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Journal of Computer Science Advancements
Published by Yayasan Adra Karima Hubbi
ISSN : 30263379     EISSN : 3024899X     DOI : https://doi.org/10.70177/jsca
Core Subject : Science,
Computer Science & IT
Journal of Computer Science Advancements is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of science, engineering and information technology. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the Journal of Computer Science Advancements follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 86 Documents
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The Influence of Artificial Intelligence Technology on User Experience in E-Business Haerawan, Haerawan; Mudinillah, Adam; Zou, Guijiao; Anggara, Reddy
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1623

Abstract

The rapid advancement of artificial intelligence (AI) technology has transformed the landscape of e-business, significantly influencing user experience. AI-driven tools such as chatbots, personalized recommendations, and predictive analytics are becoming integral to e-business platforms. Despite widespread adoption, understanding the extent to which AI enhances user satisfaction, engagement, and loyalty remains an area requiring further exploration. This research examines the influence of AI technology on user experience in e-business, focusing on its practical applications and user perceptions. The study adopts a mixed-method approach, combining quantitative surveys with qualitative interviews. Data were collected from 300 e-business users and analyzed to assess key metrics such as usability, efficiency, and satisfaction. Additionally, in-depth interviews with industry experts provided insights into the strategic implementation of AI technologies. The findings reveal that AI significantly enhances user experience by offering personalized interactions, streamlining navigation, and improving response times. Users reported higher satisfaction levels when AI-driven features were implemented effectively. However, concerns about data privacy and algorithmic biases emerged as critical challenges, indicating the need for balanced approaches in AI deployment. The study concludes that while AI technology holds immense potential to revolutionize user experience in e-business, its effectiveness depends on strategic implementation and addressing user concerns. Future research should explore the integration of AI with other emerging technologies to further optimize user interactions in digital environments.
Use of Blockchain for Data Security in E-Government Systems Ridwan, Achmad; Maharjan, Kailie; Ulwi, Krim
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1624

Abstract

The increasing reliance on digital platforms for public administration has heightened concerns about data security in e-government systems. Cyber threats, unauthorized access, and data breaches pose significant risks to the integrity and confidentiality of sensitive governmental information. Blockchain technology, with its decentralized and tamper-proof nature, offers a promising solution for enhancing data security in e-government systems. This research explores the use of blockchain to safeguard data in e-government platforms, focusing on its potential benefits, challenges, and implementation strategies. The study adopts a mixed-method approach, combining a systematic literature review and expert interviews. The literature review analyzed 50 academic articles and industry reports, while interviews with 10 blockchain experts provided practical insights. Key factors such as data integrity, transparency, and access control were evaluated to determine blockchain’s effectiveness in addressing e-government security challenges. The findings reveal that blockchain significantly improves data security by ensuring immutability, enabling secure data sharing, and reducing reliance on central authorities. Experts highlighted blockchain’s potential to enhance transparency and accountability while maintaining privacy through cryptographic techniques. However, challenges such as high implementation costs, scalability issues, and regulatory uncertainties were identified as barriers to adoption. The study concludes that blockchain can revolutionize e-government data security by offering a robust and decentralized framework. Addressing the challenges of implementation and policy alignment will be critical for realizing its full potential. Future research should focus on pilot projects and sector-specific adaptations to accelerate blockchain adoption in e-government systems.
Implementation of a Cloud-Based E-Learning System for Integrated Learning in Higher Education Parini, Parini; Rahmi, Sri Nur; Bili, Fransiskus Ghunu; Ayaka, Ahmya
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1625

Abstract

The integration of technology in higher education has gained significant momentum, with cloud-based e-learning systems emerging as a transformative approach to support integrated and flexible learning environments. Traditional learning systems often face limitations in scalability, accessibility, and resource-sharing, prompting the need for innovative solutions. Cloud-based e-learning systems offer a centralized platform that enhances collaboration, resource management, and learning continuity. This research explores the implementation of a cloud-based e-learning system in higher education institutions, focusing on its impact on learning outcomes and system efficiency. The study employs a mixed-method approach, combining quantitative surveys and qualitative interviews. Data were collected from 300 students and 50 faculty members across three universities that recently adopted cloud-based e-learning platforms. The research assessed system usability, learner engagement, and academic performance, alongside implementation challenges and benefits. The findings reveal that cloud-based e-learning systems significantly improve accessibility, resource-sharing, and collaboration among students and educators. Survey results indicated a 40% increase in learner engagement and a 35% improvement in resource utilization. Faculty interviews highlighted reduced administrative burdens and enhanced flexibility in course delivery. However, challenges such as data security concerns and the need for technical support were noted. The study concludes that cloud-based e-learning systems are a valuable tool for modernizing higher education. Addressing implementation challenges and ensuring continuous technical support are critical for maximizing their potential. Future research should explore long-term impacts and integration with emerging technologies to further enhance learning experiences.
Mobile Application Design Based on Natural Language Processing to Improve the Quality of Health Services Ridwan, Achmad; Nizam, Zain; Satybaldy, Daniyar
Journal of Computer Science Advancements Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i1.1626

