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Ainul Hizriadi, S.Kom., M.Sc.
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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 86 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.

Page 9 of 9 | Total Record : 86

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