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Data Science: Journal of Computing and Applied Informatics
Published by Universitas Sumatera Utara
ISSN : 25806769     EISSN : 2580829X     DOI : -
Core Subject : Science,
Computer Science & IT
Data Science: Journal of Computing and Applied Informatics (JoCAI) is a peer-reviewed biannual journal (January and July) published by TALENTA Publisher and organized by Faculty of Computer Science and Information Technology, Universitas Sumatera Utara (USU) as an open access journal. It welcomes full research articles in the field of Computing and Applied Informatics related to Data Science from the following subject area: Analytics, Artificial Intelligence, Bioinformatics, Big Data, Computational Linguistics, Cryptography, Data Mining, Data Warehouse, E-Commerce, E-Government, E-Health, Internet of Things, Information Theory, Information Security, Machine Learning, Multimedia & Image Processing, Software Engineering, Socio Informatics, and Wireless & Mobile Computing. ISSN (Print) : 2580-6769 ISSN (Online) : 2580-829X Each publication will contain 5 (five) manuscripts published online and printed. JoCAI strives to be a means of periodic, accredited, national scientific publications or reputable international publications through printed and online publications.
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Articles 5 Documents
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Search results for , issue "Vol. 10 No. 1 (2026): Data Science: Journal of Computing and Applied Informatics (JoCAI)" : 5 Documents clear
SmartNutri: An Android-Based Application to Improve Parental Nutrition Literacy and Growth Monitoring for Children Under Five Years Old Kaharu, Nur Alinuddin; Wildan
Data Science: Journal of Computing and Applied Informatics Vol. 10 No. 1 (2026): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v10.i1-23411

Abstract

Stunting and undernutrition among children under five remain major public health challenges in Indonesia, primarily due to low parental nutrition literacy, limited access to educational resources, and the absence of consistent household-level growth monitoring. These issues lead to poor nutritional practices and hinder national efforts to reduce stunting. Addressing these problems requires innovative, evidence-based digital interventions that can simplify complex nutrition information into practical guidance for parents. This study aims to develop and evaluate SmartNutri, an Android-based nutrition education application designed using Object-Oriented Programming (OOP) and the Waterfall development model. The application integrates a nutrition calculator, growth monitoring dashboard, and menu recommendations aligned with the Indonesian Ministry of Health Regulation No. 2 of 2020 and WHO growth standards, forming a comprehensive and user-friendly platform. A mixed-method approach was used, involving requirement analysis from 50 parents and 10 Posyandu health workers, iterative software development, and quantitative usability testing. The system achieved 100% functional accuracy across 25 test cases and an average System Usability Scale (SUS) score of 84.6 (“Excellent”), reflecting high user satisfaction and operational stability. Parental nutrition knowledge significantly increased from 58.4 ± 10.2 to 81.7 ± 8.9 (p < 0.001) after four weeks of use, confirming SmartNutri’s educational effectiveness. SmartNutri successfully bridges the gap between nutrition literacy and behavioral practice, providing a scalable, evidence-based digital tool to support early childhood nutrition. Future research will focus on long-term impact assessment, integration with community health systems, and AI-driven personalization to enhance engagement, scalability, and public health relevance.
The Evolution of Cybercrime and Its Impact on Various Sectors Aprilia, Cindy; Gebrewold, Robel Amare; Bamiduro, Mercy Damilola; Borisova, Niurguiana; Mengdigali, Anel
Data Science: Journal of Computing and Applied Informatics Vol. 10 No. 1 (2026): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v10.i1-24471

Abstract

This research undertakes an investigation, into the development of cybercrime and its significant consequences in five crucial industries; Manufacturing, Finance and Insurance, Professional Business and Consumer Services, Energy and Retail and Wholesale. By examining the progression of cybercrime, the study traces its evolution from curious hacking to its present state as a sophisticated global threat. The analysis delves into cyber incidents in these industries exploring how these criminal activities have evolved from simple digital pranks to globally impactful actions. The narrative provides insights into the effects of these crimes and the evolving strategies employed to mitigate risks enhancing our understanding of the ever-changing cybersecurity landscape. This research serves as an introduction to facilitate an exploration laying the foundation for an understanding of past current and emerging trends in cybercrime, within these key sectors. It aims to significantly contribute to the development and strengthening of future cybersecurity approaches and resilience by providing a deep understanding of these trends.
Generative AI Usage and Information Literacy Skills among University Students in North-West Nigeria: Generative AI Usage and Information Literacy Skills among University Students in North-West Nigeria Dada, Kayode Sunday John; Quadir, Opeyemi Romoke
Data Science: Journal of Computing and Applied Informatics Vol. 10 No. 1 (2026): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v10.i1-24709

