The increasing use of social media has significantly impacted wireless network performance, particularly in data transmission efficiency. This study aims to analyze the impact of social media usage on network congestion and wireless data transmission efficiency. Experimental and quantitative methods were employed, utilizing direct network traffic measurement and simulation techniques. The study assessed key parameters, including throughput, latency, packet loss, and jitter, to evaluate network congestion levels caused by social media activities such as video streaming, image uploads, and real-time communication. The results indicate a 15–30% decrease in throughput, an increase in latency from 30 ms to 120 ms, a packet loss surge from 0.5% to 4–7%, and a jitter increase from 10 ms to 50 ms during peak hours of social media usage. The study also found that channel bonding techniques can improve throughput by up to 63.6% under controlled interference conditions. These findings highlight the necessity of effective traffic management strategies, including 5G network deployment, machine learning-based traffic optimization, and Quality of Service (QoS) implementation, to mitigate network congestion issues caused by the exponential growth of social media users.

In the rapidly evolving era of big data, predictive analytics has become a crucial approach in supporting data-driven decision-making across various sectors such as finance, healthcare, and marketing. However, the effectiveness of predictive models is highly dependent on the quality of features utilized in model training. This study aims to evaluate and compare various feature engineering techniques to enhance the accuracy of predictive models based on Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms. The research employs a quantitative experimental approach by applying different feature engineering techniques, including SHAP-based feature importance, Principal Component Analysis (PCA), and categorical variable encoding. The evaluation results indicate that the implementation of SHAP-based feature importance yields the best outcomes, with a Mean Squared Error (MSE) of 0.150 and a Root Mean Squared Error (RMSE) of 0.387 in the XGBoost model. These values outperform those without feature engineering, which recorded an MSE of 0.230 and an RMSE of 0.479. The combination of PCA and encoding techniques also shows a significant performance improvement with an MSE of 0.160 and an RMSE of 0.400. The XGBoost algorithm consistently demonstrates superior performance compared to RF across various testing scenarios. The contribution of this study lies in its recommendation of appropriate feature engineering techniques to improve the predictive quality of Machine Learning (ML)  models. This research provides insights for researchers and practitioners in developing more effective feature engineering strategies and opens opportunities for exploring advanced techniques in more complex data domains.

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 social media platform TikTok has grown rapidly as one of the main platforms for creative expression and information exchange. This article discusses the use of video editing tools provided by TikTok in creating interesting learning materials for students. We analyze TikTok's creative features including visual effects, music, and quick edits, and illustrate how to apply them to learning. This research involves a qualitative approach through case studies, focusing on the teacher's experience in creating learning materials with the TikTok video editing tool. The findings show that this approach can motivate students by providing visual content that matches the preferences of the younger generation. Students' positive response to this approach indicates increased engagement and interest in learning. However, challenges in utilizing TikTok's video editing tools are also acknowledged. Integrating creativity with substantial content and ensuring the integrity of education is maintained are some of the aspects that must be considered. Ethical and privacy implications are also discussed in the context of the use of social media platforms in education. In conclusion, TikTok's video editing tools offer exciting potential for creating impactful learning materials. With a thoughtful approach, educators can design content that fits current visual trends and communication styles, while maintaining the required educational quality.

The aim of this research is to examine the role of social media marketing in creating a significant relationship between digital marketing capabilities and marketing performance in construction companies on the island of Java. The research method was carried out quantitatively by collecting data by distributing questionnaires via electronic forms and printed formats which were delivered directly to respondents where the respondents were directors who worked for contractor companies in Central Java province. The sampling technique was carried out purposively based on the criteria of directors who had worked for a minimum of five years at contractor companies in Central Java province. Based on these criteria, there are 225 company directors who have met these criteria. The data was analyzed using AMOS 22 statistical software. The research results showed that digital marketing capabilities had a positive and significant effect on social media marketing. Digital marketing capabilities do not have a positive and significant effect on marketing performance, and social media marketing has a positive and significant effect on marketing performance.

Peer-to-peer systems nowadays are widely used because of the scalability and high reliability. File replication and consistency maintenance are widely used techniques to achieve high system performance. These techniques are connected to each other. The connection of these techniques is consistency maintenance is needed in file replication to keep the consistency between a file and the replicas. Traditional file replication and consistency maintenance methods need a high cost. The usage of IRM (Integrated file Replication and Consistency Maintenance inP2P systems) which will achieve high efficiency at a significantly lower cost can be used to solve this problem. IRM reduces redundant file replicas, consistency maintenance overhead, and unnecessary file updates.

