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This research is motivated by the problems in manual attendance systems at schools, which remain vulnerable to fraud, time-consuming, and inefficient. The expected solution is to develop an automated attendance system based on face recognition that can operate in realtime with high accuracy. The research object is vocational high school students, with the applied method implementing the YOLO v10 algorithm for face detection, followed by the face_recognition library for identification. The instruments used include an Imou CCTV camera as the input device, a mid-range laptop as the hardware platform, and Python with SQLite as the software environment for data processing and attendance storage. The results show that the developed system achieved an average face detection accuracy of 96% under normal lighting and 91% under low lighting, with an average processing speed of 27 FPS. The implementation of an anti-duplication feature also ensured data validity by allowing each student to be recorded only once per day. In conclusion, the use of YOLO v10 in face-based attendance proved to be effective, efficient, and capable of reducing fraud. The implication of this study is that the system can be applied in both Islamic boarding schools and general schools as a modernization of attendance systems, with a recommendation for further development through web-based application and cloud database integration.

This study aims to design and develop an Augmented Reality (AR)-based application for real-time data monitoring of an automatic weather station at PT. XYZ. The system was developed using a prototyping method, involving five key stages: sensor data acquisition via CBOX devices and the Modbus RTU protocol, data transmission through a Flask-based Web API, storage in a MySQL database, QR code-based target image creation using the Vuforia Engine, and a 3D visualization interface developed with Unity integrated with the Vuforia SDK on Android devices. The system successfully received and stored weather data—such as temperature, humidity, and wind speed—into the MySQL database. The AR application also displayed a stable 3D interface panel over QR code markers, providing real-time data updates through an HTTP-based mechanism. This research demonstrates the successful integration of SCADA, Flask, MySQL, and Unity, enhancing both the functionality and commercial appeal of PT. XYZ’s weather station products. Future research should focus on field testing, cloud network integration, and device compatibility.

The SIAP Application is an innovation developed as a solution to support the archiving business process in the Planning Bureau. Currently, most employees still use computers and a web-based cloud system developed by Pusdatin using the PHP programming language as a storage medium for files that are no longer actively used and are categorized as dynamic archives. However, the cloud system does not have a well-structured archive management system, which often causes difficulties when employees need to retrieve archived files. As a result, employees must search for files manually by checking archived documents one by one in the cloud. This process is time-consuming and may lead to problems such as difficulties in finding files and the risk of data loss. Based on these conditions, the SIAP Application was developed to improve the effectiveness and efficiency of archive management in the Planning Bureau.The SIAP Application has an advantage in the archiving process, where employees can easily search for archives using the letter number or subject that has been entered into the system. The system will automatically display the required archive, making the retrieval process faster, more accurate, and more organized compared to the previous cloud system.The development of the SIAP Application uses PHP and HTML as the programming languages, with Bootstrap and Tailwind CSS as the user interface frameworks, and MySQL as the database. The system development method used is the Waterfall model, problem analysis is carried out using the PIECES method, and system testing is conducted through black-box testing and the System Usability Scale (SUS) to measure system usability.

While TLS 1.3 is the latest standard, TLS 1.2 remains widely implemented in many cloud infrastructures. The selection of cipher suites in TLS 1.2, particularly between AES-128-GCM and AES-256-GCM, presents a trade-off between cryptographic strength and system performance. This research aims to analyze the performance comparison of these two algorithms on an Nginx server to determine the optimal configuration for cloud storage services. The study uses a quantitative experimental method by benchmarking two scenarios: (A) Strict (AES-256-GCM), and (B) Balanced (AES-128-GCM). Performance metrics measured include Requests Per Second (RPS), Latency, and Throughput. The results show that handshake performance (RPS and Latency) is nearly identical across all scenarios. However, in large file transfer tests, the AES-128-GCM algorithm (Scenario B) achieved a throughput of 32.4 MB/s, which is 12.5% faster than AES-256-GCM (28.8 MB/s). This study concludes that AES-128-GCM provides the best balance of security and efficiency for data-intensive environments.

