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.

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.

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.

In today's digital era, Learning Management Systems (LMS) play a crucial role in education. Despite the availability of numerous LMS platforms, challenges in designing effective and efficient systems persist, particularly in integrating comprehensive frameworks like the Zachman Framework. This study aims to explore the application of the Zachman Framework in LMS design to enhance system effectiveness and user satisfaction. The research employs a mixed-methods approach, combining qualitative and quantitative methods. Data is collected through a survey involving 100 respondents, including instructors, LMS developers, and students. The study analyzes qualitative data using thematic analysis and quantitative data through descriptive statistical techniques. The findings reveal that 85% of respondents believe that applying the Zachman Framework in LMS design significantly improves system effectiveness. Additionally, the average user satisfaction score for LMS designed using this framework is 4.2 on a 5-point scale, indicating a high level of satisfaction. This research concludes that implementing the Zachman Framework not only aids in identifying user needs and designing essential system functions but also ensures that all elements are well-integrated. These findings provide valuable insights for LMS developers and educational institutions in creating more effective and responsive systems that meet user needs..

Serial communication is a fundamental method for data transfer in electronic devices, particularly in Arduino-based systems. However, existing protocols, such as Universal Asynchronous Receiver/Transmitter (UART), often lack robust error detection mechanisms, leading to potential data integrity issues. This study aims to address the knowledge gap regarding error detection in UART communication by implementing Hamming Code, a well-established method for detecting and correcting single-bit errors. The research employs a systematic approach, including data encoding before transmission and decoding with error correction at the receiver end. The results demonstrate that the integration of the Hamming Code significantly enhances the reliability of data transmission, reducing error rates and improving overall system performance. The implications of this research extend to various applications requiring high data integrity, such as industrial control systems and Internet of Things (IoT) devices. By providing a practical solution to the challenges of error detection in serial communication, this study contributes to the advancement of reliable communication systems in modern technology.

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.

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

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.

Intrusion Detection Systems (IDS) are an efficient defense against network attacks as well as host attacks as they allow network/host administrators to detect any policy violations. However, traditional IDS are vulnerable and unreliable for new malicious and genuine attacks. In other case, it is also inefficient to analyze large amount of data such as possibility logs. Furthermore, for typical OS, there are a lot of false positives and false negatives. There are some techniques to increase the quality and result of IDS where data mining is one of technique that is important to mining the information that useful from a large amount of data which noisy and random. The purpose of this study is to combine three technique of data mining to reduce overhead and to improve efficiency in intrusion detection system (IDS). The combination of clustering (Hierarchical) and two categories (C5, CHAID) is proposed in this study. The designed IDS is evaluated against the KDD'99 standard Data set (Knowledge Discovery and Data Mining), which is used to evaluate the efficacy of intrusion detection systems. The suggested system can detect intrusions and categorize them into four categories: probe, DoS, U2R (User to Root), and R2L (Remote to Local). The good performance of IDS in case of accuracy and efficiency was the result of this study.

Intrusion Detection Systems (IDS) are an efficient defense against network attacks as well as host attacks as they allow network/host administrators to detect any policy violations. However, traditional IDS are vulnerable and unreliable for new malicious and genuine attacks. In other case, it is also inefficient to analyze large amount of data such as possibility logs. Furthermore, for typical OS, there are a lot of false positives and false negatives. There are some techniques to increase the quality and result of IDS where data mining is one of technique that is important to mining the information that useful from a large amount of data which noisy and random. The purpose of this study is to combine three technique of data mining to reduce overhead and to improve efficiency in intrusion detection system (IDS). The combination of clustering (Hierarchical) and two categories (C5, CHAID) is proposed in this study. The designed IDS is evaluated against the KDD'99 standard Data set (Knowledge Discovery and Data Mining), which is used to evaluate the efficacy of intrusion detection systems. The suggested system can detect intrusions and categorize them into four categories: probe, DoS, U2R (User to Root), and R2L (Remote to Local). The good performance of IDS in case of accuracy and efficiency was the result of this study.