Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Applied Information System and Management Jurnal Sains Informatika Terapan (JSIT)
Soares, Domingas
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Languange, Linguistic, Communication & Media

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
An Integrated IT Governance and Project Management Framework for Resource-Constrained Universities in Timor-Leste Trisnawaty, Ni Wayan; Raharjo, Teguh; Soares, Domingas
Applied Information System and Management (AISM) Vol. 8 No. 2 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i2.46689

Abstract

This study designed and validated an integrated information technology governance (ITG) and project management strategy for resource-constrained universities in developing countries. A mixed-methods approach combined a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review, three criterion-based elite interviews at a private university in Timor-Leste, and expert validation to refine the model. The framework operationalized ISO/IEC 38500 principles as governance guardrails across the PMBOK 7th Edition performance domains, linking decision rights, escalation paths, and conformance duties to day-to-day delivery routines. Findings indicated that the integration clarified accountability, mitigated the mum effect through time-boxed escalation and red-flag protocols, supported phased low-bandwidth service deployment, and aligned institutional priorities with budget and capacity constraints. This study introduced a governance–execution fit mechanism that made governance actionable in resource-constrained higher education settings. It also provided policy recommendations for university leaders and regulators: formalize an IT Steering Committee (ITSC) by decree, embed ISO/IEC 38500 guardrails into portfolio and project life cycles, mandate lightweight governance artifacts (charters, responsible–accountable–consulted–informed (RACI) matrices, risk registers, and decision logs), and adopt phase-gated funding with targeted capability building. These measures strengthen feasibility, scalability, and strategic adoption across comparable contexts.
Patient Prevention Prediction and Diagnosis Using Data Mining in Healthcare Quality Management Noviyanty; guterres, juvinal Ximenes; Gusmao, Adozinda Soares; Soares, Domingas; Guterres, Anita; da Silva, Recardina Freitas
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The expansion of digital medical records and clinical data has strengthened the development of intelligent analytical systems to support early disease detection and improve diagnostic accuracy. This study aims to evaluate the performance of three classification algorithms, namely Random Forest, Support Vector Machine, and Logistic Regression, in predicting stroke risk using multidimensional patient clinical information. The dataset consists of 224 patient records derived from the Kaggle Stroke Dataset and additional questionnaire data collected from hospitals and primary health centers. The variables include demographic characteristics, clinical history, lifestyle factors, and physiological indicators. The research methodology involves several stages, including data preprocessing, feature selection using ANOVA F value, class balancing through the Synthetic Minority Oversampling Technique, model training, and performance evaluation using Accuracy, Precision, Recall, F1 Score, Matthews Correlation Coefficient, and Area Under the Curve. The results indicate that the Random Forest model achieves the highest performance, with an accuracy of 0.91 and an Area Under the Curve of 0.91, outperforming Support Vector Machine and Logistic Regression. This outcome confirms the effectiveness of ensemble based approaches in identifying complex nonlinear patterns and managing imbalanced data. The study contributes to healthcare quality improvement by providing a reliable prediction framework that supports early clinical decision making, reduces diagnostic delays, and enhances patient care outcomes.
Co-Authors da Silva, Recardina Freitas Gusmao, Adozinda Soares Guterres, Anita Guterres, Juvinal Ximenes Noviyanty Raharjo, Teguh Trisnawaty, Ni Wayan