This study was motivated by the need for madrasahs to adapt to technological developments, particularly in document management, which was previously done manually. The purpose of this study was to describe the role of administration in digital archive management at MTsN 3 Jember, identify the obstacles that arose, and explain the efforts made to overcome these obstacles. The research used a descriptive qualitative approach with data collection through interviews, observation, and documentation. The results showed that administration played an important role as the main manager of digital documents through the use of applications such as SIMPEG, TTE, and SAKTI, as well as the use of cloud-based storage to maintain the security and ease of access to archives. The obstacles faced include the limited digital capabilities of employees, technical disruptions to applications, and suboptimal technological infrastructure. However, various efforts have been made, including performing regular backups, improving the organization of digital documents, and enhancing staff capabilities through independent learning. This study confirms that the success of digital archive management requires synergy between human resource readiness, technological support, and managerial policies that support the transformation of madrasah administration.

In the modern computing era, servers face significant challenges in data storage due to hardware failures, cyber attacks, or human errors. The problem highlighted focuses on the impact of file systems on three critical aspects: data integrity (accuracy and consistency of data without corruption), data recovery (the ability to restore data after a failure), and failure resilience (fault tolerance, such as redundancy and journaling to prevent downtime). The main issue is that traditional file systems like FAT32 or NTFS are often susceptible to fragmentation, metadata loss, or long recovery times, which can lead to data loss of up to 20-30% on enterprise servers, especially in high-traffic environments like cloud computing.A simple problem-solving process is conducted through a straightforward comparative analysis approach: (1) A literature review of popular file systems (ext4, ZFS, Btrfs); (2) Failure simulations using tools like fsck and stress testing on virtual servers (e.g., via KVM or Docker); and (3) Measuring performance metrics with benchmarking tools like Bonnie++ for I/O throughput, recovery time, and error rates. This process is designed to be simple, requiring only a virtual lab setup without expensive hardware, and is analyzed quantitatively with descriptive statistics.The solution to the problem indicates that advanced file systems like ZFS or Btrfs provide significant improvements: data integrity is up to 95% more secure through automatic checksums, data recovery is achieved in minutes through snapshots and RAID integration, and failure resilience is higher with copy-on-write features. The main recommendation is to migrate to journaling-based file systems for servers, combined with automated backups, which can reduce the risk of downtime by up to 50%. This research provides practical guidance for system administrators to enhance server reliability without excessive additional costs.

The waste detection application is a solution to identify and classify waste. With the increasing waste problem, this application aims to assist in classifying waste into organic, inorganic, and unknown categories. This research implements a waste detection application that runs on smartphones with the Android operating system. The application is the result of a capstone project from the Bangkit program in the MSIB batch 6. The development of this application is divided into several parts, namely backend, frontend, and deployment. The author focuses on the deployment process of the application using Google Cloud Platform. The Google Cloud Platform infrastructure was chosen due to its scalability and flexibility. The services utilized include Google Compute Engine (GCE), Google Virtual Private Cloud (VPC), and Google Cloud Storage (GCS). The deployment process involves creating a new project in Google Cloud, configuring virtual machines, and setting up Google Cloud Storage. The server VM configuration includes the installation of SQL Server (MariaDB), deployment of the Machine Learning API, and Backend API. Testing was carried out on the Machine Learning API using Postman and the Backend API through a browser. The results show that Google Cloud Platform can be implemented as a cloud computing infrastructure for waste detection applications. The Machine Learning API is able to read objects in the form of images sent by users.