Abstract

This study examined the relationship between generative AI usage and information literacy skills among university students in North-West Nigeria. The research investigated awareness levels, evaluation practices, ethical considerations, and barriers affecting the integration of AI-powered tools in academic contexts. Employing a quantitative research design, the study surveyed 385 undergraduate students from federal, state, and private universities using the Generative AI and Information Literacy Impact Questionnaire (GAIL-IQ). Data analysis utilized descriptive statistics including means, standard deviations, and frequencies through SPSS version 26. The ACRL Framework for Information Literacy (2016) provided the theoretical foundation, emphasizing threshold concepts in information understanding. Findings revealed moderate awareness levels (M=3.42, SD=0.89) of AI-powered tools among students, with significant variations across institutional types. Students demonstrated limited capacity in evaluating AI-generated content credibility (M=2.78, SD=0.94), raising concerns about information accuracy assessment. Ethical practices regarding attribution and academic integrity showed moderate adherence (M=3.15, SD=1.02), though infrastructural constraints and inadequate training emerged as primary barriers (M=3.68, SD=0.87). The study concluded that while students increasingly engage with AI-powered tools, critical evaluation competencies and ethical awareness require substantial improvement. The study recommends that Universities in North-West Nigeria should integrate comprehensive information literacy training programs specifically addressing AI-powered content evaluation, ethical usage frameworks, and attribution practices into undergraduate curricula to enhance academic integrity and critical thinking capabilities.
Quantum Computing, Blockchain Technology and its Future Impact on Library Encryption Standards in Nigerian Libraries: Quantum Computing, Blockchain Technology and its Future Impact on Library Encryption Standards in Nigerian Libraries Dada, Kayode Sunday John
Data Science: Journal of Computing and Applied Informatics Vol. 10 No. 1 (2026): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v10.i1-24708

Abstract

The rapid evolution of information technologies is ushering in a new era for academic libraries, characterized by the simultaneous emergence of quantum computing and blockchain technology. This research investigated how these innovations could transform information management practices in Nigerian higher education libraries. The study adopts a quantitative methodology, surveying 242 librarians, computer scientists, librarians, computing specialists, and information technology officers working in university libraries to assess readiness and perception. Conversely, block chain technology offers a decentralized, immutable framework for secure digital rights management, verified academic credentials, and transparent collection development. Findings show moderate support for quantum computing in data encryption (mean=3.36, SD=1.05), but stronger endorsement for Blockchain in collection transparency (mean=3.73, SD=0.98) and bibliographic management (mean=3.74, SD=0.91). Challenges dominate, with high agreement on costs (mean=4.40, SD=0.80), skills gaps (mean=4.32, SD=0.79), and power issues (mean=4.43, SD=0.85). The paper concludes that for Nigerian libraries to remain relevant and secure, they must transition towards post-quantum cryptography while leveraging block chain for data integrity Based on these findings, the study recommends immediate formation of a national consortium among Nigerian tertiary libraries to pool resources for pilot projects in block chain-based archiving and to develop a roadmap for quantum-resistant security upgrades and the need for library administrators to prioritize building partnerships with technology departments, seek external funding for infrastructure upgrades, and develop comprehensive training programs to equip staff with necessary technical competencies for managing next-generation security systems.
Optimizing K-Nearest Neighbor Using Ant Colony Optimization for Heart Disease Classification Arini, Florentina Yuni; Pongthanoo, Patcharanikarn; Salsabila, Kansa Maulina; Raihan, Muhammad; Muzakki, Naufal Habib
Data Science: Journal of Computing and Applied Informatics Vol. 10 No. 1 (2026): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v10.i1-23647

Abstract

Heart disease is one of leading causes of death globally, making early detection essential for improving clinical outcomes. This study presents a heart disease prediction approach using the K-Nearest Neighbor (KNN) algorithm, addressing class imbalance with Synthetic Minority Over-sampling Technique (SMOTE) and enhancing feature selection through Ant Colony Optimization (ACO). Exploratory data analysis identified age, gender, cholesterol, blood pressure, e xercise-Induced Angina (EIA), ST-segment depression, number of affected vessels, and thalassemia status as key indicators of disease severity. KNN model achieved 0.90 accuracy with balanced precision and recall. The employment of SMOTE improved sensitivity for the minority class, slightly reducing overall accuracy to 0.88. However, ACO as hyperparameter tuning KNN able to produce promising accuracy 0.91. This result indicate that combining KNN with metaheuristic optimization provides a reliable, interpretable method for heart disease prediction, offering valuable support for clinical decision-making and risk assessment.

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