cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Johan Reimon Batmetan
Contact Email
garuda@apji.org
Phone
+6285885852706
Journal Mail Official
danang@stekom.ac.id
Editorial Address
Jl. Majapahit No.304, Pedurungan Kidul, Kec. Pedurungan, Semarang, Provinsi Jawa Tengah, 52361
Location
Kota semarang,
Jawa tengah
INDONESIA
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
 20   21   22   23   24 
Design of an AI-Driven Analytical Framework Integrating Machine Learning and Hyperspectral Remote Sensing for Detection and Classification of Locust-Prone Areas in South Nyanza Kenya Emmanuel Ochako Manyange; Juliana Kamaghe; lilian Mutalemwa
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.v5i2.560

Abstract

Desert locust outbreaks pose a persistent threat to agricultural productivity and food security in East Africa, while conventional surveillance approaches remain limited by delayed reporting, restricted spatial coverage, and weak predictive capability. This study develops and evaluates an artificial intelligence-driven analytical framework that integrates machine learning with hyperspectral remote sensing for detecting and classifying locust-prone areas in South Nyanza, Kenya. An empirical quantitative experimental design was applied to 150 georeferenced spatial observation units using spectral, vegetation, and bioclimatic indicators derived from Sentinel-2, CHIRPS, and MODIS data. The analytical framework incorporated automated feature engineering, Random Forest classification, Logistic Regression, and geospatial hazard visualization. The Random Forest model, configured with 500 decision trees and an mtry value of 3, achieved an overall classification accuracy of 81.33%, precision of 81.58%, recall of 81.58%, F1-score of 81.58%, and an Out-of-Bag error rate of 18.67%. The validated ROC-AUC reached 0.835, indicating good discrimination capability. Variable importance analysis identified precipitation, soil moisture represented by SAVI, and vegetation greenness represented by NDVI as the most influential predictors. Logistic Regression showed a positive association between hyperspectral indicators and locust-prone classification, although the predictors were not statistically significant at the 5% level. The findings demonstrate that integrating machine learning with remotely sensed environmental indicators provides a scalable approach for strengthening locust surveillance and supporting evidence-based early warning systems.
Performance Evaluation and Validation of an AI-Driven Hyperspectral Remote Sensing Framework for Locust Surveillance Using Ground-Truth Data Emmanuel Ochako Manyange; Juliana Kamaghe; Lilian Mutalemwa
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.v5i2.561

Abstract

Desert locust outbreaks continue to threaten agricultural productivity and food security across East Africa, creating an urgent need for surveillance systems that are accurate, scalable, and capable of supporting early intervention. This study evaluates and validates an AI-driven hyperspectral remote sensing framework for locust surveillance in South Nyanza, Kenya, using ground-truth observations and conventional field-scouting records as benchmarks. The study employed 150 georeferenced spatial units, with 70% used for model training and 30% reserved for independent validation. Random Forest and Logistic Regression were applied to hyperspectral and environmental indicators, while model performance was assessed using confusion matrix metrics, Out-of-Bag error, and Receiver Operating Characteristic analysis. On the 45-unit validation set, the AI-driven framework correctly classified 37 locations, achieving an accuracy of 82.22%, precision of 83.33%, recall of 83.33%, and an F1-score of 83.33%, with an Out-of-Bag error rate of 18.20%. The Random Forest model achieved an ROC-AUC of 0.844, substantially higher than the 0.585 obtained from the conventional field-scouting baseline. The framework also reduced false-negative detections from 10 to 4 cases. These findings demonstrate that integrating machine learning with hyperspectral remote sensing can strengthen locust surveillance by improving classification reliability, reducing missed infestations, and supporting evidence-based early warning and intervention planning.
LLM-Inspired Ontology-Based Semantic Enrichment for FinTech M&A Intelligence in SEC Structured Disclosures Guanzheng Zhao; Dingyuan Zhang; Sisi Meng
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.v5i2.566