Peer-to-peer systems nowadays are widely used because of the scalability and high reliability. File replication and consistency maintenance are widely used techniques to achieve high system performance. These techniques are connected to each other. The connection of these techniques is consistency maintenance is needed in file replication to keep the consistency between a file and the replicas. Traditional file replication and consistency maintenance methods need a high cost. The usage of IRM (Integrated file Replication and Consistency Maintenance inP2P systems) which will achieve high efficiency at a significantly lower cost can be used to solve this problem. IRM reduces redundant file replicas, consistency maintenance overhead, and unnecessary file updates.

SMA YATPI Godong merupakan perwujudan dalam membentuk, mencerdaskan generasi muda Bangsa Indonesia. Semakin bertambahnya jumlah siswa setiap tahun, menuntut ketelitian dan ketelitian dalam memberikan informasi yang akurat dan tepat kepada siswa tanpa mengulang data yang sama atau informasi, serta tentang transparansi data keduanya nilai, kehadiran dan biaya kuliah harus dapat diakses kapan saja baik oleh siswa maupun orang tua wali. SMA YTPI Godong sangat membutuhkan a sistem yang dapat mendukung proses penyajian informasi akademik, saat ini sistem akademik diimplementasikan dengan menggunakan metode konvensional, data hanya disimpan di komputer dan tidak dapat diakses oleh seluruh civitas akademika dan orang tua. Untuk menghadapi ini masalah, sistem Informasi Akademik Berbasis Web dibutuhkan di SMA YTPI Godong sebagai amemberikan solusi yang akurat, cepat dan tepat informasi. Sistem informasi akademik ditawarkan dapat mengelola informasi akademik, baik data kehadiran siswa, absensi mengajar dan absen mengajar guru, mengelola data nilai, jadwal pelajaran, jadwal mengajar guru, dan bisa mempublikasikan informasi yang berkaitan dengan akademik. Itu pengembangan informasi akademik berbasis web ini sistem menggunakan pengembangan aplikasi cepat (RAD) metode, model pengembangan ini digunakan karena ini model dianggap model dengan mengutamakan waktu, jadi prosesnya relatif singkat sehingga dengan pengembangan sistem informasi ini dapat dilakukan bermanfaat dalam memberikan kemudahan baik bagi siswa maupun guru.

In the current era, learning adheres to the 2013 curriculum which includes HOTS (Higher Order Thinking Skills) consisting of critical thinking competencies, creativity and innovation, communication skills, collaboration skills and selfconfidence. These five things need to be trained or improved for deaf children so that they can take part in learning well. So learning media products are made that can be used to recognize letters and numbers. This Learning Media is equipped with sign language to support the understanding of deaf children in addition, it is packaged with an attractive appearance so that deaf children are interested in learning it. Often in daily teaching and learning activities students still often experience problems in remembering the material being taught both in the
introduction of letters and also the introduction of numbers and what becomes an obstacle for students learning must be done repeatedly so that they can
remember it and can really understand what it means. being taught and also they are sometimes bored and lazy in learning. From the problems that occur above,
it is necessary to have a learning media that can help children with disabilities, especially the deaf, especially for the introduction of numbers and letters
so that they can easily understand and understand letters and numbers. not bored, bored and lazy in learning. Learning media in the form of an android
application so that it can be used on smartphones which are currently very widely used by both teachers and parents of students with disabilities.

By looking at current technological developments that already use automatic sensors, the application of the concept to this system can be a solution for dealing with ornamental fish farming owners. In this research, a tool is made that can perform feeding, checking temperature, water turbidity, feed level, water level and water pump control, from this information will be sent to android by firebase automatically monitored and controlled anywhere via android. This tool is designed with ease of maintenance and is an extension of the existing tools. IoT technology is used to monitor online, based on Firebase and Nodemcu Esp8266. In this study, researchers used the research method of the Borg and Gall (1987) model from 10 steps to 6 steps. The results of this study were carried out by an expert validation test to get a value of 30 which means "Very Good (Valid)". While the effectiveness test by 10 users plus by experts and employee users get a total score of 35.16 which means the effectiveness test is classified as "Very Good (Valid)" it can be said that the new system is more effective than the old system.