This study aims to analyze the effect of feed supplements on the health and growth of beef cattle. The method used is a literature review, examining various relevant sources related to livestock feed and supplements. The results show that the addition of protein in feed increases the average daily gain (ADG) of cattle by 1.0-1.2 kg/day compared to cattle without supplements (0.7 kg/day). Feed supplements also proved to improve feed conversion efficiency by up to 20-25% and reduce feed costs when combined with local feed. Additionally, the provision of minerals and vitamins such as vitamin E and selenium contributes to improving cattle immunity and reproductive health. The implementation of good livestock health management, including routine administration of vitamins and deworming, is key to enhancing the productivity and health of beef cattle.

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.

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.

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.
 

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.

This study aims to develop an interactive puppet learning media to enhance the interest and understanding of fourth-grade students in the Javanese language. The method used is Research and Development (R&D), which includes needs analysis, design, development, and media trials. Observational results show that students struggle with understanding Javanese vocabulary and etiquette, which affects their comprehension. The interactive puppet media is expected to serve as a solution to capture students' attention and enrich their learning experience. This media presents content visually and engagingly, facilitating the understanding of difficult concepts, and offering a more interactive learning experience. Validation from content, language, and media experts indicates that this media is suitable for use in Javanese language learning. This study is expected to improve the quality of Javanese language education in elementary schools, particularly in fourth-grade classes, and encourage students to further explore Javanese culture.

Indonesian continues to evolve along with social, cultural, and technological dynamics, producing new vocabulary that requires systematic documentation. This research aims to identify the role of intern students at the Balai Bahasa Sumatra Utara in proposing entries for the 6th edition of the online Great Dictionary of the Indonesian Language (KBBI). Using a descriptive qualitative approach, data were collected through interviews, observations, and document analysis. The research results show that interns made significant contributions to the lemma proposal process, which included data collection, linguistic analysis, and recommendation drafting. A total of 200 entries from the fields of physics, law, chemistry, and psychology were successfully proposed. The main findings include (1) a collaborative process between students, experts, and KBBI editors that supports the validity of lemmas; (2) contributions to the documentation of new scientific terms; and (3) challenges, such as limited reference sources and understanding of usage contexts. This research shows that the internship program not only contributes to the development of the Indonesian language but also provides practical learning experiences for students. Recommendations are provided for strengthening the internship system and optimizing the role of students in the management of the national language.

This study discusses the design of a goods storage information system at the Medan Class A Search and Rescue Office to improve the efficiency and accuracy of goods management management. This system is designed to automate the process of storing and tracking goods, which has been done manually and often causes various problems such as tracking difficulties and data inaccuracies. The results of the implementation show that this information system is able to minimize errors, reduce the risk of loss of goods, and ensure the availability of important equipment in real-time. Despite challenges such as user resistance and resource requirements, careful planning and proper training overcame these obstacles. In conclusion, this system contributes significantly in improving the operational performance of the Medan Class A Search and Rescue Office.

Scam threats on digital platforms continue to rise alongside the rapid adoption of technology in Indonesia. The unique characteristics of Indonesian digital users, such as low digital literacy and high social media usage, make them particularly vulnerable to various forms of scams, including phishing, impersonation, and emotional manipulation. This study aims to develop a machine learning-based model for predicting and detecting scams by identifying threat patterns within a local context. The methodology involves collecting a survey-based dataset from Indonesian digital users, capturing language patterns and user interaction behaviors. The dataset was processed through text-cleaning techniques, tokenization, normalization, and representation using TF-IDF and Word Embeddings. The machine learning models employed in this study are Random Forest and Support Vector Machine (SVM), evaluated using accuracy, precision, recall, and F1-score metrics. Hyperparameter tuning was conducted to optimize model performance, while k-fold cross-validation was utilized to minimize the risk of overfitting. The results indicate that the Random Forest model achieved the best performance, with an accuracy of 92.5%, precision of 90.7%, recall of 94.1%, and F1-score of 92.4%. The use of local datasets improved detection accuracy by 7.8% compared to global datasets, highlighting the critical importance of contextual representation in identifying scam patterns specific to Indonesia. The model was also effective in recognizing unique threat patterns, such as the use of informal language and manipulative phrases in scam messages. This study makes a significant contribution to the field of digital security by providing an effective machine learning-based approach to detecting scam threats in Indonesia. Moreover, the findings underscore the importance of developing local datasets and educating users as part of a holistic solution to enhance digital security. These insights emphasize the necessity of incorporating cultural and contextual factors into technology-driven approaches for combating scams in developing countries like Indonesia

The rapid evolution of Industry 4.0 has positioned Internet of Things (IoT) technologies as key enablers for smarter industrial operations, particularly in predictive maintenance and machine monitoring. This research proposes an innovative IoT-driven vibration monitoring system that addresses limitations in traditional approaches such as high costs, limited scalability, and insufficient real-time capabilities. Employing low-cost sensors, edge computing, and LoRaWAN-based communication, the framework enables efficient fault detection and operational analysis. Data from industrial machinery was collected over two months and analyzed using advanced signal processing and machine learning techniques to extract meaningful insights. The system demonstrated an accuracy rate of 92%, a detection latency of 150 milliseconds, and extended sensor life to 12 months, marking significant improvements over conventional methods. Furthermore, scalability tests showed stable performance across setups involving up to 500 sensors, even in challenging industrial conditions. This study also highlights cost reductions of 30% and a 25% decline in machine downtime, reinforcing its practical value for industrial applications. By delivering an adaptable, energy-efficient, and secure solution, this research advances the integration of IoT into industrial systems. It lays the groundwork for future enhancements, including real-world testing and multimodal data integration