Abstract

This study develops an ontology-based event-intelligence framework for FinTech merger-and-acquisition evidence in U.S. Securities and Exchange Commission (SEC) structured disclosures. Five quarterly SEC Financial Statement Data Sets from 2025Q1 through 2026Q1 contain 32,254 filings, 7,335 registrants, and 18,312,494 numerical XBRL facts; validation adds a 1,800-filing full-text Form 8-K sample and 192 FDIC events. A deterministic ontology maps XBRL tag names, labels, and documentation to acquisition, disposition, valuation, integration, risk, and payment concepts. The method is therefore LLM-inspired semantic enrichment rather than direct LLM extraction. Logistic regression, decision tree, and random forest classifiers are evaluated in three expanding forward-quarter tests with training-only preprocessing and threshold selection. A proximal protocol retains semantically close predictors, whereas a strict protocol excludes label-generating variables and deterministic descendants. Across the rolling tests, proximal logistic-regression M&A detection attains mean F1 = 0.981941, ROC-AUC = 0.999517, and average precision = 0.998315; strict performance falls to F1 = 0.735640, ROC-AUC = 0.930870, and average precision = 0.824107. FinTech M&A F1 declines from 0.921198 to 0.449503. Strict random forests yield F1 = 0.798129 for integration risk and 0.753987 for valuation signals. In independent full text, strict main-text-plus-exhibit F1 is 0.297482; among 24 automatically linked FDIC events in rolling test quarters, 9 are detected. Near-perfect scores therefore describe ontology reconstruction, not transaction-level accuracy.
Explainable GLCM–Decision Tree Framework for Pulmonary Tuberculosis Classification from Chest X-Rays Wiwid Wahyudi; Fitro Nur Hakim; Nur Rokhman; Irdha Yunianto
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.v5i2.567

Abstract

Pulmonary tuberculosis (TB) remains a major global health challenge, while chest X-ray interpretation is still influenced by radiologist expertise and inter-observer variability. This study develops an explainable machine learning framework for pulmonary TB classification from chest X-ray images by integrating Gray-Level Co-occurrence Matrix (GLCM) texture features, statistical image features, and a Decision Tree classifier implemented in RapidMiner. The dataset comprised 1,000 chest X-ray images representing TB-positive and normal classes. Image preprocessing included resizing, grayscale conversion, and intensity normalization, followed by extraction of four GLCM features—contrast, energy, homogeneity, and correlation—and four statistical features comprising mean, variance, standard deviation, and entropy. Model performance was evaluated using stratified 10-fold cross-validation. The Decision Tree achieved an average accuracy of 85.4%, precision of 87.1%, recall of 83.6%, and F1-score of 85.3%. Beyond predictive performance, the model generated interpretable IF–THEN rules that provide transparent reasoning for classification outcomes. Analysis of false-negative cases further identified clinically important misclassification patterns that require additional diagnostic confirmation. The proposed framework demonstrates that interpretable machine learning can provide a practical balance between classification performance, transparency, computational efficiency, and reproducibility, supporting its potential use as a clinical decision-support tool for TB screening in resource-constrained healthcare environments.
Clustering of Infectious and Non-Communicable Disease Distribution in Lhokseumawe City Using the Fuzzy Gustafson-Kessel Method M. Rahmat Azhari; Eva Darnila; Yesy Afrillia
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.v5i2.576

Abstract

The high incidence of infectious and non-communicable diseases in Lhokseumawe City requires a data-driven approach for mapping distribution areas. This study designs a disease distribution clustering system using the Fuzzy Gustafson-Kessel (FGK) method and compares three fuzzy membership functions: linear, sigmoid, and Gaussian. FGK excels in handling non-uniform data distributions utilizing an adaptive covariance matrix based on Mahalanobis distance. The data comprised 38,063 cases from the Lhokseumawe City Health Office (2023–2024), aggregated into 4 sub-districts, and processed using z-score standardization. The clustering was implemented in a Python and MySQL web-based system and evaluated using the Xie-Beni validity index. The FGK method successfully grouped the sub-districts into 3 clusters: low, moderate, and high. For infectious diseases (converging at iteration 24), Blang Mangat formed the low cluster, Muara Dua and Muara Satu the moderate, and Banda Sakti the high. For non-communicable diseases (converging at iteration 9), Blang Mangat was low, Muara Satu moderate, while Banda Sakti and Muara Dua were high. The linear membership function proved optimal, yielding the lowest average Xie-Beni Index of 0.1316. In conclusion, the FGK method with a linear membership function effectively maps disease distribution, assisting the local Health Office in targeted policy planning and resource allocation.
Offline Routing for Health Language Systems with Clarification and Escalation Zijian Cao; Justin Gao
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.v4i2.591

