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The rapid development in the field of information and communication technology currently requires students to master Microsoft Word. This skill becomes a crucial asset, both to support academic activities and as early preparation before entering the workforce. One of the applications frequently used in document preparation is Microsoft Word. Observation data at SMK Ibrahiny 1 Sukorejo indicate that the utilization of Microsoft Word features by some students is not yet optimal. This is evident in the students' limitations in configuring layouts, margins, as well as creating headers and footers in documents. In order to achieve efficiency and speed in the creation of administrative documents, this training is focused on enhancing students' competencies in exploring the features of Microsoft Word. This training was conducted on June 1, 2026, in the computer lab of SMK Ibrahimy 1 Sukorejo, involving 15 students from the 10th grade forestry department as participants. The method used during the training included the delivery of basic material followed by direct practice and the use of a system roll method on each computer to identify obstacles or challenges in preparing administrative documents, as well as direct assistance to students to quickly resolve any issues and ensure no one misses the material. The purpose of this training is to improve efficiency in properly and accurately preparing administrative documents. Therefore, the organization of Microsoft Word training can be categorized as a strategic step in boosting the digital skills of the students.

Malware represents an evolving cybersecurity threat that demands more effective detection methods. Conventional signature-based detection systems have limitations in identifying new variants, driving the development of deep learning-based approaches. This research implements and evaluates four variants of the YOLOv11 algorithm (n, s, m, l) for malware classification based on visual image representation. The dataset consists of 22,056 malware and benign images, divided into 70% training, 15% validation, and 15% testing across 8 classes (adware, backdoor, benign, downloader, spyware, trojan, virus, worm). Each model was trained for 100 epochs with batch size 32 using Google Colab with GPU support. Results demonstrate that all variants achieve high accuracy (97.8%-98.1%) with YOLOv11m as the best performer (98.1%). YOLOv11n offers optimal balance between accuracy (97.9%) and efficiency (1.5M parameters, 0.3 ms/img inference) ideal for real-time applications. This research surpasses previous methods such as K-NN (97.18%) and hybrid CNN (96.55%) with superior inference speed (0.3-0.9 ms/img vs tens to hundreds of ms/img), proving the effectiveness of YOLOv11 for fast, accurate, and scalable malware detection.

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.

The development of the digital economy has driven increased customer interaction through online chat services, making customer satisfaction a key factor in business success. Response speed and chat service quality are two important aspects in shaping the customer experience, but previous research has tended to examine them separately. This study aims to analyze the influence of online chat services and response speed on customer satisfaction partially and simultaneously. The method used is a qualitative approach with a literature review of 12 scientific articles from 2020–2025 obtained from academic databases such as Google Scholar and SINTA. The analysis technique used is descriptive-critical through the identification, comparison, and synthesis of previous research findings. The results show that online chat services have a positive effect on customer satisfaction, primarily through interaction quality such as information accuracy, ease of use, and problem-solving ability. Response speed has also proven to be an important determinant, where a fast response significantly increases customer satisfaction. However, speed without quality has the potential to decrease satisfaction. The discussion shows that the two variables have a complementary and inseparable relationship. Online chat services function as a medium for interaction, while response speed is a quality attribute that determines the effectiveness of the service. Therefore, the integration of both in one model is the main contribution of this research in filling the literature gap, especially in the context of e-commerce in Indonesia.

 The quality of digital system services in the ship clearance process is closely related to the work effectiveness of shipping agency companies. The advancement of port operational services is influenced by the system’s ability to provide convenience, speed, accuracy, and service integration for users. The system used in the ship clearance process must be able to meet user needs and support inter-agency coordination so that ship services can operate more effectively and efficiently. This study aims to analyze the performance level and the level of user importance regarding the implementation of the Single Submission Pengangkut System in the ship clearance process at PT Trans Cakrawala Perkasa. The research employed a descriptive quantitative approach with a sample of 30 respondents consisting of ship agents involved in the clearance process. Data were collected through questionnaires, observations, and literature studies. The data were analyzed using validity tests, reliability tests, conformity level analysis, and the Importance Performance Analysis (IPA) method. The results indicate that the SSM Pengangkut System has made a positive contribution to accelerating document processing, facilitating system usage, increasing work productivity, and supporting coordination among related agencies. Based on the IPA Cartesian diagram, one indicator was identified as a priority for improvement, while other indicators were categorized as maintain performance, low priority, and high performance with lower importance levels. Overall, the implementation of the Single Submission Pengangkut System has been running quite well; however, continuous evaluation and improvement are still needed to optimize the ship clearance process further.

