Data's exponential growth and cloud computing advancements have significantly impacted artificial intelligence (AI) model development. This study investigates how big data techniques integrated with cloud computing enhance the scalability and accuracy of AI models across sectors such as healthcare, business, and cybersecurity. Adopting a qualitative methodology, the research examines secondary data from 2020–2024, including case studies and literature. Key findings reveal that cloud computing enables large-scale data processing with significant efficiency, achieving average speeds of 20–45 seconds for datasets ranging from 50–120 TB/day. AI model accuracy also improved across sectors, increasing by 20% on average—reaching 92% in cybersecurity, 90% in healthcare, and 85% in business applications. The study identifies deep learning algorithms as pivotal for leveraging cloud computing's flexibility, allowing for advanced data analysis and real-time insights. However, challenges in data security and privacy remain critical concerns. This research contributes by highlighting the transformative role of cloud computing in big data management and AI optimization, offering practical insights into enhancing predictive capabilities while addressing operational cost efficiency through scalable infrastructure. The findings emphasize the necessity of robust security protocols to mitigate risks and ensure sustainable AI applications. Future research should explore sector-specific implementations to refine and expand the practical utility of these integrated technologies.

Integrating Big Data and Edge Computing is revolutionizing the efficiency of artificial intelligence (AI) systems, particularly in applications requiring real-time responses. This study explores the synergistic role of these technologies in two critical sectors: autonomous vehicles and healthcare. Using a case study approach, real-world datasets and simulation platforms were employed to evaluate improvements in latency, prediction accuracy, and system efficiency. Key findings reveal that Edge Computing reduces latency by 30%, with response times dropping from 150 ms to 105 ms in autonomous vehicles and from 200 ms to 140 ms in healthcare applications. Additionally, leveraging Big Data for AI training enhanced prediction accuracy by 15% for traffic pattern recognition and 12% for patient condition monitoring. Despite these advancements, challenges such as scalability, data security, and interoperability persist, necessitating robust infrastructure and end-to-end encryption solutions. This research highlights the transformative potential of combining Big Data and Edge Computing to optimize AI systems for real-time applications, offering insights into improving operational efficiency and predictive accuracy. The findings are expected to guide future developments in AI technologies, particularly in the context of expanding 5G networks and growing demand for real-time data processing.
 

In the era of Industry 4.0, integrating artificial intelligence (AI) and big data analytics in the industrial sector demands high-performance computing infrastructure to handle increasingly complex and voluminous datasets. This study investigates the optimization of AI performance by implementing a hybrid computing architecture, integrating CPUs, GPUs, FPGAs, and edge-cloud computing. The research aims to enhance processing speed, model accuracy, and energy efficiency, addressing the limitations of standalone computing systems. A quantitative methodology was employed, using over 1 TB of industrial data from IoT sensors and production logs. A hybrid architecture was implemented with dynamic workload scheduling to distribute tasks efficiently across computational components. Performance metrics included processing time, model accuracy, energy consumption, and cost analysis. Results demonstrated that hybrid architectures significantly improved performance: the CPU-GPU combination reduced processing times to 650 ms, increased model accuracy to 88.3%, and achieved an energy consumption of 2.1 kWh. Meanwhile, the CPU-FPGA configuration, while slightly less accurate (87.5%), proved more energy-efficient at 1.3 kWh. AI models developed using hybrid systems exhibited superior predictive accuracy, with Mean Squared Error (MSE) as low as 0.0248 and R² of 0.91. The study concludes that hybrid computing architecture is a transformative approach for optimizing AI systems in industrial applications, balancing speed, accuracy, and energy efficiency. These findings provide actionable insights for industries aiming to leverage advanced computing technologies for improved operational efficiency and sustainability. Future research should focus on advanced workload scheduling and cost-effectiveness strategies to maximize the potential of hybrid systems.

The rapid growth of digital services has intensified the energy demands of data centers, significantly impacting operational costs and global carbon footprints. This study leverages Machine Learning (ML) and Big Data to optimize energy management in data centers, addressing limitations in prior approaches that overlooked real-time variability. A predictive model utilizing the Random Forest algorithm was developed to reduce energy consumption based on dynamic factors, such as workload and environmental conditions like temperature. The research used a six-month dataset consisting of approximately 3 million data points from an operational data center. After preprocessing the data, the model achieved a high predictive accuracy, reflected by an R-squared value of 0.87. The findings demonstrate that the model reduces energy consumption by an average of 11.17% daily, with peak savings of up to 15.56% during off-peak hours. Key metrics, including a Mean Squared Error (MSE) of 0.034 and a Root Mean Squared Error (RMSE) of 0.184, validated the model's effectiveness and reliability. Statistical tests further confirmed its precision within a 95% confidence interval. This study contributes to the academic field by integrating real-time environmental data into predictive modeling, offering a scalable solution for energy-efficient data center operations. The outcomes support sustainability initiatives by mitigating carbon emissions and reducing operational costs. The findings also provide a framework for applying ML in broader industrial contexts requiring efficient energy management. Future research may explore incorporating additional variables, such as user behavior, to further refine predictive capabilities.