Abstract

The increasing demand for efficient and personalized health services has driven the integration of advanced technologies into healthcare systems. Mobile applications leveraging natural language processing (NLP) offer promising solutions to improve patient communication, diagnostic accuracy, and service delivery. Despite advancements, challenges remain in developing user-friendly applications that address diverse healthcare needs. This research focuses on designing a mobile application based on NLP to enhance the quality of health services, emphasizing usability, accuracy, and accessibility. The study employs a user-centered design approach combined with experimental evaluation. The application was developed using Python-based NLP libraries, integrating features such as symptom analysis, medical query responses, and appointment scheduling. A prototype was tested with 150 participants, including patients and healthcare professionals, to evaluate performance metrics such as response accuracy, user satisfaction, and system reliability. The findings indicate that the NLP-based application achieved an 85% accuracy rate in interpreting medical queries and a 90% user satisfaction rate. Participants reported improved communication with healthcare providers and faster access to relevant medical information. However, challenges such as handling complex medical terminology and ensuring data privacy were noted. The study concludes that NLP-powered mobile applications have significant potential to improve health service quality by enabling efficient and accurate communication between patients and providers. Addressing challenges related to data security and expanding linguistic capabilities will be essential for future development. The research underscores the importance of integrating advanced technologies to meet the evolving needs of the healthcare sector.
Interpretation of Deep Learning Models in Natural Language Processing for Misinformation Detection with the Explainable AI (XAI) Approach muhammadiah, mas'ud; Rahman, Rashid; Wei, Sun
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2104

Abstract

The increasing spread of misinformation through digital platforms has raised significant concerns about its societal impact, particularly in political, health, and social domains. Deep learning models in Natural Language Processing (NLP) have shown high performance in detecting misinformation, but their lack of interpretability remains a major challenge for trust, transparency, and accountability. As black-box models, they often fail to provide insights into how predictions are made, limiting their acceptance in sensitive real-world applications. This study investigates the integration of Explainable Artificial Intelligence (XAI) techniques to enhance the interpretability of deep learning models used in misinformation detection. The primary objective of this research is to evaluate how different XAI methods can be applied to explain and interpret the decisions of NLP-based misinformation classifiers. A comparative analysis was conducted using state-of-the-art deep learning models such as BERT and LSTM on benchmark datasets, including FakeNewsNet and LIAR. XAI methods including SHAP (SHapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), and attention visualization were applied to analyze model behavior and feature importance. The findings reveal that while deep learning models achieve high accuracy in misinformation detection, XAI methods significantly improve transparency by highlighting influential words and phrases contributing to model decisions. SHAP and LIME proved particularly effective in providing human-understandable explanations, aiding both developers and end-users. In conclusion, incorporating XAI into NLP-based misinformation detection frameworks enhances model interpretability without sacrificing performance, paving the way for more responsible and trustworthy AI deployment in combating online misinformation.
Introduction of LoRa Communication System and Remote Control System in Agricultural Automation With Internet of Things Prabowo, Yani; Riwurohi, Jan Everhard; Windihastuti, Wiwin; Hasan, Fuad
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2230

Abstract

This research focuses on the integration of LoRa (Long Range) communication system and remote control system in agricultural automation with Internet of Things (IoT) using ESP32 microcontroller, Arduino nano and STM32 aims to improve the efficiency of intelligent agricultural management. LoRa is used as a long-range wireless communication protocol to collect data from sensors that are widely distributed in agricultural land, such as soil moisture sensors, temperature. The ESP32 microcontroller functions as the main controller that processes data from sensors and sends it in real-time to the control center via the LoRa network. Modbus is used as a standard serial communication protocol to connect sensors, actuators and other devices, thus ensuring compatibility between devices. In addition, Node-RED is used as a graphical interface (GUI) to manage data flow, control automation processes, and provide real-time data visualization to users. The results of this research are a stable integration system between sensor systems and communication systems. The novelty of this research is the integration of LoRa, ESP32, Modbus, and Node-RED to create a reliable and efficient agricultural automation system, enabling remote management of irrigation, fertilization, and environmental monitoring, thereby increasing agricultural productivity and optimizing resource use.
The Application of Artificial Intelligence in Processing Health Data in Biomedical Information Prayudani, Santi; Lase, Yuyun Yusnida; Husna, Meryatul; Adam, Hikmah Adwin
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2245

Abstract

The increasing complexity and volume of health data in modern biomedical systems have necessitated advanced technologies for effective data processing and analysis. Traditional methods often fall short in managing real-time, multidimensional data generated from various biomedical sources, such as electronic health records (EHRs), wearable devices, and genomic data. This research investigates the application of artificial intelligence (AI) in optimizing the processing and interpretation of biomedical health data. The objective of this study is to explore how AI-based technologies, including machine learning and deep learning algorithms, enhance the efficiency, accuracy, and predictive capabilities in biomedical information systems. By identifying patterns, anomalies, and correlations in large datasets, AI offers potential improvements in disease diagnosis, patient monitoring, and treatment personalization. This research employs a qualitative systematic review method, analyzing peer-reviewed literature published between 2015 and 2024 from major databases such as PubMed, IEEE Xplore, and Scopus. The analysis focuses on case studies, comparative evaluations, and implementation outcomes of AI in various biomedical domains. The findings reveal that AI applications significantly improve data processing speed and accuracy, enable early diagnosis of diseases such as cancer and diabetes, and support predictive analytics for patient outcomes. However, challenges remain in areas such as data privacy, ethical compliance, and algorithm transparency. In conclusion, the integration of AI into biomedical data systems holds transformative potential for healthcare delivery, though further interdisciplinary collaboration is required to address its limitations and ensure equitable access and ethical use.
Utilization of Big Data in Improving the Efficiency of E-Business Systems in Indonesia Nugroho, Agung Yuliyanto; Prasetio, Rachmat; Wong, Lucas; Rao, Ananya
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2251

