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.

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.

Almost every house already uses a Wifi network as internet access. With the help of this Wifi network, it is used as Middleware that can be controlled and monitored when outside the home. This study engineered data packet traffic using the ESP8266 wifi module on the Middleware device network to find out how fast the data packet capture time using wifi can be controlled and monitored when outside the home. Based on the analysis, the measurement results on the IoT device network to find out how fast the data time using wifi it reaches 2.95 kbps to 7.4 kbps.

Credit Card (CC) scam In financial markets is a growing nuisance. CC scams increasing rapidly and causing large amounts of financial losses for organizations, governments, and public institutions, especially now that all payment methods for e-commerce shopping can be done much more easily through digital payment methods. For this reason, the purpose of this study is to detect scam CC transactions from a given dataset by performing a predictive investigation on the CC transaction dataset using machine learning techniques. The method used is a predictive model approach, namely logistic regression models (LR-M), random forests (RF), and XGBoost combined along particular resampling techniques that have been practiced to anticipate scams and the authenticity of CC transactions. Model performance was calculated grounded Re-call Curve (RC), precision, f1-score, PR, and ROC.
The experimental results show that the random forest in combination with the hybrid resampling approach of SMOTE and removal of Tomek Links works better than other models. The random forest model and XGBoost accomplished are preferred over the LR-M as long as their global  f1 score is without re-sampling. This demonstrates the strength of one technique that can provide greater achievement alike in the existence of class inequality dilemmas. Each approach, at the same time when used with Ran-Under, will give a great memory score but fails cursedly in the language of accuracy. Compared to the coordinate model sine re-sampling, the accuracy and RS are not repaired in cases where Tomek linker displacement was used. RF and xgboost perform quite well in terms of f1-S when Ran-Over is used. SMOTE increases the random forest draw score and xgboost but the precision score (PS) decreases slightly.
Completely, during a hybrid solution of Tomek delinker and SMOTE was practiced with random forest, it gave equitable attention and RS in the PR-AUC. XGboost failed to increase the PS even though the same re-sampling technique was used. For future research, a fee-delicate study method can be applied as long as fee misclassifications. So for future research, it is very necessary to consider this behavior change and it is also very important to develop predictive models. In addition to this, much larger data is needed so that detailed studies on handling non-stationary properties in CC scam detection can be carried out better.

Stock reports on purchases and sales of a period. useful for helping employees to assess the amount of inequality in the flow of purchases, monitor stock / inventory of goods, and provide reports both purchase reports, sales reports, stock condition reports, and profit / profit reports to management to determine further business policies The process of recording transactions in a conventional or paper-based manner is identical with several weaknesses, namely easily damaged, difficult / long when looking for stored data, difficult / long when it comes to generating reports. These weaknesses can be corrected by implementing computer-based monitoring applications. If the monitoring application is planned/made properly, the computer-based information system is relatively safer. The model to be developed refers to the Research and Development (R&D) model from Borg and Gall and the tools used and the tools used to design the system are use case diagrams, sequence diagrams, activity diagrams, and class diagrams. while the implementation of making stock/inventory monitoring applications is done with PHP web-based programming and databases using MySQL. This application can help and get more accurate information about the stock data of goods that have run out.