The advancement of digital technology has significantly transformed organizational decision-making, particularly in modern leadership contexts that demand rapid and data-driven responses. Artificial Intelligence(AI) has emerged as a strategic technology capable of enhancing accuracy, speed, and effectiveness in decision-making through comprehensive data analysis. This study aims to analyze the role of AI in supporting leadership decision-making and its implications for organizational effectiveness using a narrative literature review approach. Secondary data comprising peer-reviewed national and international journal articles were analyzed to identify patterns, themes, and interactions between AI, leadership, and decision-making processes. The findings indicate that AI functions not only as a data analysis tool but also as a strategic element that strengthens leaders’ capabilities in evidence-based decision-making, improves team coordination, and optimizes organizational processes. Thematic synthesis identified three main domains analytics and predictive capabilities, leadership strategies, and implementation challenges that form the basis for integrating AI into managerial practice. This study contributes theoretically by expanding the digital leadership and technology-based decision-making framework and practically by providing guidance for organizations to optimize AI utilization to enhance decision quality and efficiency. The research also offers directions for future empirical studies to explore AI-leadership interactions across various organizational sectors, supporting more adaptive, effective, and data-driven decision-making in the digital era.

This research aims to design and develop a prototype wave power plant that utilizes the vertical motion of a buoy as a source of mechanical energy, which is then converted into electrical energy using a recoil starter mechanism. The system is designed to be installed at the stern of a prototype ship. The vertical movement of the buoy caused by ocean waves is transmitted to the recoil starter through a drive rope, producing a stable one-way rotational motion. This rotation is further transmitted to a gearbox to increase rotational speed before driving a DC generator. The electrical energy generated is stored in a 12 VDC battery, supported by a buck–booster converter to stabilize the output voltage. This study employs an experimental engineering approach to evaluate system performance based on empirical test data. The main components of the system include a buoy as a wave energy collector, a recoil starter as the initial rotating mechanism, a DC generator as the electrical energy producer, a buck–booster converter as a voltage regulator, a 12 VDC battery as an energy storage unit, and a monitoring system based on an ESP32 microcontroller integrated with a PZEM-017 sensor. Experimental results show that the recoil starter operates effectively in driving the generator under both no- load and buoy-loaded conditions. Increases in generator rotational speed are directly proportional to increases in output voltage and current. The PZEM-017 sensor demonstrates a high level of measurement accuracy, approaching 100% when compared with a multimeter. Overall, the proposed wave power generation system functions reliably and shows potential for further development as a small-scale alternative renewable energy source.

The dimensions of healthcare service quality are a concept for measuring service quality. This study aims to determine the effect of the dimensions of healthcare service quality on the satisfaction of inpatients at Cut Nyak Dhien General Hospital in Langsa City. This study uses a quantitative design of the Survey Analytic type with a cross-sectional approach. The population consisted of 121 inpatients with a sample size of 55 respondents using purposive sampling technique. Analysis used multiple logistic regression test (α=0.05). The research results show that there is an influence between Responsiveness p<0.042 with an EXP (B) value of 11.361, Assurance p<0.008 with an EXP (B) value of 20.163, Empathy p<0.018 with an EXP (B) value of 13.547, Reliability p<0.033 with an EXP (B) value of 6.896 on inpatient patient satisfaction (p<0.05), while the Tangible variable shows no influence with p<0.088 and an EXP (B) value of 4.967. Among these variables, the one that significantly affects inpatient patient satisfaction is the Assurance variable with p<0.008 and an EXP (B) value of 20.163. It is recommended to continuously improve the quality of service comprehensively in every dimension of service quality, strengthen the competence and professionalism of healthcare personnel, enhance the speed and accuracy of service, and build good communication to improve the quality of inpatient services and maintain patient trust in the hospital.  

