual transfer is the most reliable strategy. Calibration intervals achieve approximately 85–87% coverage against a 90% target. The findings demonstrate that a few recent observations substantially improve cold-start forecasting, while source-tenant metadata alone provides limited zero-shot planning capability.
A Therapist-Facing Session Copilot for Live Counseling Support: Reasoning-Guided Retrieval and Ranking from Multi-Turn Counseling Dialogues Yifan Zhang; Hailey Zhang
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.v4i2.547

Abstract

This study develops and evaluates a retrieval-based therapist-facing session copilot for live counseling support using the public bilingual Psy-Insight dataset. Rather than providing autonomous psychotherapy or relying on a generative large language model (LLM), the system assists human therapists by ranking historical responses, retrieving interpretable rationales, and providing conservative contextual support. The reproducible pipeline combines TF-IDF representations, class-balanced LinearSVC routers, nearest-neighbor rationale retrieval, and label-aware response ranking without LLM fine-tuning. Experiments use all 520 English and 431 Chinese sessions (6,208 and 5,776 turns, respectively) with session-level train/dev/test splits. Psychotherapy routing achieves strong macro-F1 scores of 0.897 in English and 0.757 in Chinese, whereas strategy routing remains weak (0.253 and 0.268). Label-aware rationale retrieval improves ROUGE-L from 0.145 to 0.152 in English and from 0.142 to 0.151 in Chinese. The best response-ranking approach presents retrieved reasoning in parallel rather than through reasoning-fused reranking, increasing MRR from 0.498 to 0.541 in English and from 0.519 to 0.523 in Chinese while maintaining low latency (6.98–14.64 ms/query). These results demonstrate computational feasibility but do not establish therapeutic safety or clinical effectiveness.
Power-Aware Inventory Planning for AI Infrastructure Using Job-Level Forecasting and LLM Workload Explanations Shilu He; Jiayi Nie; Chengliang Li
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.v5i1.548

Abstract

AI infrastructure planning is commonly expressed as a GPU-count problem, yet operational risk is created by the electric and thermal envelope that accompanies each accelerator. This paper evaluates a power-aware planning method on Dataset A, using the B200 eight-GPU Llama-8B training trace with 45,000 raw 20 ms telemetry rows and 8,940 reproducible supervised decision records after a 100 ms decision stride. The forecasting task predicts total eight-GPU power one second ahead from job-level counters, autoregressive lags, and rolling statistics. The planning task converts forecasts into a peak-aware admission rule and a circuit-inventory simulation for 32 concurrent jobs. XGBoost produced the strongest mean forecast, with MAE 273.26 W, RMSE 636.74 W, and R2 0.923. A calibrated high-quantile forecast produced lower peak-error behavior, reducing the scheduling violation rate from 5.31% under GPU-count-only admission to 0.18% while admitting 61.63% of decision points. In the inventory simulation, XGBoost mean forecasting used 21.00 mean circuits with 1.80% violation risk, whereas the calibrated p95 plan used 22.70 circuits and eliminated observed violations in 1,000 trials. The results show that capacity plans based only on GPU count hide measurable electrical risk. A combined GPU-capacity, power-envelope, and workload-explanation view produces a reproducible basis for AI data center purchasing, placement, and sustainability decisions.
Narrative-Aware Scientific Claim Verification Agent with Evidence Ranking for ClimateCheck Wenhao Su; Siyu Chen; Ethan Qian
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.v5i1.549

Abstract

Climate misinformation often combines a factual proposition with a recognizable narrative, such as denying observed warming, rejecting human causation, minimizing impacts, attacking mitigation, or casting doubt on climate science. This paper presents a lightweight narrative-aware scientific claim verification agent for the official ClimateCheck setting. The revised evaluation uses the official annotated ClimateCheck training data, the official publications corpus of 394,269 abstracts, and a claim-level validation split of the annotated data. The public ClimateCheck test file is treated as a blind claim list because its public fields do not contain verification or narrative labels. The system combines hashed BM25, TF-IDF retrieval, latent semantic analysis, narrative-family probabilities, and a logistic-regression verifier. Full-corpus retrieval shows that BM25 remains the strongest first-stage retriever, with Recall@10 = 0.466, while the narrative-aware hybrid obtains Recall@10 = 0.444. In the judged candidate reranking setting, the narrative-aware ranker obtains the highest Candidate Recall@1 = 0.789 and MAP = 0.848, compared with 0.759 and 0.843 for TF-IDF. End-to-end verification remains difficult: the BM25 top-1 pipeline reaches Macro-F1 = 0.408, while the narrative-aware pipeline reaches Macro-F1 = 0.355. Claim-level narrative evaluation no longer produces a perfect score; single-label top-family Macro-F1 is 0.422, and fine-grained multi-label CARDS-code Macro-F1 is 0.098. These results show that narrative information is useful for reranking already plausible evidence candidates, but it does not replace strong lexical retrieval and does not by itself solve claim verification.
Synergistic Integration of Neural Architecture Search, Ensemble Learning, and Meta-Learning with Explainable AI Migunani Migunani; Joseph Teguh Santoso; Budi Raharjo
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.v5i1.207

