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Global Science: Journal of Information Technology and Computer Science
Published by International Forum of Researchers and Lecturers
ISSN : 31089976     EISSN : 31089968     DOI : 10.70062
Core Subject : Science,
Computer Science & IT
Global Science: Journal of Information Technology and Computer Science; This a journal intended for the publication of scientific articles published by International Forum of Researchers and Lecturers This journal contains studies in the fields of Information Technology and Computer Science, both theoretical and empirical. This journal is published 1 year 4 times (March, June, September and December)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 35 Documents
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Improving Data Security and Verification in Federated Learning with Homomorphic Encryption: Literature Review: Subtitle Alfina Tiur Mida Sitanggang; Eddy Refianto Eddy; Ferry Muhamad Ramadhan; Frangky
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 2 (2026): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i2.200

Abstract

Federated Learning has emerged as a prominent solution for collaboratively training machine learning models without sharing raw data, thereby preserving user privacy in today's digital era. However, threats such as man-in-the-middle attacks, data reconstruction attacks, and model manipulation remain significant challenges for this approach. This literature review explores the integration of Homomorphic Encryption (HE) into Federated Learning to enhance data security and ensure model integrity verification. The findings indicate that the combination of Homomorphic Encryption and Federated Learning can reduce the risk of data leakage by up to 90% compared to conventional non-encrypted methods. Furthermore, despite introducing a computational overhead of approximately 20–30%, model accuracy remains relatively high, with only a 1–2% reduction. This study contributes to the development of a more secure, efficient, and reliable Federated Learning framework for critical applications, including healthcare, finance, and the Internet of Things (IoT). Keywords: Federated Learning, Homomorphic Encryption, Data Security, Model Verification, Privacy Protection.
Explainable AI for Predicting Hypertension Risk in Diabetic Patients Using Model Interpretability Techniques Samsinar; Cyntia Lasmi Andesti; Ummul Fitri Afifah
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 2 (2026): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i2.201

Abstract

Hypertension is a major comorbidity in diabetic patients, significantly increasing the risk of cardiovascular diseases and other complications. Despite advancements in machine learning for predicting hypertension, challenges such as class imbalance and lack of model interpretability remain barriers to clinical adoption. This study aims to bridge these gaps by integrating Explainable AI (XAI) techniques with resampling methods to predict hypertension risk in diabetic patients. This study applied SMOTE, SMOTE-ENN, and threshold tuning to improve model performance while maintaining interpretability. The Random Forest and XGBoost models were evaluated using accuracy, precision, recall, F1-score, and AUC. After applying SMOTE, Random Forest achieved an accuracy of 77.66%, with a recall of 33.33% and an AUC of 0.67. XGBoost with SMOTE showed an accuracy of 82.31% and recall of 23.81%, with an AUC of 0.69. The application of SMOTE-ENN improved recall to 53.33% for Random Forest, while XGBoost reached a recall of 47.62%. Threshold tuning enhanced recall to 61.90%, but decreased precision to 22.57%. The Balanced Random Forest model, after hyperparameter tuning, achieved an accuracy of 79.31% and a recall of 31.43%. This work’s key contribution is the integration of XAI techniques to enhance model transparency, improving its applicability in clinical settings. Despite improvements, further optimization of precision and recall is needed for real-world deployment, and future research should focus on validating this framework across diverse datasets.
Bridging the Language of Cybersecurity: A Semantic Comparison Between NIST and ISO/IEC 27000 Terminologies Eka Julianti; Dewi Andriyanti; Aji Nurrohman; Rudolf Sinaga
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 2 (2026): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i2.203