Abstract

Health-language systems require a control decision before response generation: answer with the available information, request clarification, or escalate for professional review. This study compared eight offline routing policies on HealthBench using researcher-defined actions mapped from physician-agreed categories. The implementations used TF-IDF logistic regression, bootstrap ensembles, stacked classification, and deterministic decision rules. A cohort of 2,601 dialogues provided disjoint development partitions, a 323-dialogue in-distribution test, and a locked 453-dialogue Hard test. Six policies used dialogue-derived information; two also accessed case-specific physician rubrics. Under this privileged-information condition, the rubric-augmented router achieved the highest accuracy, 69.97% in-distribution and 65.34% on Hard. The privileged cascade had the lowest severity-weighted routing loss, 0.1564 and 0.2851. Removing its rubric stage increased Hard loss to 0.3324 and unsafe direct-answer omission from 36.72% to 43.28%. Nevertheless, the privileged cascade directly escalated only 9/16 urgent cases; 6/16 were routed to clarification and 1/16 to an answer. Rubric access improves offline discrimination, but the strongest results do not establish an equivalent deployable verifier. The framework supports controlled prototyping of pre-generation routing, with substantial residual errors and no evaluation of generated medical responses or clinical outcomes.
Domain-Shift-Aware Climate-Smart Crop Portfolio Advisory for Nepal with Self-Supervised Tabular Learning and Conformal Shortlists Lumeng Han; Derek Zhou
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.v4i2.592

Abstract

Crop advisory models can perform well on balanced benchmarks while failing under unfamiliar environmental or household conditions. This study evaluates two separate research tracks: crop-label classification using 2,200 agronomic benchmark records and observed crop-portfolio prediction using 43,753 village-census households and 981 TAFSSA households in Nepal. Eight crop classifiers were compared within a training pool, with independent temperature-calibration, conformal-calibration, and test subsets. Household models were evaluated under random and geographic holdouts, including 30 paired community-level ablations. An external explanation-consistency experiment used 900 held-out, human-annotated RAGTruth responses. Random forest attained the highest cross-validated macro-F1 (0.992), while LightGBM had the lowest conventional-model log loss (0.059). Temperature scaling produced negligible change in held-out log loss. At 90% nominal coverage, adaptive prediction sets included the benchmark label in 438 of 440 cases, with a mean of 1.09 crops. Environmental-cluster holdouts reduced macro-F1 to 0.000–0.656, partly because some test crops were absent from training. Village-census logistic-regression macro average precision declined from 0.680 under random partitioning to 0.578 under district holdout for the same test households. TAFSSA cross-district values were 0.332 and 0.445. A fixed-split contextual gain did not persist across community holdouts: full context reduced macro average precision by 0.037 relative to farm-only predictors. On RAGTruth, numeric checks detected 132 of 579 erroneous responses; a source-linked lexical screen detected 432 but also rejected 102 of 321 valid responses. The findings support cautious interpretation of benchmark shortlists and household associations. The components remain a research prototype without end-to-end or field validation of agricultural decisions in Nepal.
Lightweight Image–Text Triage for Rice Leaf Diseases with Clarify-or-Abstain Decisions and Nepal-Context Advisory Support Lumeng Han; Derek Zhou
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.v4i1.593