Abstract

The automation of deep learning model design through Neural Architecture Search (NAS) has emerged as a transformative paradigm, yet the integration of NAS with ensemble learning and meta-learning remains underexplored. This study presents a comprehensive framework that synergistically combines (1) NAS for automated neural network architecture discovery, (2) homogeneous ensemble learning through NAS-generated architectures, (3) heterogeneous ensemble learning integrating diverse base learners, and (4) meta-learning with stacked generalization for adaptive model fusion, all complemented by Explainable AI (XAI) via SHAP for model interpretability. The proposed framework is evaluated on four benchmark datasets: Iris, Wine, Breast Cancer, and Digits. Experimental results demonstrate that the heterogeneous ensemble achieves competitive or superior performance across all datasets, with cross-validation accuracy reaching 98.87% (wine), 97.72% (breast cancer), 98.05% (digits), and 95.33% (iris). SHAP-based explainability analysis reveals consistent feature importance patterns across NAS architectures, providing valuable interpretability insights. The framework establishes a robust pipeline for automated, accurate, and interpretable machine learning, addressing the growing demand for transparent AI systems in critical applications.
IntentRouter-QAC: Distilling LLM-Derived Intent Signals into Small Language Models for Context-Aware Autosuggest and Search Entry Routing Haowei Tu; Yinchen Shi; Sophia Chen
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.v5i1.563

Abstract

Search boxes increasingly function as routing interfaces: a partial query may require a completion, immediate submission, a context-sensitive suggestion, or a fallback action. This paper evaluates IntentRouter-QAC as a lightweight lexical routing framework and tests product retrieval independently. Five mutually exclusive operational outcomes are derived within the 20,000-row AmazonQAC test file and evaluated with a strict session-disjoint temporal split; product-query provenance is not used as a route class. Across five seeds, the combined word/character TF-IDF router reached 0.473 mean accuracy (SD 0.031) and 0.330 mean macro-F1 (SD 0.018), indicating uneven performance across routes. On the 951-row temporal test set, context-nearest completion achieved 0.033 Success@10 and 0.025 MRR@10, compared with 0.029 and 0.023 for confidence-gated routing. The paired MRR@10 difference was 0.002 (95% CI -0.007 to 0.010; Holm-adjusted p = 1.000). Product retrieval on 480 WANDS queries and 42,994 products produced a different pattern: a word/character lexical hybrid reached 0.690 nDCG@10 and 0.504 Exact-MRR@10, significantly exceeding the title-only baseline on both measures. Route-aware decision-making is therefore useful as an architectural separation of entry actions, but it does not automatically improve exact autocomplete ranking over a strong session-context baseline.
Cross-Dataset Parcel Workload Priors for Last-Mile Capacity Forecasting: Integrating Package Segmentation with Delivery Operation Signals Ruiyan Ma; Long Zhang; Annie Bai
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.v5i1.564

Abstract

This paper evaluates a cross-dataset framework for parcel segmentation, next-day last-mile operational-intensity forecasting, and capacity-oriented error analysis. The operational study uses 4,514,661 delivery tasks and 6,136,147 pickup tasks from LaDe-D and LaDe-P across five cities (May–October 2022), while the visual study uses 2,197 Package Segmentation images with 7,643 annotated package instances. A segmentation model estimates package count, foreground area, and instance-area dispersion to construct an additive workload descriptor. Because the images are not paired with operational records, city-day visual variables are represented as cross-dataset distributional proxies derived from empirical-rank mapping. Segmentation baselines are evaluated independently of forecasting. The random-forest pixel classifier achieves the best segmentation performance (IoU 0.5323, Dice 0.6450), outperforming YOLO11n-seg (IoU 0.3439, Dice 0.4591). Operational intensity is represented by the first principal component of delivery orders, active couriers, areas of interest, regions, and area types, explaining 89.19% of training variance. On a 230 city-day chronological holdout, ElasticNet Operational achieves the best forecasting accuracy (MAE 2.0521). Within XGBoost models, Vision-Prior records MAE 2.4988, slightly outperforming Delivery+Pickup (MAE 2.5154) and a shuffled-prior control (MAE 2.5973). However, the 0.0166 MAE improvement is not statistically significant under a paired moving-block bootstrap (95% CI: −0.0558 to 0.0319). A separate analysis of 6,112 Amazon routes and 1,457,175 packages similarly shows only a 0.24% MAE reduction from parcel-mix features. Overall, the proposed proxy provides only limited gains, while the operational ElasticNet remains the strongest baseline. Time- and location-paired visual observations are needed to establish practical operational benefits from computer vision.
Token-Burst-Aware Capacity Planning for LLM Inference Services: Request Arrival, Token Demand, and Failure Risk Modeling from BurstGPT Traces Jiayi Nie; Yinchen Shi; Lucas Zhao
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): 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.v5i2.565

