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This journal presents the development of the Material Tracking Application at PT. Pindad. During the assembly process, some components may be rejected due to defects or non-compliance with other components from different suppliers. It is difficult to identify the origin of rejected components due to the lack of rejection data from each supplier. This issue led to the development of applications that can trace the origin of components by attaching QR codes to packages. Scanning the QR code activates the Quality Function to identify the origin of the component. QR codes were chosen because of their cost-effectiveness, low operational costs, and ease of reading compared to other Automatic Identification Procedure (Auto-ID) technologies. Another problem is the administrative process when moving parts/components from the component warehouse to the production warehouse. The recording process still uses paper-based documentation which is time-consuming and error-prone. Therefore, QR codes are also used to simplify and speed up the recording process by the security function. From designing web-based applications which are then deployed and run on Google's cloud services. The researcher then carried out a testing process with 2 stages, namely the process of verification and validation. It was found that the Material Tracking Application went well according to the researchers' expectations. There are still many opportunities for the development of this Material Tracking Application. Mainly development using other Auto-ID and different case studies in the world of manufacturing.

To ensure efficient operations and cost-effectiveness, resource management in cloud computing entails managing cloud resources to satisfy application needs, financial restrictions, and security. In this regard, utilizing data analytics tools for the allocation of resources in cloud computing to efficiently predict, track, allocate, and monitor resources enables businesses to make informed decisions based on real-time data, which plays a crucial role in resource allocation. Organizations adopting cloud computing services face increased network traffic, limiting traffic routing flexibility and causing excess traffic to reach unprepared physical nodes due to an inability to adjust to real-time traffic changes. This paper uses a systematic literature review to investigate the data analytics techniques used for resource allocation in cloud computing. It uses data from 2019 to 2024, sourced from different research databases. The results show that the majority of data analytics tools, including ARIMA and SVM, are employed for resource allocation in cloud computing. This study offers guidance to organizations regarding data analytics tools for the allocation of resources in cloud computing, and the recommendations can be utilized for the enhancement of the results in cloud computing, as well as to scholars by suggesting techniques to further investigate resource allocation to address the current gaps in cloud computing

The shift to the cloud enables organizations of all sizes to swiftly, efficiently, and innovatively move their operations. The adoption of cloud computing has significantly transformed most organizations' work, communication, and collaboration methods, making it a crucial necessity for maintaining competitiveness in the digital age. Organizations are implementing cloud bursting to handle IT demand peaks by utilizing private cloud capacity and public cloud capacity, freeing up local resources for critical applications, and reverting data back to the private cloud. Organizations face challenges in allocating resources in cloud computing to automatically switch from private cloud to public cloud, leading to system issues, user frustration, operational failure, increased stress, and revenue loss. To address these concerns. This paper investigates traffic predictions by comparing three prediction tools, such as support vector machines, spatio-temporal, and edge-cloud collaborative schemes, and proposing conceptual solutions. Efficient cloud computing traffic management can prevent system bottlenecks, especially during peak periods, potentially leading to dissatisfied clients.