Dr. Soedirman Kebumen Regional General Hospital has implemented PACS to replace traditional film, but limitations in IT infrastructure, RME integration, and human resource readiness require an integrated implementation model that combines cloud-hybrid, DICOM/HL7 with SSO, continuous training, and managerial support. To evaluate the implementation of the PACS system in the Radiology Department of Dr. Soedirman General Hospital in Kebumen and to analyze the factors that support and hinder the effectiveness of the PACS system in improving the quality of radiology services. The research used a qualitative approach with an interactive model. Data collection was conducted through in-depth interviews, Focus Group Discussions (FGD), direct observation, and documentation. The data obtained were analyzed using ATLAS.ti software to explain the PACS implementation and its impact on the effectiveness of radiology services. The PACS implementation improved the quality of radiology services by accelerating access to medical images and enhancing workflow efficiency. Some challenges, such as system downtime, integration with other systems, and technical limitations, need to be addressed. Integration of artificial intelligence (AI) and telemedicine technologies needs to be enhanced to achieve optimal radiology services. Factors supporting successful implementation include the adoption of advanced technologies (cloud computing and AI), adequate infrastructure, technical support from the IT team, and strong managerial commitment.  Barriers to success include imperfect system integration, power outages, downtime, storage capacity limitations, and a shortage of trained human resources. Proposed implementation models include improving PACS system infrastructure, developing ongoing training for staff, improving PACS system integration with other hospital systems, and improving interdepartmental communication to streamline workflows and reduce obstacles in the diagnostic process.

The digitalization of Micro, Small, and Medium Enterprises (MSMEs) has become a crucial strategy for enhancing competitiveness in the digital economy era. This study examines the utilization of digital technology by MSMEs in Bandung City, as well as the challenges and opportunities faced during the digital transformation process. Employing a literature review approach and the Systematic Literature Review (SLR) method, this research identifies the impact of the digital economy on MSMEs, including the use of cloud computing for data storage and analytics to understand consumer behavior. Despite challenges such as digital literacy gaps and budget constraints, support from the government through initiatives like the UMKM Go Digital Movement and Bandung Smart City has accelerated digitalization efforts. These programs provide significant training and assistance, with over 5,000 MSMEs receiving digital training and reporting an average revenue increase of 30%. The study concludes that collaboration among government, academia, and the private sector is essential to ensure the sustainability of MSME digital transformation. Policy recommendations are also suggested to support MSMEs in adopting digital technology more effectively

Perkembangan teknologi informasi yang pesat telah mendorong adopsi layanan cloud storage sebagai solusi utama dalam penyimpanan dan pengelolaan data digital. Kemudahan akses, skalabilitas, dan efisiensi biaya menjadi alasan utama banyak individu maupun organisasi beralih ke layanan ini. Studi ini bertujuan untuk melakukan analisis perbandingan antara dua layanan cloud storage populer, yaitu Google Drive sebagai layanan komersial berbasis cloud publik, dan Nextcloud Server sebagai solusi open-source berbasis self-hosted. Penelitian ini menggunakan metode eksperimen kuantitatif deskriptif, dengan fokus pada pengujian parameter teknis seperti kecepatan unggah dan unduh, efisiensi penggunaan bandwidth, kestabilan koneksi, serta estimasi biaya operasional tahunan. Pengujian dilakukan dalam tiga skenario akses: jaringan lokal (intranet), akses publik melalui layanan Cloudflare, dan koneksi ke cloud komersial. Hasil pengujian menunjukkan bahwa Google Drive unggul dalam performa kecepatan dan kemudahan penggunaan, didukung oleh infrastruktur global dan optimisasi jaringan. Sementara itu, Nextcloud lebih menonjol dari sisi fleksibilitas konfigurasi, kontrol data secara penuh, serta efisiensi biaya jangka panjang. Berdasarkan analisis Total Cost of Ownership (TCO), Nextcloud menjadi pilihan ekonomis untuk penggunaan jangka panjang, terutama bagi institusi pendidikan dan organisasi dengan infrastruktur lokal. Penelitian ini diharapkan dapat menjadi referensi bagi pengguna dalam memilih layanan cloud storage yang paling sesuai dari sisi teknis, ekonomi, maupun kebijakan privasi data.

