cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Adam Mudinillah
Contact Email
adammudinillah@staialhikmahpariangan.ac.id
Phone
+6285379388533
Journal Mail Official
adammudinillah@staialhikmahpariangan.ac.id
Editorial Address
Jorong Kubang Kaciak Dusun Kubang Kaciak, Kelurahan Balai Tangah, Kecamatan Lintau Buo Utara, Kabupaten Tanah Datar, Provinsi Sumatera Barat, Kodepos 27293.
Location
Kab. tanah datar,
Sumatera barat
INDONESIA
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 5 Documents
 1 
Search results for , issue "Vol. 3 No. 1 (2025)" : 5 Documents clear
Big Data Analysis to Predict Consumption Patterns in Smart Cities Susilo, Anto; Prasetiyo, Rachmat; Aslam, Bilal; Farah, Rina
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.v3i1.1535

Abstract

The rapid development of smart cities has increased the demand for efficient resource management and personalized services, where understanding consumption patterns is crucial. Big data analysis offers a powerful tool for predicting these patterns, enabling city planners and service providers to make data-driven decisions to enhance urban living quality. This study aims to utilize big data analytics to predict consumption patterns across various sectors in smart cities, including energy, water, and transportation. By leveraging large datasets, this research seeks to provide actionable insights for optimizing resource allocation and anticipating future consumption demands. The methodology involves collecting and analyzing data from multiple sources, such as IoT sensors, public utility records, and social media, to identify consumption trends. Machine learning algorithms, including time series analysis and clustering, were applied to detect patterns and forecast demand. Results indicate that big data analytics can accurately predict consumption fluctuations, with an 85% accuracy in energy demand forecasting and a 78% accuracy in water usage prediction. The findings highlight correlations between demographic factors and consumption, providing a comprehensive understanding of urban needs. The study concludes that big data analysis is a valuable approach to managing resources effectively in smart cities. By predicting consumption patterns, city planners can proactively address demand surges, reduce waste, and improve resource distribution, ultimately supporting sustainable urban growth. Implementing these insights could significantly enhance smart city efficiency and resilience.
Sentiment Analysis on Social Media Using Data Mining for Mapping Community Satisfaction Usup, Usup; Sahirin, Rohmat; Lucas, Laura; Qingjun, Chu
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.v3i1.1536

Abstract

Social media has become a significant platform for individuals to express opinions, including satisfaction and dissatisfaction with services and policies, making it a valuable source of community sentiment data. Understanding public sentiment can assist policymakers and organizations in responding to community needs effectively. This study aims to conduct sentiment analysis on social media using data mining techniques to map community satisfaction levels. By analyzing sentiment patterns, this research seeks to provide actionable insights for improving public services and enhancing community engagement. The research applies data mining methodologies, including text mining and machine learning algorithms, to analyze posts and comments collected from various social media platforms. Sentiment classification was performed using natural language processing (NLP) and a supervised machine learning approach to categorize sentiments as positive, neutral, or negative. The model was trained on a large dataset and validated to ensure accuracy in sentiment detection. Results indicate that social media sentiment analysis can reliably reflect community satisfaction trends, with findings showing 70% positive, 15% neutral, and 15% negative sentiments regarding local services. The study concludes that data mining for sentiment analysis provides a robust method for assessing community satisfaction on social media, offering a real-time understanding of public opinion. By implementing this approach, organizations and policymakers can identify areas of improvement and proactively address community concerns, ultimately fostering a responsive and community-centered approach to public service.  
Use of Artificial Intelligence in Predicting Electricity Needs in Smart Cities Fawait, Aldi Bastiatul; Li, Zhang; Hussain, Sara
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.v3i1.1620

Abstract

The rapid urbanization and adoption of smart city technologies have led to increasing complexities in managing electricity demand. Traditional methods of forecasting electricity needs often fail to accommodate the dynamic and real-time nature of energy consumption in smart cities. Artificial Intelligence (AI) offers a promising approach by leveraging machine learning algorithms and predictive analytics to address these challenges. This study explores the use of AI in predicting electricity needs, focusing on its applicability in optimizing energy distribution and reducing inefficiencies in smart city infrastructures. The research aims to develop an AI-based predictive model to forecast electricity demand using historical and real-time data. The methodology involves data collection from smart meters, weather forecasts, and demographic records, followed by training machine learning algorithms such as Random Forest, Support Vector Machines, and Neural Networks. Performance metrics, including prediction accuracy, computational efficiency, and scalability, were analyzed to evaluate the model's effectiveness. Results indicate that AI-based models outperform traditional forecasting methods, achieving an average prediction accuracy of 92%. Neural Networks demonstrated the highest performance, particularly in handling complex and nonlinear data patterns. The AI model also showcased scalability by adapting to increasing datasets without significant degradation in performance. The study concludes that AI is a transformative tool for predicting electricity needs in smart cities. By enhancing forecast accuracy and enabling efficient energy distribution, AI contributes to sustainable urban development and smarter energy management systems.
Application of Augmented Reality in E-Commerce to Increase Product Sales Susanto, Ruhiat; Rizalfi, Vinto; Sok, Vanna; Khan, Omar
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.v3i1.1622

Abstract

The rapid development of e-commerce has led businesses to adopt innovative technologies to enhance customer engagement and increase product sales. Augmented reality (AR) has emerged as a transformative tool that allows consumers to visualize products in real-world settings, bridging the gap between physical and digital shopping experiences. This research explores the application of AR in e-commerce and its effectiveness in driving product sales by enhancing customer interaction and confidence in purchasing decisions. The study employs a mixed-method approach, combining quantitative surveys of 300 e-commerce users with qualitative interviews of 10 industry experts. The survey measured user engagement, purchasing intent, and satisfaction with AR-enhanced shopping experiences. Expert interviews provided insights into AR implementation strategies and its impact on sales performance. The findings indicate that AR significantly improves product visualization, leading to a 25% increase in customer engagement and a 30% boost in sales conversions. Consumers reported higher confidence in their purchases when using AR features, citing improved understanding of product dimensions, colors, and functionality. However, challenges such as high implementation costs and technical complexity were identified as barriers to widespread adoption. The study concludes that AR is a valuable tool for e-commerce businesses aiming to increase sales by enhancing the customer experience. Addressing challenges such as cost and accessibility will be critical for maximizing AR’s potential. Future research should explore the integration of AR with other emerging technologies to further optimize its impact on e-commerce performance.
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.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 6 (2025) Vol. 3 No. 5 (2025) Vol. 3 No. 4 (2025) Vol. 3 No. 3 (2025) Vol. 3 No. 2 (2025) Vol. 3 No. 1 (2025) Vol. 2 No. 6 (2024) Vol. 2 No. 5 (2024) Vol. 2 No. 4 (2024) Vol. 2 No. 3 (2024) Vol. 2 No. 2 (2024) Vol. 2 No. 1 (2024) Vol. 1 No. 6 (2023) Vol. 1 No. 5 (2023) Vol. 1 No. 4 (2023) Vol. 1 No. 3 (2023) Vol. 1 No. 2 (2023) Vol. 1 No. 1 (2023) More Issue