Abstract

Image-based rice disease screening requires assessment of unfamiliar conditions, degraded photographs, and the evidence used for advice. This study evaluates a modular image–text workflow with separately trained visual and retrieval components. A rotation- and reflection-aware audit of 5,932 rice leaf images identified 343 similarity groups for leakage-reduced training, validation, and testing. Seven lightweight classifiers and a 24,724-parameter CNN were evaluated alongside open-set rejection, image-quality triage, simulated recapture, and external testing on 10,333 exact-unique Paddy Doctor images. Color-texture logistic regression achieved 78.41% grouped-test accuracy and the CNN achieved 81.03%; their accuracies on 4,232 external images from the four known classes fell to 13.33% and 12.55%, respectively. External unknown-detection AUROCs were 0.512 for Mahalanobis scoring and 0.486 for CNN nonconfidence. Across 7,745 scoped CROP records, reranked retrieval recovered the paired answer within five candidates for 934/1,168 test queries (79.97%). Evidence-assisted benchmark accuracy was 65.21% on 365 questions and 41.34% on 179 without an exact question match in the retrieval collection. Four advisory gates enforced disease, location, treatment, and numeric-rate rules, with a trade-off between answer delivery and reference-answer retention. Nepal-context retrieval remained preliminary: the reranker succeeded on 7/8 queries (87.50%; Wilson 95% CI, 52.91–97.76%). Classification, triage, and advisory retrieval provide separate levels of evidence. The findings support further investigation of the workflow, while the external failures preclude claims of reliable field deployment.
Stockout-Aware Relational Demand Forecasting and Cross-Dataset Error Trade-offs Mengyi Chen; Jeffrey Jin
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.v4i1.594

Abstract

Retail sales are a censored view of demand when products become unavailable. This study evaluates profile-weighted temporal reconstruction and relational forecasting on FreshRetailNet-50K, then examines cross-dataset error tradeoffs on M5. FreshRetailNet contains 50,000 store–product series, 4.85 million daily records, hourly sales, and stockout labels. Category-hour profiles estimate missing intraday shares, while a trailing 28-row own-series prior supplies the demand scale. Artificial contiguous-hour masking on days without labeled open-hour stockouts yields reconstruction WAPE of 8.82%–21.32%; it does not validate natural-stockout demand recovery. On 206,562 observable rows in the seven-day official test, calibrated relational forecasting achieves RMSE 0.6761, MAE 0.3621, and WAPE 29.27%, versus 30.54% for observed-sales boosting. Recovery alone does not improve WAPE, and the incremental graph contribution is small. Two earlier seven-day windows support the direction of the calibrated comparison but do not assess seasonal stability. M5 evaluation covers 30,490 series and 28 rolling one-day-ahead origins. Fixed-budget 1:1 pooling lowers relational-model RMSE from 2.1075 to 2.0716, while increasing WAPE from 71.67% to 72.21%. Alternative sampling ratios and full-target augmentation show the same metric tradeoff. Dense series receive the strongest squared-error benefit, without a WAPE advantage. The findings distinguish controlled reconstruction accuracy from observable-sales prediction and show that cross-retailer pooling does not uniformly improve forecasting.

Page 24 of 24 | Total Record : 239

 20   21   22   23   24 

Filter by Year

2022 2026


Reset
Filter By Issues
All Issue Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): APRIL | JTIE : Journal of Technology Informatics and Engineering Vol. 3 No. 3 (2024): December (Special Issue: Big Data Analytics) | JTIE: Journal of Technology Info Vol. 3 No. 2 (2024): Agustus : Journal of Technology Informatics and Engineering Vol 3 No 2 (2024): Agustus : Journal of Technology Informatics and Engineering Vol. 3 No. 1 (2024): April : Journal of Technology Informatics and Engineering Vol 3 No 1 (2024): April : Journal of Technology Informatics and Engineering Vol 2 No 3 (2023): December : Journal of Technology Informatics and Engineering Vol. 2 No. 3 (2023): December : Journal of Technology Informatics and Engineering Vol 2 No 2 (2023): August : Journal of Technology Informatics and Engineering Vol. 2 No. 2 (2023): August : Journal of Technology Informatics and Engineering Vol 2 No 1 (2023): April : Journal of Technology Informatics and Engineering Vol. 2 No. 1 (2023): April : Journal of Technology Informatics and Engineering Vol 1 No 3 (2022): Desember: Journal of Technology Informatics and Engineering Vol 1 No 3 (2022): December: Journal of Technology Informatics and Engineering Vol. 1 No. 3 (2022): December: Journal of Technology Informatics and Engineering Vol 1 No 2 (2022): August: Journal of Technology Informatics and Engineering Vol 1 No 2 (2022): Agustus: Journal of Technology Informatics and Engineering Vol. 1 No. 2 (2022): August: Journal of Technology Informatics and Engineering Vol. 1 No. 1 (2022): April: Journal of Technology Informatics and Engineering Vol 1 No 1 (2022): April: Journal of Technology Informatics and Engineering More Issue