Letter management is one of the important aspects in supporting the efficiency of academic administration in schools. This study aims to explore and understand how letter management at SMA Al-Hidayah Medan can be optimized to improve the efficiency of academic administration. The study used a descriptive qualitative approach with data collection through direct observation and interviews with parties involved in the correspondence administration process. The research findings show that SMA Al-Hidayah Medan implements a combined letter management system, namely combining manual and digital management. Incoming and outgoing letters are recorded manually in the agenda book and expedition book, and archived digitally in the school computer system. However, the system used is still limited and has not been fully integrated. The obstacles found include limited human resources who have not fully mastered administration and information technology, weak document supervision, and archiving of physical letters that have not been systematically arranged. In addition, digital archiving which is still conventional without automatic categorization makes the archive search process less efficient. As an optimization effort, the school has implemented a semi-digital system, provided training for administrative staff, compiled letter classification codes, compiled SOPs for letter management, and utilized cloud storage and digitization of old archives. These steps demonstrate institutional awareness to build a more efficient and modern administration system. In conclusion, effective letter management plays a very important role in supporting academic administration, but requires continuous coaching, better digital integration, and strong managerial commitment.

File systems are key operating system components because they store and retrieve files as needed. Traditional hierarchical file systems no longer suit current users’ expectations in organizing extensive collections of files for easy retrieval based on intrinsic and user-defined properties. The number of files in users’ collections is growing substantially, partly because of the ease with which consumer devices capture information. With the enormous capacity of contemporary storage devices and the rising trend of users storing their data in the cloud, they only fuel the number of files needing to be managed. We suggest that current file systems require an enhanced technique of data organization and access so that users can properly handle these ever-increasing data collections. File systems are key operating system components because they store and retrieve files as needed. This study offers improvements to the conventional Hierarchical File System to improve file organization and retrieval through built-in querying capabilities and support filesystem-level operations that execute significant amounts of metadata updates. This is accomplished using attributes (name-value pairs) in a file collection hierarchy. A series of improvements to the HFS introduce the “AttFS” file system. These improvements include using attributes rather than names, logical collections rather than directories, and introducing a query language to the API. We assess the expressive capacity of the resulting model, demonstrate that it solves the relevant shortcomings of traditional file systems in this space, and compare our new approach to those provided by others and our earlier work. We conclude that attributes are better suited than tags to overcome traditional HFS shortcomings.

The main focus of this research is to examine data storage efficiency methods, algorithm scheduling, and resource usage optimization techniques. This research uses literature study methods and simulation experiments to analyze system performance. The research results show that storage management methods based on deduplication and data compression have significant efficiency in reducing storage space requirements. The implications of this research include improving cloud system performance and reducing operational costs.

Durian is one of the leading fruit commodities with high economic value in various tropical regions, including Indonesia. However, the durian cultivation process often faces challenges related to unstable environmental conditions, such as temperature fluctuations, soil moisture, and nutritional deficiencies which can affect the level of plant fertility and the quality of the fruit produced. Therefore, an effective and efficient monitoring system is needed to optimize durian plant care. This research aims to develop a fertility monitoring system for durian trees using Internet of Things (IoT) technology which can help farmers manage the environmental conditions of plants in real-time. The designed system uses various sensors, such as soil moisture sensors, air temperature and humidity sensors, light intensity sensors, and soil nutrient sensors, to collect relevant environmental data. The data obtained from these sensors is then processed by a microcontroller and sent via the IoT network to a cloud-based storage platform. The trial results show that this system can monitor environmental conditions with high accuracy and provide appropriate maintenance recommendations, thereby increasing efficiency in managing durian plants.

Storage virtualization has become a key data management technology in the digital age, providing an efficient and secure solution for growing storage needs. Google Drive has adopted this technology to optimize user data management, including designing a security architecture, implementing data encryption in transit and at rest, and managing encryption keys. Access control using two-factor authentication (2FA). A reliable data backup and recovery system ensures fast recovery in the event of an accident. The technology enables additional features such as deduplication, compression, and version control that increase storage efficiency and flexibility. As a result, Google Drive offers secure, efficient and reliable cloud storage that allows users to manage data from multiple devices, confident that their data is protected.    