The purpose of this study is to determine the extent of content marketing using storytelling. This study uses a qualitative research type with a netnography approach. with the creation of effective content marketing and storytelling to increase sales and even create awareness of the products being sold. Social media is the main media for implementing content marketing and storytelling strategies because of its speed in disseminating information. Marketing communication with social media can reach a wider target market. The use of content marketing and storytelling can increase sales as seen from the development of stores and the addition of branches.

This article aims to describe the influence of social media on the formation and spread of slang among students at Muhammadiyah University, Maumere. Social media, such as Instagram, TikTok, and Twitter, are not only a means of interaction, but also a major catalyst in creating and disseminating new terms. This phenomenon reflects students’ linguistic creativity and is also a reflection of the dynamics of digital culture. In the context of Maumere Muhammadiyah University students, the use of slang is not just a trend, but also a tool to adapt to a dynamic social environment. New terms that are popular on social media are often adopted in daily conversations as symbols of modern and contemporary identity. However, this phenomenon also raises challenges, especially related to the preservation of formal language and traditional values in communication. This article discusses the characteristics of slang that emerges from social media, its role in forming social identity, and its impact on the use of formal language. In conclusion, social media plays a significant role in shaping student communication patterns, but efforts are needed to maintain a balance between language creativity and the use of standard language in formal contexts.

This research aims to examine how feminist values ​​are represented in the short story Kunang-Kunang in Bir by Djenar Maesa Ayu. Using descriptive qualitative research methods.  Feminist literary criticism allows for in-depth analysis of themes, characters, and symbols related to issues of gender and inequality. In the analysis of this short story, gender inequality, social norms and symbolism were found. The results of this research show women's struggle in facing patriarchal norms and injustice that prevent women from achieving happiness. The symbolism used increases public criticism of ongoing gender inequality. It is hoped that this research can contribute to feminist literary studies and encourage awareness of the importance of women's freedom in making life decisions.

The Ministry of Health released screening data on the depression levels of doctors in the Specialist Doctor Education Program (PPDS) at Vertical Teaching Hospitals, showing that 22.4% of PPDS doctors experience depression and up to 0.3% have suicidal thoughts. This study aims to analyze the influence of the work environment on depression levels among PPDS doctors in Indonesia. Depression is one of the mental health issues often faced by PPDS doctors due to high workloads, academic pressure, and lack of social support. Using a normative juridical research method, this study examines the regulations and policies governing the work environment and their impact on the mental health of PPDS doctors. The results indicate that an unconducive work environment significantly contributes to increased depression levels among PPDS doctors. Efforts to improve the work environment through more responsive policies and adequate mental health support are necessary to reduce depression levels

The purpose of this research is to see how the Literacy Warkop location influences student buyer interest at Sultan Ageng Tirtayasa University (Untirta). Factors such as comfort, accessibility and atmosphere were studied through a survey conducted by twenty students. In this current era of globalization, a coffee shop is not only a place to enjoy coffee, but also a place to interact socially and intellectually. It is hoped that the location of the literacy shop near the library or academic building will increase student accessibility and comfort. The research results show that students' buying interest is greatly influenced by the right purchase location. The buyer's desire to buy something which is influenced by internal and external factors is called buying interest. This research helps business people determine location placement methods to attract student attention. Therefore, location maintenance is very important for the success of literacy warkop in attracting student customers.

This research explores the digital transformation in Islamic philanthropy, particularly through the optimization of zakat and waqf management using Islamic fintech. The digital economy and fintech present significant opportunities to enhance the efficiency and transparency in collecting and distributing Islamic philanthropic funds. By leveraging modern financial technology, zakat and waqf can be managed more effectively, ensuring precise and accountable distribution. The study also emphasizes the importance of Islamic financial literacy in supporting public participation in the digital economy. The findings show that integrating fintech in zakat and waqf management not only improves transparency and efficiency but also strengthens public trust in the Islamic financial system. Therefore, this research suggests further development of Islamic fintech technologies and enhanced educational programs to support the growth of Islamic philanthropy in the digital era. The research further emphasizes the necessity for comprehensive financial education programs and easily accessible information to enhance Islamic financial literacy among the broader public. Collaborative efforts between the government, financial institutions, and educational bodies are crucial to developing and promoting fintech solutions that comply with Sharia principles. Integrating technology into Islamic philanthropy not only optimizes fund management but also contributes to broader social and economic development goals by supporting more inclusive and sustainable growth. Consequently, Islamic fintech holds immense potential to revolutionize the management of zakat and waqf, fostering a more resilient and trusted financial ecosystem within the Muslim world.