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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.
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Articles 5 Documents
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Search results for , issue "Vol. 9 No. 1 (2025): Data Science: Journal of Computing and Applied Informatics (JoCAI)" : 5 Documents clear
A Hybrid Cryptosystem Using Rprime RSA And Extended Tiny Encryption (XTEA) For Securing Message Santoso, Zikri Akmal; Budiman, Mohammad Andri; Efendi, Syahril
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.i1-16574

Abstract

Abstract. Ensuring the security of messages in sending message publicly is very important, we must ensure the security of messages with one of security method called cryptography. Focusing solely on security can affect the speed of message delivery processes. Therefore, this research is conducted to provide solutions to both of these issues. Thus, this research will discuss the Analysis of Hybrid Cryptography Scheme in the combination of RPrime RSA and XTEA (Extended Tiny Encryption) in securing instant messages. Hybrid cryptography is one of the methods in cryptography that allows to enhance speed of message delivery with messages encrypted by symmetric algorithms and the symmetric algorithm keys will be encrypted using asymmetric algorithms, public keys. RPrime RSA is an asymmetric public key algorithm and one variant of RSA, which is a combination of Rebalanced RSA and MPrime RSA algorithms. XTEA is a symmetric key algorithm and improved version of the TEA algorithm. This research tested by using strings with uppercase letter, numeric, and punctuation characters with the value of k in RPrime RSA from 2 to 6 with unconstrained modulus digits. The result of the test indicate that the required time for encryption and decryption is proportional, the time processing for factorization to get d is proportional to the value of k.
Towards Automated Motor Impulsivity Monitoring in Real-world Scenarios: A Multiple Object Tracking Approach Dalimarta, Fahmy; Andono, Pulung Nurtantio; Soeleman, Moch. Arief; Hasibuan, Zainal Arifin
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.i1-16686

Abstract

Assessment of motor impulsivity often faces several challenges. Conventional assessments that rely on controlled settings often fail to capture impulsive behaviors in real-world contexts. This study proposes an automated approach using Multiple Object Tracking (MOT) technology to assess motor impulsivity. The aim was to develop a system for detecting and quantifying motor impulsivity in naturalistic, multi-person environments. By employing cutting-edge MOT algorithms, the solution tracks multiple individuals concurrently, enabling movement and interaction analyses. This methodology integrates MOT with behavioral models to identify motor impulsivity patterns such as abrupt trajectory changes or impulsive gesturing. Trained on real-world annotated datasets, the system ensures adaptability across settings. Our approach successfully distinguished impulsive movements from typical behavioral patterns, with an accuracy of 95.43%. This approach could revolutionize assessments by providing objective and quantitative measurements and facilitating enhanced diagnostics and personalized interventions. Extensive evaluations are required to assess real-time capabilities, robustness in occluded environments, and accurate impulsive pattern identification. These findings could enable broader clinical, research, and behavioral monitoring applications, advancing our understanding of the implications of motor impulsivity.
Phishing Detection Techniques: A review Abdolrazzagh-Nezhad, Majid; Langarib, Nafise
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.i1-19904

Abstract

Phishing remains one of the most pervasive and sophisticated threats to cybersecurity, exploiting human and system vulnerabilities to compromise sensitive information. This study systematically reviews and categorizes phishing detection techniques into four groups: anti-phishing tools, heuristic approaches, machine learning-based techniques, and metaheuristic algorithms. Each method is critically analyzed for its effectiveness, highlighting their strengths and limitations. The review identifies significant advancements in phishing detection, such as the adoption of hybrid techniques and real-time detection algorithms, while also addressing gaps, including handling zero-day phishing attacks and scalability in large datasets. The findings provide a roadmap for future research, encouraging the development of more robust, adaptive, and efficient solutions. This comprehensive analysis not only synthesizes the state-of-the-art in phishing detection but also lays the groundwork for designing next-generation defense mechanisms.
Economy and banking sector performance: Spillover effect of Uncertainty of Covid-19 on Non-performing loans of Turkish Agricultural sector Olorogun, Lukman Ayinde; Kamil, Anton Abdulbasah
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.i1-19905

Abstract

This spillover index research paper tries to find connectedness between Non-performing loans (NPL) and Covid-19 global pandemic specifically in the Turkish agricultural sector. The research covers variables of NPL, Geopolitical risk index (GPR), Fishing, Agriculture, Non-performing loans to total assets (NPL_TCL), Return on assets (ROA), Return on equity (ROE). The data for this research includes a monthly time series dataset covering between Dec. 2004–April 2020. To perform the statistical analysis descriptive statistics, correlation matrix with its T-statistics and probabilities, and Dielbold and Yilmaz index were adopted to uncover the level of connectedness among the group. The descriptive statistics results of the group reveal that all variables understudy were contributing factors to the increase of NPL of the banking sector in general. Specifically, the agricultural sector’s NPL has a significant effect on the banking sector’s aggregate NPLs. The correlation analysis indicated that there is a higher correlation between NPL and agriculture, Fishing, Timber, and a moderate average correlation between NPL and Hunting. Similarly, a moderate high correlation amongst individual agricultural sectors i.e. Hunting, Fishing, Agriculture, Timber and as well as higher correlation between ROA and ROE that are under consideration. This shows that there is somehow acceptable interconnectedness among the group. The results of the spillover index effects through Dielbold and Yilmaz procedure revealed a total spillover effect 57.3%. Whereas, another unexplained effect in this study is 43.7% which might be as a result random noise in the dataset due to impact of Covid-19 pandemic. This research is significant as it is first of its kind on a proof of spillover from the empirical viewpoints as it related to Turkey on level of spillover and its impacts on the NPLs of the banking sector measuring the agricultural sector’s contribution.
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.

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