In this digital era, the process of reporting the Annual Tax Return (SPT) for Individuals can be done online. However, there are still many Taxpayers who experience difficulties related to the procedure for reporting Annual SPT online. Community service activities in the form of assistance in reporting Individual SPT through e-Filing carried out by students of Nahdlatul Ulama University Sidoarjo as part of the Tax Volunteers held by the Directorate General of Taxes (DJP) located at the West Sidoarjo Pratama Tax Service Office. This activity aims to help reduce the spike in taxpayers who will report annual SPTs and optimize the role of Tax Volunteers in assisting Taxpayers. The method used is assistance and service to Taxpayers. As a result, a number of annual SPTs were successfully reported online. It is hoped that this Tax Volunteer activity will be routine every year with a wider coverage area.

The aim of this research will focus on the management of Regional Original Income and regional expenditure in Pasuruan City. Descriptive qualitative analysis methods will be used to explain the research results. Data obtained from the realization of Pasuruan City's financial reports for 2019-2023. The results of the analysis show that the degree of decentralization ratio has poor performance. The average performance financial independence ratio is very low. The average performance of the expenditure growth ratio is not good. Average operational expenditure performance is good. Average capital expenditure performance is good.

Business legality is the identity of a business that legalizes a business so that it is recognized by the community. Business legality must be valid according to applicable laws and regulations so that the business can be protected by various documents that are valid in the eyes of the law. One of the supporting factors for the sustainability of a business is influenced by the existence of legal elements of the business being run. Business permits that must be owned by the community are a business establishment deed, business entity NPWP, trade business license (SIUP), company domicile certificate (SKDP) and business registration number (NIB). The increase in community businesses in Sei Bingai District, Langkat Regency has triggered many business permits that are not directly supervised by the local government. Community business permits are important documents in supervising the running of these community businesses. The types of businesses in Sei Bingai District also vary, such as tourism, C mining, trade, factories and so on.

The Industrial Internet of Things (IIoT) enhances the connectivity and efficiency of living lifestyles. However, it also comes with significant security vulnerabilities. Traditional authentication methods are often inadequate, leading to IIoT devices opened to security threats. This paper proposes a comprehensive security framework integrating blockchain, cryptographic techniques, smart contracts, and deep learning-based Intrusion Detection Systems (IDS) to tackle the mentioned issue. Blockchain ensures data integrity and prevents tampering through a decentralized ledger. A decentralized device identity management system enhances user verification, while secure communication protocols using Hash-based Message Authentication Codes (HMAC) safeguard data integrity. Smart contracts automate transactions, providing transparent, secure record-keeping without a central authority. The deep learning-based IDS, utilizing Contractive Sparse Autoencoder (CSAE) and Attention-Based Bidirectional Long Short-Term Memory (ABiLSTM) networks, effectively detects cyber threats. Evaluation metrics, including precision, recall, F1-score, and False Acceptance Rate (FAR), demonstrate high accuracy and low false alarm rates across datasets. This framework addresses the need for secure, efficient, and scalable authentication in IIoT, combining blockchain's security features with advanced cryptographic and anomaly detection techniques, offering robust defence against cyber threats.

In recent years, virtual assistants have become an integral part of everyday life, simplifying routine tasks and allowing users to focus on more important matters. This research aiming to design GiggleGate, a virtual desktop assistant integrated with both face and speech recognition technology to enhance authentication security. The objective is to develop an authentication system that not only verifies user identity but also provides a more intuitive experience and seamless interaction. The research employs a development methodology to create and implement the system, which integrates face recognition via OpenCV and speech recognition via a Python library. The findings indicate that the integration of these technologies enhances security and user experience by offering dual-factor authentication. The system is expected to contribute to more secure and accessible virtual assistant applications, offering both a practical and efficient solution for users. The implications of this study suggest that the combination of face and speech recognition can provide an effective means to protect user privacy and improve the overall functionality of desktop assistants.