The rapid advancement of Industrial Internet of Things (IIoT) technology necessitates robust authentication solutions to ensure security, scalability, and efficiency. This project, titled "Enhancing Security in Industrial IoT: Authentication Solutions Leveraging Blockchain," examines various blockchain-based authentication methods for IIoT and identifies their strengths and weaknesses. Despite the enhanced security and decentralized nature of blockchain, issues such as scalability, high latency, and computational load persist. To address these challenges, we propose the integration of Multi-Factor Authentication (MFA) as a supplementary solution. MFA can distribute the authentication load, enhance flexibility and security, and reduce latency by utilizing quick-to-verify factors. Moreover, MFA ensures high availability and scalable storage and processing through cloud services, seamlessly integrating with existing systems to provide a superior user experience. This comprehensive approach not only mitigates the inherent limitations of blockchain technology in IIoT but also reinforces the overall security framework, ensuring resilient and efficient authentication mechanisms. The results demonstrate significant improvements in system performance and user satisfaction, establishing MFA as a viable enhancement to blockchain-based IIoT security solutions.

This research examines the impact of the use of cloud storage by companies on various types of security breaches, both in the short and long term. Adoption of cloud storage is associated with an increase in external breaches and unintentional internal errors in the short term. However, over time, the positive relationship between use of Cloud storage and external infringement becomes less significant in the long term. Research findings show that use of cloud Storage can reduce the risk of unexpected internal error in the longer term, but does not have a significant relationship with harmful internal breach. This research confirms the importance of companies allocating resources wisely to manage security risks in using cloud storage for both short- and long-term purposes. The researchers contributed to an understanding of the security of cloud storage by applying the theory of attention, which highlighted the importance of paying attention to temporal aspects in the management of enterprise IT security through various attention mechanisms.

Increasing fruit consumption is one of the challenges of public health. To address this, the authors developed a mobile application that allows users to scan and find information related to fruit. FruityFit is an application that can help users to get nutritional and processed information related to certain fruits that can increase users' interest in fruit consumption. This research focuses on constructing the back-end component of the FruityFit application. The design adheres to the REST architectural, leveraging the well-established ExpressJS framework for developmen. To realize the system, we implemented it using a suite of Google Cloud Platform (GCP) services. These services include Cloud Run, App Engine, Cloud Storage, and Cloud SQL, providing a scalable foundation for the back-end infrastructure.. The final result of this research is a REST API that has been running well and documented to help the front-end consume the API.

Technology has undergone significant and rapid change with the transition from self-managed data centers to visualized infrastructure. Data archiving has a very important role because it can make it easier to process data quickly and accurately. Web-based information systems have become an integral part of digital transformation in various industrial sectors. By providing centralized access to computing resources, data storage, and applications over the internet, the cloud provides significant freedom and flexibility. Web-based information systems bring great benefits to organizations that successfully adopt them. While challenges such as security and regulatory compliance need to be overcome, the opportunities for innovation and operational efficiency make cloud-based information systems a strategic choice in a company's digital transformation roadmap. Several institutional companies and individuals have used web-based archiving but it has not been effective. For this reason, a good archiving system is needed that makes it easier to search and process data. Web-Based Archiving Management Information System which will provide convenience in processing data to be more effective and accurate. Apart from that, it can also save time and better manage reports and decision making within the Company.

The rapid growth of big data has significantly increased the demand for efficient and scalable data processing methods, particularly within cloud computing environments. This study aims to evaluate the effectiveness of distributed computing frameworks, specifically Apache Hadoop and Apache Spark, in optimizing big data processing. A qualitative approach using a Systematic Literature Review (SLR) method is employed to analyze existing studies related to distributed systems, cloud computing architectures, and performance optimization techniques. The analysis focuses on key performance indicators, including processing speed, resource utilization, and scalability, as well as the suitability of each framework for different data processing scenarios. The findings indicate that Apache Hadoop is highly effective for batch processing and storage-intensive tasks due to its disk-based architecture, while Apache Spark demonstrates superior performance in real-time and iterative processing through its in-memory computing capabilities. Additionally, system configuration factors such as cluster size, memory allocation, and network bandwidth are identified as critical elements influencing overall performance. The study also highlights emerging trends, including the adoption of hybrid cloud environments, the integration of artificial intelligence and machine learning, and the utilization of edge computing to enhance real-time data processing. In conclusion, distributed computing frameworks play a vital role in improving the efficiency and scalability of big data processing in cloud environments. The selection of an appropriate framework, combined with optimized system configuration, can significantly enhance operational performance and support data-driven decision-making.