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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.
Arjuna Subject : -
Articles 91 Documents
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Precision Document Transaction Type Classifier Using Machine Learning Techniques Sabado, Jay Carlou C.; Sapuay-Guillen, Sheena I.
Data Science: Journal of Computing and Applied Informatics Vol. 9 No. 1 (2025): 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.v9.i1-19945

Abstract

This paper aimed to develop a Precision Document Transaction Type Classifier using machine learning to identify transaction types, aligning with the Ease of Doing Business Law (RA 11032), which aims to streamline government services and improve service delivery. With the use of existing government documents, a dataset was created and processed for the training and evaluation of models, including Naïve Bayes, Bidirectional Long Short-Term Memory (Bi-LSTM), and Bidirectional Encoder Representations from Transformer (BERT). The BERT Model was the most accurate, efficient, and precise among other models. For the development of the software application Agile Methodology was used to ensure iterative progress and adaptability during the development phase. For the software quality evaluation, it was assessed using ISO/IEC 25010:2011, achieving a general high score mean of 4.25 corresponding to a descriptive equivalent of Excellent covering various software quality metrics demonstrating reliability, efficiency and overall performance.
Blockchain Implementation on Subsidised LPG Distribution in Gas Supply Chain (Case Study: Medan) Nugroho, Habibie Satrio
Data Science: Journal of Computing and Applied Informatics Vol. 9 No. 2 (2025): 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.v9.i2-16624

Abstract

This study examines the potential of private blockchain technology on the Multichain platform for the implementation of a subsidised gas distribution information system in Medan. The objective is to enhance transparency, security, and reliability. The data were collected via a literature review and documentation analysis, and the system was developed using the waterfall methodology. The Multichain-based architecture ensures secure, transparent, and traceable transactions, thereby reducing the incidence of fraud and discrepancies. The results demonstrate that the architecture fulfils the requisite criteria, establishing a robust framework for gas distribution. This validates the effectiveness of Multichain-based private blockchain for improved efficiency and reliability in Medan's subsidised gas distribution system.
Spatial Clustering Analysis of Stunting in North Sumatra Based on Environmental Factors Using K-Means Algorithm Ramadhani, Fanny
Data Science: Journal of Computing and Applied Informatics Vol. 9 No. 2 (2025): 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.v9.i2-17179

Abstract

This research aims to analyze the spatial grouping of stunting events in North Sumatra based on environmental factors using the K-Means algorithm. The data used in this research includes the incidence of stunting, environmental factors (such as access to health services, living environment conditions, water use and sanitation), and spatial data (geographical coordinates). The data comes from Basic Health Research (RISKESDAS 2018, then processed and normalized. The elbow method and silhouette analysis are used to determine the optimal number of clusters, resulting in four different clusters. The application of the K-Means algorithm produces the following cluster characteristics: Cluster 1, with good environmental conditions and access to health services, shows low levels of stunting; Cluster 2, with moderate environmental conditions, shows moderate levels of stunting; Cluster 3, which is characterized by poor living conditions and limited access to health services, has levels high stunting; and Cluster 4, with varied environmental conditions but very limited access to health and sanitation services, also shows a high stunting rate. Validation using the Silhouette Coefficient produces an average score of 0.65 which indicates good clustering quality shows that environmental factors, access to health services, and sanitation conditions have a significant impact on the incidence of stunting. Based on these findings, policy and intervention recommendations are focused on Clusters 3 and 4, which have high stunting rates. The interventions carried out include increasing access and quality of nutrition, health services, sanitation conditions, economic empowerment, and health education.
Safety Measures For Special Care Individuals At The Bureau Of Fire Protection In Bolinao, Pangasinan, Philippines:Basis For A Plan Of Action Mayugba, Abelardo S.
Data Science: Journal of Computing and Applied Informatics Vol. 9 No. 2 (2025): 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.v9.i2-21837

Abstract

This study assessed the implementation of fire safety measures by the Bureau of Fire Protection (BFP) in Bolinao, Pangasinan, focusing on the needs of special care individuals, including persons with disabilities, the elderly, and those with mobility or cognitive impairments. Using a descriptive-comparative and correlational research design, data were gathered from 150 special care individuals and caregivers, and 20 BFP personnel through validated questionnaires. The results revealed that while special care individuals generally perceived the level of fire safety implementation as high across awareness, preparedness, facilities, training, and policy compliance, BFP personnel rated them more moderately, highlighting gaps in training and infrastructure. Statistical analysis showed significant discrepancies in perception and underscored the need for targeted interventions. The study concludes with a proposed action plan aimed at enhancing inclusive fire safety protocols, training, and equipment for vulnerable populations.
Identification Of Malaria Parasites Plasmodium Vivax on Red Blood Cells Using the Probabilistic Neural Network Method Mutiawani, Viska
Data Science: Journal of Computing and Applied Informatics Vol. 9 No. 2 (2025): 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.v9.i2-22535

Abstract

Malaria is a disease that is infects human red blood cells transmitted through the bite of a female Anopheles mosquito that contains the parasite genus Plasmodium. Plasmodium vivax is one of the types of parasites that causes malaria, which is known as the type of malaria with the widest distribution area, from tropical, subtropical to cold climates. The diagnosis of malaria, basically depend on microscopic analysis of Giemsa-smeared thin and thick films of blood. However, this diagnostic method is time consuming and prone to human error. To overcome this problem, a method is needed to automatically identify malaria parasites on red blood cells. This study proposes to identifying the malaria parasite Plasmodium vivax using the Probabilistic Neural Network method. The steps taken before identification are preprocessing using Green Channel, Contrast Limited Adaptive Histogram Equalization (CLAHE), Morphological Close and Background Exclusion, then segmentation with Otsu Thresholding, next step is post- processing with Connected Component Analyst (CCA) and feature extraction with Invariant Moment. The results of this research showed that the method used was able to identify the malaria parasite plasmodium vivax on microscopic images of reb blood cells with an accuracy rate of 97.14%, and sensitivity of 95%.
Simple IoT-Based Home Security System Using ESP32 and Blynk Fikri, Abdul
Data Science: Journal of Computing and Applied Informatics Vol. 9 No. 2 (2025): 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.v9.i2-22595

Abstract

Advances in Internet of Things (IoT) technology have brought about various innovations in home security systems. This research aims to design and implement an IoT-based home security system integrated with the ESP32, the KY-037 sound sensor, and the HC-SR501 PIR motion sensor. This system is capable of detecting suspicious movement or sounds around the home door and then sending real-time notifications to the Blynk app on the user's smartphone. The method used was the development of an ESP32-based prototype connected to the internet to transmit sensor data. Test results show that the system can provide notifications with a response time of less than 2 seconds. This system is easy to implement, energy efficient, and suitable for household use. This research demonstrates the great potential of IoT in improving security with affordable costs and high flexibility. The study also highlights the importance of sensor threshold testing to improve detection accuracy. Further developments could include integration with IP cameras and facial recognition systems.
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.

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