Emotional mental health in adolescents is an important focus because this age group is highly vulnerable to academic and social pressures, as well as the demands of personal development. Emotional problems such as anxiety, stress, and depression that are not recognized early on can interfere with concentration, social relationships, and psychological well-being. This study aims to review the literature related to the early detection of emotional mental disorders in adolescents, so that it can serve as a basis for the development of prevention and intervention strategies. The method used is a literature review, analyzing a number of recent research articles discussing risk factors, symptoms, and early screening methods using instruments such as the Self Reporting Questionnaire (SRQ) and the Strengths and Difficulties Questionnaire (SDQ). The results of the study show that the most common symptoms include anxiety, fatigue, concentration disorders, and relationship problems with peers in adolescents, as well as academic pressure in students. Early screening has been proven to help identify individuals at risk so that intervention can be carried out more quickly. The use of technology, such as web-based expert systems, can also speed up the screening process and improve the accuracy of assessments. It is recommended that educational institutions and health services be more active in conducting mental health screening, providing promotional education, and designing preventive programs. Further research should use analytical or experimental designs to explore risk factors and the effectiveness of interventions in greater depth.

This study aims to develop a web-based Electronic Medical Record (EMR) application to improve the efficiency and accuracy of patient data recording at Elopada Community Health Center. The research adopts a system development approach using the waterfall model, including requirements analysis, system design, implementation, and testing. Data were collected through observation, interviews, and documentation. The developed system was evaluated using Black Box Testing to verify the conformity between input and output without considering internal program structures. The results indicate that the EMR system enhances data processing speed, improves recording accuracy, and facilitates access to patient information. These findings are consistent with prior studies showing that EMR implementation improves the quality and efficiency of clinical documentation . Therefore, the proposed system is considered feasible to support the advancement of digital healthcare services.

The purpose of this study is to determine how the use of SOGAM puzzle media in mathematics can improve students' basic counting skills at the elementary school level, especially for addition and subtraction materials in grade 1. This study adopted a descriptive qualitative approach involving 27 grade 1 students of MI NU Hidayatul Mustafidin as research subjects. Data collection was carried out through observation and documentation during learning activities. The research findings showed that students appeared inactive in the learning process and faced difficulties in understanding the concept of addition and subtraction before the introduction of SOGAM puzzle media in mathematics. After the implementation of SOGAM puzzle media in mathematics, students' counting skills improved, as shown by increased accuracy in answering questions, speed in calculating, and better understanding of concepts. In addition, students became more active, enthusiastic, and motivated during learning. SOGAM puzzle media in mathematics can create a fun and interactive learning environment by incorporating game elements in the learning process. Thus, the use of SOGAM puzzle media in mathematics is proven to be effective in improving students' basic counting skills in addition and subtraction materials in elementary schools.

Fixed asset management is a crucial component in supporting the performance of local governments. However, the Polewali Mandar Regency Government still faces obstacles in managing assets conventionally, such as the risk of recording errors, duplicate data, and difficulties in field data verification which is time-consuming. This study aims to design and build a Web-Based Fixed Asset Management Information System integrated with Quick Response Code (QR Code) technology as a digital solution for real-time asset data collection, tracking, and monitoring. The approach taken for system development is known as the Waterfall approach, encompassing the phases of analyzing requirements, system design, execution, evaluation, and ongoing support. The creation of the system was carried out utilizing the PHP coding language, the MySQL database system, and Tailwind CSS to achieve a mobile-friendly interface. Data collection techniques were conducted through observation, interviews, and literature studies at the General Affairs Division of the Regional Secretariat of Polewali Mandar Regency. The result of this research is a fixed asset management information system featuring the generation of unique QR Code labels for each asset, category and location management, and reporting features divided into three access levels: Admin, Operator, and Verificator. Based on the system testing results, an average score of above 4.00 was obtained for indicators of ease of use (user-friendly), access speed, and information accuracy. In conclusion, the implementation of this QR Code-based system is proven to increase the efficiency of the asset identification process in the field, minimize manual input errors, and improve accountability in regional asset management in Polewali Mandar Regency.