Abstract

Abstract: Background: Inconsistent terminology across cybersecurity frameworks undermines global governance and interoperability. The National Institute of Standards and Technology Cybersecurity Framework (NIST CSF 2.0) and ISO/IEC 27001:2022 share similar objectives but diverge semantically in defining risk, control, and resilience. This semantic gap causes difficulties in compliance mapping and automated policy translation. Research Objectives: This study aims to analyze the semantic similarity and divergence between NIST and ISO/IEC 27000 terminologies, identify conceptual structures influencing interoperability, and propose an AI-assisted foundation for harmonizing cybersecurity language globally. Methodology: A mixed-method semantic comparative design integrates Natural Language Processing (NLP) and ontology mapping. Using the nist_glossary.csv dataset and ISO vocabularies, terms were normalized and analyzed via cosine similarity using sentence-transformer embeddings. Ontological alignment was visualized through the Semantic Threat Graph (STG) and validated by certified experts using Cohen’s Kappa reliability tests. Results: From 672 term pairs, results show 40.9% high semantic equivalence, 38.8% partial overlap, and 20.3% semantic divergence. Strongest alignment appears in “Protect” and “Identify” domains, while divergences occur in governance and recovery-related terms. Ontology mapping revealed three conceptual clusters—Risk Governance, Technical Safeguards, and Organizational Readiness. Conclusions: Findings confirm a 79.7% total semantic alignment, indicating strong potential for harmonizing global cybersecurity standards. The study contributes an empirical model combining computational linguistics and AI-based ontology mapping to establish semantic interoperability, enabling unified cybersecurity governance and AI-driven compliance automation. Keywords: Semantic Interoperability; Ontology Mapping; Cybersecurity Frameworks; NIST; ISO/IEC 27000 Terminology
Predictive Modelling of Disease Patterns Using Time-Series Patient Data in Primary Healthcare Moh. Khoridatul Huda; Jerhi Wahyu Fernanda; Darmatasia
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 2 (2026): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i2.204

Abstract

Understanding and predicting disease distribution patterns in primary healthcare settings require models capable of integrating both spatial and temporal dimensions. Traditional statistical approaches often fail to capture complex non-linear relationships across locations and time, leading to delayed detection of disease clusters. Objective: This study aims to develop a spatiotemporal machine learning framework to identify and forecast potential disease hotspots using electronic primary care records from 2024. Methods: The dataset comprised 5,343 patient visit records containing temporal, geographic (village-level), and clinical attributes. Data preprocessing included temporal aggregation, spatial encoding, and feature normalization. Three models—Gradient Boosting Machine (GBM), Temporal Random Forest (TRF), and Multi-EigenSpot—were trained and evaluated. Model performance was assessed using AUC, F1-score, and spatial accuracy metrics to ensure both predictive precision and spatial coherence. Results: Analysis revealed a clear seasonal pattern, with disease incidence peaking between April and August. Spatial mapping identified consistent hotspots in Sungai Asam and Beringin, accounting for over 70% of total cases. Among all tested models, Multi-EigenSpot achieved the best performance (AUC = 0.91; F1 = 0.86), effectively capturing multi-cluster spatial variability across months. Conclusions & Implications: The findings demonstrate that spatiotemporal learning models can significantly enhance disease surveillance and early warning capabilities in primary healthcare systems. Integrating spatial intelligence with explainable machine learning improves predictive accuracy, supports evidence-based policy, and enables targeted interventions for emerging disease hotspots in resource-limited settings.
Uncovering Latent Patient Profiles in Multidimensional Healthcare Data Using Unsupervised Clustering: A Comparative Analysis Waliya Rahmawanti; Ni Kadek Winda Patrianingsih; Usep Abdul Rosid
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 2 (2026): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i2.205

Abstract

The increasing complexity of healthcare data presents significant challenges in identifying meaningful patient patterns, particularly in chronic disease management such as diabetes. Traditional analytical approaches often fail to capture hidden structures within multidimensional clinical data. This study aims to uncover latent patient profiles by applying unsupervised learning techniques to integrated demographic and physiological attributes, including age, body mass index, blood pressure, and gender. A comparative clustering framework was employed using K-Means, Hierarchical Clustering, and DBSCAN, with performance evaluated through Silhouette Score and Davies–Bouldin Index. The experimental results indicate that the optimal clustering configuration is achieved with K=3, where K-Means outperforms the other methods by producing more compact and well-separated clusters. Visual validation using Principal Component Analysis further confirms the structural coherence of the clusters. Specifically, Cluster 0 represents 60.4% of the dataset, with an average BMI of 24.31 and systolic blood pressure of 125.77 mmHg. Cluster 1, containing 29.3% of the patients, exhibits a higher average age of 61.16 years, with stable BMI and blood pressure values. Cluster 2, accounting for 10.2% of the patients, shows elevated physiological characteristics, including an average BMI of 26.49 and systolic pressure of 159.55 mmHg. The findings reveal three distinct patient groups: a majority group with moderate physiological conditions, an older-age group with relatively stable health indicators, and a smaller high-risk group characterized by elevated body mass index and significantly higher blood pressure levels. These results demonstrate the capability of unsupervised learning to identify clinically meaningful subpopulations without labeled data. In conclusion, this study provides a robust and scalable framework for patient segmentation using multidimensional healthcare data. The approach supports data-driven insights for healthcare analysis and risk stratification. Future work should explore the integration of additional clinical variables and advanced clustering techniques to enhance model interpretability and performance.

Page 4 of 4 | Total Record : 35

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