System Development Life Cycle (SDLC) is a common methodology used to develop information systems. The purpose of this study is to determine system development between the Waterfall and Rapid Application Development (RAD) models The advantage of using the waterfall method is that the quality of the resulting system will be good because the implementation is carried out in stages. The disadvantage of using the waterfall method is that the system development process takes a long time. The advantage of using the RAD Model method is that the integration of sensors/other applications can be processed faster and more effectively. The disadvantage of using the RAD method is the limitations of payment gateways, and chat bots.

Kendaraan listrik menggunakan sumber tenaga dari energi listrik yang akan diubah menjadi energi kinetik. Sumber energi listrik tersebut disimpan di dalam baterai. Proses pengisian baterai dapat dilakukan dengan menggunakan sumber tenaga dari Panel Surya dan PLN. Berdasarkan analisis hasil pengukuran dapat disimpulkan bahwa (1) pengisian ulang baterai menggunakan Panel Surya dari kondisi minimum hingga maksimum membutuhkan waktu 4 jam 38 menit, dan (2) pengisian ulang baterai menggunakan PLN dari kondisi minimum hingga maksimum membutuhkan waktu 2 jam.

The increasing sophistication of cyber threats has outpaced the capabilities of traditional detection and response systems, necessitating the adoption of advanced machine learning architectures. This study investigates the application of Transformer-based models in cybersecurity, focusing on their ability to enhance threat detection and response. Leveraging publicly available datasets, including CICIDS 2017 and UNSW-NB15, the research employs a systematic methodology encompassing data preprocessing, model optimization, and comparative performance evaluation. The Transformer model, tailored for cybersecurity, integrates self-attention mechanisms and positional encoding to capture complex dependencies in network traffic data. The experimental results reveal that the proposed model achieves an accuracy of 97.8%, outperforming conventional methods such as Random Forest (92.3%) and deep learning approaches like CNN (94.1%) and LSTM (95.6%). Additionally, the Transformer demonstrates high detection rates across diverse attack types, with rates exceeding 98% for Denial of Service and Brute Force attacks. Attention heatmaps provide valuable insights into feature importance, enhancing the interpretability of the model’s decisions. Scalability tests confirm the model’s ability to handle large datasets efficiently, positioning it as a robust solution for dynamic cybersecurity environments. This research contributes to the field by demonstrating the feasibility and advantages of employing Transformer architectures for complex threat detection tasks. The findings have significant implications for developing scalable, interpretable, and adaptive cybersecurity systems. Future studies should explore lightweight Transformer variants and evaluate the model in operational environments to address practical deployment challenges.

This study developed a typo correction system for the Indonesian language by integrating the Levenshtein Distance algorithm with empirical methods. The system is designed to improve the accuracy of typo detection and correction in Indonesian texts, which feature complex morphological structures such as prefixes, suffixes, and compound words. The findings show that the system achieved a precision rate of 92% and an F1-score of 90.5%, indicating high reliability in providing relevant correction suggestions. Additionally, the system demonstrated efficiency in processing time, with an average of 0.8 seconds for short texts and 5.3 seconds for longer texts. The use of empirical methods enables the system to handle complex language variations, resulting in more contextually appropriate correction suggestions. User feedback indicated high satisfaction with the interface and the relevance of the suggestions provided. Overall, this research makes a significant contribution to the development of more adaptive and efficient typo correction systems for the Indonesian language and opens up opportunities for further development in the context of other similar languages.

This paper addresses the growing need for effective educational tools in greenhouse gardening, particularly for beginners lacking prior agricultural knowledge. Despite the increasing availability of mobile applications aimed at agricultural education, many existing resources fail to engage users through interactive experiences, thereby limiting their practical skill development. This study identifies a significant knowledge gap regarding the specific needs of novice gardeners and the effectiveness of current educational applications. To address this gap, we developed an innovative mobile application designed to facilitate asynchronous learning through interactive features such as discussion forums and quizzes. A mixed-methods approach was employed, involving user testing with 30 participants to evaluate the application’s usability and engagement levels. The results indicated that 90% of users found the application intuitive and easy to navigate, with enhanced motivation attributed to its engaging visual design. These findings suggest that the proposed application not only meets the educational needs of users but also fosters a more interactive and responsive learning environment. The implications of this research highlight the potential for mobile applications to significantly improve practical knowledge and skills in greenhouse management, ultimately contributing to more sustainable agricultural practices

The successful adoption of programmable machines for complex tasks opens up opportunities for productivity and more efficient communication, but also poses serious challenges in IT project management. This study aims to tackle the issue of high project failure rates caused by inadequate planning. It aims to assist project managers in enhancing their project planning by implementing real-time solutions through the utilization of machine learning (ML) algorithms and a user-friendly graphical interface. This research is divided into two key phases. The initial phase involves an examination of existing literature in the field of machine learning to identify relevant concepts applicable to project management. In the subsequent stage, two distinct types of ML algorithms, namely example-based learning and regression modeling, will be integrated into a user-friendly platform. This research develops a system that utilizes machine learning algorithms to assist project managers in real-time or near real-time through a user-friendly graphical interface, with a focus on improving project planning and risk mitigation. This research shows that machine learning algorithms provide positive results in overcoming human factors and preventing risks based on the experience of project managers.

Electric vehicles are starting to be widely used among conventional vehicles. The use of a motor as a driver is a very important component for electric vehicles. The battery current and voltage supplied to the motor will determine the performance of the electric vehicle. Based on the analysis of current and voltage measurement results, it can be concluded that (1) the power requirements using the Brusshes Direct Current (BDC) motor type are greater, (2) Electric vehicles are more efficient when using the Brusshes Less Direct Current (BLDC) motor type.

Android is an operating system with open source and consists of several layers, with the different layers its duties and responsibilities. Various parties in the customization chain such as device vendors such as Samsung, Xiaomi, Oppo, Huawei, and others, operators such as Telkomsel, Smartfren, XL, etc., and hardware manufacturers can customize one or more layers to adapt devices for different purposes, such as supporting specific hardware and providing different interfaces and services.
The purpose of this study was to investigate systematically for any inconsistencies that arose as a result of the processes involved in this study and to assess their various security implications. This research runs DroidDiff to perform a substantial-balance diverse investigation on images collected by the analytical methodology. DroidDiff found a lot of differences when it comes to the selected features. The method used in this study is the method of five differential analysis algorithms. As a result, by comparing the security configurations of similar figures, important security changes that could be accidentally introduced during customization can be found.
The results show that DroidDi? can be used by vendors to check the configuration of various security features in a given image. DroidDiff will extract those features from the image, and compare them to other image configuration sets, then DroidDiff will flag the inconsistent ones for further investigation by vendors who have the source code and tools to check their effect. For future work, improvements to DroidDi? to more accurately detect risky inconsistencies are highly recommended. Improving DroidDiff will help reduce the number of false positives and determine risky configurations more accurately.

The current era is growing Live streaming which includes audio and video data. This research method collects data through the results of measuring Video Streaming latency (delay time) on variations of Local Area Network and Web-based computer networks. Based on the analysis of measurement results, the latency value (delay time) of Video Streaming on Local Area Network-based devices using Quality of Service (QoS) obtained a value of 8.58 ms and is smaller (optimal) compared to Web-based networks.
Keywords : Quality of Service, latency, LAN, Web