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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 235 Documents
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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.

Page 24 of 24 | Total Record : 235

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