This study aims to analyze the performance of Android-based attendance applications used at PT PLN ULP West Sumba by integrating two evaluation methods, namely WebQual 4.0 and Importance Performance Analysis (IPA). This attendance application functions to monitor employee attendance digitally so that it is expected to be able to improve the efficiency and accuracy of data recording. Evaluations are conducted to assess the extent to which the application meets the needs of users as well as the expected performance. WebQual 4.0 is used to measure the quality of user experience in terms of ease of access, interactivity, trust, and satisfaction, while IPA is used to compare the level of user interest with application performance based on four main attributes: system quality, information quality, service quality, and usage quality. The research data was collected through a survey with questionnaires compiled according to WebQual 4.0 and IPA indicators, involving application users at PT PLN ULP West Sumba. The results show that the majority of users are satisfied with the ease of use and performance of the application, but there are aspects that need to be improved, especially the speed of the system and a more user-friendly interface design. The science analysis emphasizes that the quality of systems and information is a crucial factor that must be a priority for development. This research provides strategic recommendations for PT PLN ULP West Sumba to improve the performance of the attendance application and support the company's operational needs in a sustainable manner.

This study discusses the implementation of the CEISA 4.0 System (Customs Excise Information System and Automation) in managing Manifest Inward and Outward data at PT. Surya Samudera Indah under the supervision of the Batam Customs Office. The research is motivated by the need for efficiency and accuracy in customs processes in the era of digitalization. The purpose of this study is to analyze the effectiveness of CEISA 4.0 in supporting customs supervision and services, as well as to identify the challenges encountered during its implementation. The research employs a qualitative descriptive method with a case study approach through observation, interviews, and documentation. The findings show that the implementation of CEISA 4.0 has had a positive impact on the speed and accuracy of manifest data reporting, particularly in document validation and tracking processes. However, several technical barriers such as network disruptions and system errors, as well as non-technical obstacles including limited human resource competence and lack of coordination between the company and customs authorities, were identified. The implications of this research highlight the need for system optimization through technological infrastructure improvement and human resource capacity enhancement to ensure CEISA 4.0 functions more effectively in supporting the smooth flow of national and international logistics.  

This research aims to design and build a client service website and pre-registration of cases at the KOFIPINDO Law Office as a solution to administrative problems that are still carried out manually. The system is developed using the waterfall method through the stages of needs analysis, system design, coding, testing, and maintenance. The website that was built provides features for account registration, filling in case data, uploading supporting documents, and verification by the admin so that the file is ready to be submitted to the e-Court system. In addition, the system is equipped with a case status monitoring dashboard, automatic notifications to clients, and submission history that can be accessed at any time. The results of the study show that this platform is able to improve service efficiency, minimize administrative errors, and speed up the pre-registration process of cases. The implementation of this system also helps to increase the transparency of communication between law firms and clients. Furthermore, the use of this website has the potential to reduce the administrative workload of staff, optimize the management of case data, and improve the accuracy of legal documentation. The system is also designed with the client's data security and privacy aspects in mind in accordance with the principles of digital information protection. Thus, this system can be an effective, structured, and integrated digital means to support the modernization of legal services at KOFIPINDO, improve service professionalism, and strengthen the competitiveness of law offices in the digital ecosystem.

The rapid advancement of Artificial Intelligence (AI) is reshaping public governance, including legislative processes. In the United Arab Emirates (UAE), AI is being actively utilized to enhance law-making through faster drafting, improved consistency, and greater transparency. This study examines the role of AI in the UAE’s legislative functions, focusing on how AI tools assist in analyzing legal data, formulating policy recommendations, and drafting legislation. It explores how AI impacts the speed, accuracy, and legitimacy of law-making, while also addressing the ethical and legal challenges of delegating legislative tasks to intelligent systems. Using a qualitative case study method, the paper evaluates government initiatives, expert insights, and regulatory structures that frame AI's integration into the UAE’s law-making system. While AI offers opportunities for data-driven governance and increased legislative productivity, it also presents risks such as algorithmic bias, reduced human oversight, and accountability gaps. The study emphasizes that AI must be governed by strong regulatory frameworks to safeguard democratic values, fairness, and legal integrity. By analyzing a pioneering national model, this research contributes to global discussions on AI in governance and offers key insights for policymakers, technologists, and legal scholars seeking to balance innovation with ethical and legal standards.

