Journal of Computer Science Advancements

The Application of Artificial Intelligence in Processing Health Data in Biomedical Information

Santi Prayudani — 2025-05-08

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.

Interpretation of Deep Learning Models in Natural Language Processing for Misinformation Detection with the Explainable AI (XAI) Approach

mas'ud muhammadiah — 2025-05-08

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.

Utilization of Big Data in Improving the Efficiency of E-Business Systems in Indonesia

Agung Yuliyanto Nugroho — 2025-05-08

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.

Introduction of LoRa Communication System and Remote Control System in Agricultural Automation With Internet of Things

Yani Prabowo — 2025-05-12

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.

Implementation of Cloud Computing in the Development of Distributed Computer Systems

Memed Saputra — 2025-05-08

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.