Abstract

The rapid growth of digital technology in Indonesia has fostered the expansion of e-business systems, which in turn has generated vast volumes of data. However, many e-business platforms still face challenges in utilizing this data effectively to improve operational efficiency and decision-making. This research was conducted to explore the utilization of big data in enhancing the efficiency of e-business systems in Indonesia. The main objective of the study is to analyze how the integration of big data analytics contributes to optimizing business processes, customer engagement, and overall system performance in the Indonesian digital commerce ecosystem. A mixed-method approach was employed, combining quantitative surveys of 120 e-business practitioners with qualitative interviews involving 15 data analysts and IT managers from various sectors such as retail, fintech, and logistics. Data were analyzed using statistical tools and thematic coding to derive patterns and insights. The findings indicate that e-businesses implementing big data strategies reported a significant improvement in system responsiveness, personalized customer services, and data-driven decision-making. Moreover, big data utilization has been linked to enhanced supply chain management and real-time monitoring capabilities. Despite these benefits, challenges such as data privacy concerns, lack of skilled personnel, and high infrastructure costs remain significant barriers. In conclusion, the study confirms that the effective use of big data plays a crucial role in improving the efficiency and competitiveness of e-business systems in Indonesia. Future initiatives should focus on strengthening data governance and investing in human capital to maximize big data’s potential.
Implementation of Cloud Computing in the Development of Distributed Computer Systems Saputra, Memed; Jonathan, Davy; Aribowo, Aribowo
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2253

Abstract

The rapid evolution of information technology has driven a significant shift from centralized to distributed computing architectures. One of the most transformative innovations in this domain is cloud computing, which offers scalable, flexible, and cost-effective solutions for managing large-scale distributed systems. This study investigates the implementation of cloud computing in the development of distributed computer systems, focusing on its impact on performance, resource utilization, and system scalability. The objective of this research is to analyze the effectiveness of cloud-based infrastructures in supporting distributed applications and to identify best practices for optimizing system architecture within a cloud environment. A mixed-method approach was employed, combining qualitative system analysis with quantitative performance metrics derived from cloud-deployed prototypes. Various case studies across different sectors—education, healthcare, and business—were used to illustrate real-world applications. The findings reveal that cloud computing significantly enhances the operational efficiency and adaptability of distributed systems. Key improvements include dynamic resource allocation, simplified maintenance, and increased fault tolerance. In conclusion, the integration of cloud computing into distributed systems presents a robust framework for modern computing needs. It not only reduces operational complexity but also facilitates innovation by enabling seamless scalability and rapid deployment. Future research is encouraged to explore hybrid cloud models and edge computing integration to further enhance distributed system performance in latency-sensitive environments.
IMPLEMENTATION OF COMPUTER VISION AND NATURAL LANGUAGE PROCESSING IN SOCIAL ROBOTS FOR MORE NATURAL AND INTUITIVE HUMAN-ROBOT INTERACTION Sungkar, Muchamad Sobri; Chirwa, James; Bagrationi, Giorgi
Journal of Computer Science Advancements Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i4.2348

Abstract

The rapid advancement of artificial intelligence (AI) has driven significant developments in social robotics, particularly in enabling more natural and intuitive human-robot interaction (HRI). However, current social robots often struggle to interpret multimodal human input effectively, leading to limited contextual understanding and reduced interaction quality. This study addresses these challenges by integrating computer vision (CV) and natural language processing (NLP) to enhance robots’ perceptual and communicative capabilities. The primary aim is to design and evaluate an interaction framework that allows social robots to recognize human emotions, gestures, and spoken language more accurately, thereby improving the fluency of HRI. A mixed-methods approach was employed, combining experimental implementation with qualitative user studies. The system architecture integrates real-time image recognition, gesture tracking, and speech understanding modules, which were tested through laboratory simulations involving 50 participants in controlled social scenarios. The results demonstrate that robots equipped with CV and NLP modules achieved a 30% improvement in gesture recognition accuracy, a 25% increase in contextual language understanding, and significantly higher user satisfaction scores compared to baseline models. Users reported that the robots exhibited more human-like responsiveness and adaptability in conversational settings. These findings suggest that combining computer vision and NLP substantially improves the naturalness and intuitiveness of human-robot interactions. This research highlights the importance of multimodal AI integration for the next generation of socially intelligent robots and paves the way for applications in healthcare, education, and service industries.

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