Health services at the Puskesmas (Community Health Center) are an important sector directly related to the community. However, there are still various challenges in patient data management and handling complaints that can hinder service efficiency. One of the efforts to improve service quality is by developing a complaint information system that can efficiently manage and record patient complaints. This study aims to develop a complaint information system for services at the Puskesmas Waimangura using the Prototype method. This method was chosen because of its ability to produce system prototypes that can be immediately tested and developed according to user needs. The system is designed to allow patients to submit complaints related to the services received, as well as enabling Puskesmas staff to follow up on and record each complaint systematically. With the implementation of this system, it is expected to increase efficiency in managing complaint data, speed up problem resolution processes, and improve accuracy in recording patient and complaint data. The results of prototype testing show that this system simplifies the complaint process and provides convenience for staff in following up on patient complaints. The implementation of this information system is expected to improve the quality of services at Puskesmas Waimangura and accelerate responses to issues faced by patients.

The automotive industry requires fast and accurate sales services, particularly in vehicle credit simulation processes. At Mazda dealerships, credit simulations are still commonly conducted using conventional tools such as printed installment tables or static PDF documents, which often cause delays and calculation errors. This study aims to design and develop a mobile-based vehicle credit simulation application using the Human-Centered Design (HCD) approach and the Flutter framework. The HCD method was implemented through the inspiration, ideation, and implementation stages to ensure that the application meets the real needs of Mazda sales representatives. The application supports flexible credit calculations based on vehicle on-the-road price, down payment, loan tenor, interest rate, and insurance schemes, including All Risk and combination insurance. Usability testing results show that the proposed application significantly improves calculation speed, accuracy, and overall user experience compared to conventional methods. Therefore, the application effectively supports sales performance, minimizes human error, and enhances professionalism in automotive sales services.

The era of digitalization demands that the world of education adapt quickly to technological developments. One of the basic skills that elementary school students need to have is the ability to operate a computer, particularly in typing and using word processing applications. Community service research was conducted at the Kemala Bhayangkari Foundation 1 Medan with the aim of improving students' digital literacy thru typing training using Microsoft Word and the interactive media 10fastfingers.com. The training method is conducted using a workshop model, which consists of two main topics. The first material is a basic introduction to typing using the ASDF;LKJ pattern and the use of important keyboard keys. The second material is an introduction to basic Microsoft Word features, such as changing font type and size, using Bold, Italic, Underline, changing text color, and highlighting. Additionally, students are trained to use 10fastfingers.com to interactively improve their typing speed and accuracy. The research instruments are pretest-posttest, observation, and student response questionnaires. The research results show a significant improvement in typing skills. Average speed increased from 8–10 words per minute to 20–25 words per minute, with over 85% accuracy. Student responses were also very positive; they felt more confident, more motivated, and able to use Microsoft Word more effectively. This training program has proven effective in improving digital literacy among elementary school students while also fostering an interest in technology-based learning.

This study explores the integration of deep learning based approaches in real time video content analysis for intelligent human computer interaction (HCI) in multimedia systems. Traditional video analysis techniques, such as rule-based methods and offline processing, struggle with real time performance and adaptability to complex video data. In contrast, the deep learning model used in this research, particularly Convolutional Neural Networks (CNNs), provides high accuracy in object detection, feature extraction, and real time processing. The integration of CNNs with interactive visualization modules enables dynamic adjustments to video content based on user interactions, ensuring a seamless and engaging user experience. The system was benchmarked in terms of its processing speed, accuracy, and responsiveness, showing significant improvements over traditional approaches in real time video analysis. Moreover, the study demonstrates that combining deep learning with real time visualization enhances the efficiency of interactive multimedia applications, making it suitable for dynamic environments such as surveillance, security monitoring, and interactive media. Despite the system's strong performance, challenges such as computational demands in high-resolution video processing were identified, highlighting the need for further optimization. Future work will focus on optimizing the system for different hardware platforms, incorporating multimodal inputs, and refining deep learning models to address computational bottlenecks. This research contributes to advancing HCI by providing insights into the integration of deep learning for real time video content analysis, which is pivotal for enhancing the interactivity and adaptability of intelligent multimedia systems.