Abstract

Large language model (LLM) inference services convert request arrivals into coupled input-token prefill and output-token decoding workloads. Capacity planning therefore depends on token volume, temporal bursts, service mix, queueing behavior, and reliability signals rather than request counts alone. This study evaluates an integrated planning pipeline on BurstGPT v1.1, comprising 5,288,173 raw requests over 121 trace days and 5,188,507 completed requests. Strictly chronological experiments aggregate demand, forecast hourly completed tokens, detect minute-level burst pressure, estimate zero-response risk, simulate capacity policies, and replay representative test hours in Vidur. Random forest, selected on the validation interval, achieved 64.73% weighted absolute percentage error (WAPE) on the locked test interval; XGBoost achieved the lowest test WAPE (64.57%), while the last-hour baseline reached 67.42%, indicating limited forecastability under a pronounced level shift. A seasonal-residual burst detector achieved F1 = 0.653, although burst prevalence was sensitive to rolling-horizon and quantile settings. For minute-level zero-response risk, raw XGBoost achieved ROC-AUC = 0.813 and average precision = 0.116; isotonic calibration improved the Brier score (0.0230–0.0204) and 10-bin expected calibration error (0.0262–0.0122), despite low F1. Static P90/P95 capacity eliminated under-provisioned test hours at cost indices of 13.10 and 18.09. More economical dynamic baselines achieved 12.65% under-provisioned hours at a cost index of 2.37 and 13.63% at 2.43. The validation-selected random-forest policy was cheaper but less reliable (34.31% at 1.23). Vidur replay linked normalized demand to A100/H100 GPU counts, latency, batching, and memory pressure. The results support conservative interpretation of point forecasts and validation of reserve rules under distribution shift, rare-event calibration, and serving-stack constraints.
Psychology-Informed Live-Commerce Analytics: Structural Modeling, Predictive Validation, and Governed Script Scoring for TikTok Shopping Xiaochen Li; Yinchen Shi; Jason Zhang
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.v5i1.568

Abstract

This paper examines the psychological factors associated with impulsive-purchase propensity in TikTok Live Commerce and translates those associations into an auditable analytics workflow. It presents a secondary analysis of a public survey workbook containing 415 Indonesian responses on Social Attraction, fear of missing out, Narrative Involvement, Telepresence, Parasocial Interaction, Social Presence, and Impulsive Purchase. The analysis combines measurement diagnostics, a bootstrapped composite structural model, repeated cross-validated classification and continuous prediction, calibration assessment, a coefficient-based illustrative scenario analysis, and external validation of an ethical microcopy screen. In the full sample, Narrative Involvement has the largest association with Parasocial Interaction (β = .516) and Social Presence (β = .496); Parasocial Interaction and Social Presence jointly account for 67.5% of the in-sample variance in Impulsive Purchase. The pattern remains in the quality-screened TikTok-viewer subset (n = 238; Impulsive Purchase R² = .647). For the quality-screened subset, the strongest full-feature classifier is Random Forest (ROC-AUC = .938 ± .031 across 50 held-out folds), while Ridge regression gives the largest mean continuous-outcome R² (.622 ± .114). On 2,356 ec-darkpattern strings, a page-grouped hybrid word/character classifier reaches F1 = .959 ± .011 and ROC-AUC = .989 ± .003. The scenario scores are deterministic implications of the estimated coefficients rather than observed purchase effects. The technical contribution is a governed decision-support architecture that keeps structural association, contemporaneous prediction, scenario prioritization, ethical screening, human review, and prospective testing distinct.
Development and Usability Evaluation of an AI-Powered English Vocabulary Learning Application Joseflim Marchel; Dendy Kurniawan; Azed Yayah Durrotun Nihayah
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): 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.v5i2.383

Abstract

Learning vocabulary is a major challenge for beginner foreign-language learners, as repetition-based methods can be tiring and often lead to rapid forgetting. Generative AI, particularly Large Language Models (LLMs), enables the automatic creation of large amounts of learning content. However, without appropriate pedagogical guidance, AI-generated materials may be inconsistent and provide limited support for effective learning progress. This study therefore developed and evaluated an adaptive application for beginner-level English vocabulary practice. The application uses an LLM to provide contextual assistance and combines it with a modified spaced repetition mechanism. Its vocabulary and learning activities follow the appropriate CEFR level, while the difficulty is adjusted based on the accuracy of each learner’s answers. The application was evaluated through a mixed-methods usability study involving 72 learners with beginner-level English proficiency. User experience and acceptance were measured using the System Usability Scale (SUS) and selected measures from the Technology Acceptance Model (TAM). Usability was rated highly. Although the participants were beginners with limited English, they generally found the interface comfortable and manageable. Their SUS responses produced a mean of 81.46 (SD = 7.18), a result classified as “Excellent.” They also viewed the application as useful and relatively easy to operate. The respective scores were M = 4.38 (SD = 0.42) for Perceived Usefulness and M = 4.29 (SD = 0.45) for Perceived Ease of Use.

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