Personal Protective Equipment (PPE) is essential for worker safety, especially in oil palm harvesting activities. PT Langkat Nusantara Kepong faces major challenges related to work safety, with analysis showing that work accidents still occur frequently in the Padang Brahrang Plantation. This indicates the need for an in-depth evaluation of the use of Personal Protective Equipment (PPE) to reduce the risk of work accidents. By using the clustering method to group data based on the type of Personal Protective Equipment (PPE) used and aims to provide recommendations for optimizing the use of personal protective equipment based on risk management and reducing the incidence of work accidents. From testing the results of cluster 3, cluster 4 and cluster 5 it can be concluded that clustering with 5 clusters provides the most efficient and precise results, followed by 4 clusters, while 3 clusters provide greater variation within clusters, indicating that clustering with fewer clusters is less able to capture subtle differences in the data.

Energy efficiency is a critical concern in wireless sensor networks (WSNs) due to the limited power resources available in sensor nodes. Prolonging network lifespan while ensuring reliable data transmission is essential for successful deployment in various applications, such as environmental monitoring, military operations, and industrial automation. This paper presents a mathematical model designed to optimize energy consumption across various nodes in WSNs. By implementing simulations and analyzing data from these models, the study demonstrates significant improvements in extending network lifespan while maintaining reliable data throughput. The findings contribute valuable insights into energy management for large-scale sensor deployments.

The development of Internet of Things (IoT) technology provides opportunities to automate various devices, including height measuring devices. This research aims to design and build an automatic height measuring device that is integrated with IoT technology. This tool is designed to measure body height automatically and send the measurement data to a cloud-based platform, making it easier for users to monitor data in real-time via smart devices. This system uses an ultrasonic sensor to detect body height, a microcontroller as a data processor, and a Wi-Fi module to send data to the server. Test results show that this tool is capable of measuring body height with a good level of accuracy and provides convenience in storing and monitoring measurement data remotely. Thus, this IoT-based height measuring device can be an innovative solution for the need for height measurement that is more efficient and integrated with digital systems.

This study aims to analyze the efficiency of the curriculum of Islamic universities in Indonesia in ensuring the readiness of students to enter the workforce, by reviewing the role of Islamic religious education in improving intellectual competence. Through a literature study approach, this study identifies the challenges and opportunities faced by Islamic universities in integrating religious values ??with the intellectual and professional needs of students. The main focus of this study is the effectiveness of the curriculum in balancing theoretical aspects of religion and practical skills needed in the workplace. This study found that the curriculum of Islamic universities in Indonesia has attempted to adopt an integrative approach that combines religious education with the development of soft skills and hard skills that are relevant to professional careers. However, there are still some weaknesses in the implementation, such as the lack of curriculum adjustment to the dynamics of the labor market, as well as the imbalance between religious education and practical skills. The results of this study indicate that Islamic religious education has an important role in shaping the character and professional ethics of students, which are the main capital in entering the workforce. Therefore, increasing the efficiency of the curriculum through collaboration between academics, industry practitioners, and policy makers is needed to ensure that students' intellectuality is in accordance with the demands of the workplace. This study contributes to the academic discussion on the reform of Islamic higher education curriculum and emphasizes the importance of Islamic religious education in producing graduates who are not only intellectually competent, but also have strong ethical values ??to face professional challenges. The implication of this study is the need for a more flexible and adaptive curriculum strategy to improve the competitiveness of Islamic higher education graduates in the global job market.

As cyber-physical systems (CPS) gain prevalence in sectors such as manufacturing, transportation, and critical infrastructure, ensuring their security and reliability is paramount. Traditional anomaly detection methods often fall short due to the dynamic and complex nature of CPS, leading to missed or false alarms. This study introduces an enhanced machine learning model that integrates statistical and deep learning techniques for real-time anomaly detection in CPS. By employing a hybrid approach of convolutional neural networks (CNNs) with statistical pattern recognition, the model demonstrates improved detection accuracy and responsiveness. Performance is evaluated using industry-standard CPS datasets, showing that the proposed model outperforms existing techniques in both accuracy and efficiency.