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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.

This study aims to develop a sentiment classification method for YouTube user comments related to the game Love and Deepspace using the Naïve Bayes algorithm, focusing on improving the text data processing and understanding user perceptions. Comment data were collected through scraping from YouTube videos, followed by preprocessing including text cleaning, normalization, stopword removal, stemming, and translation into English. Initial labeling was conducted using TextBlob, then the data were randomly sampled for training the Naïve Bayes model. Evaluation involved comparing sentiment distributions and visualization using Word Cloud and bar charts. The Naïve Bayes model achieved an accuracy of 77.36% in sentiment classification. The sentiment distribution shows differences between TextBlob (positive: 1,011, neutral: 1,312, negative: 575) and Naïve Bayes (positive: 901, neutral: 1,627, negative: 370), with Naïve Bayes being more conservative. The Word Cloud visualization identifies dominant words such as "bang," "game," and "main," while the bar chart shows the largest proportion of neutral sentiment. Naïve Bayes is effective for sentiment classification on informal comment data, with significant differences from rule-based methods like TextBlob. This research contributes to the development of text data processing techniques and user perception analysis, as well as opening up optimization opportunities with other algorithms like SVM for better accuracy.

This study aims to analyze the influence of work discipline, professionalism, and organizational culture on organizational commitment among employees in the Public Service Sector. Organizational commitment is an important factor that reflects employee loyalty and attachment to the institution, while the three independent variables are considered as internal factors that can strengthen this commitment. The method used in this study is associative quantitative with a multiple linear regression approach. Data were collected through distributing questionnaires to 46 respondents based on probability sampling techniques. Data processing and analysis in this study used SPSS software version 27. Based on the results of the study, it was found that there is a significant influence of work discipline on organizational commitment. In addition, professionalism has a significant effect on organizational commitment, organizational culture has a significant effect on organizational commitment. Based on simultaneous hypothesis testing, it is proven that work discipline, organizational culture, and professionalism jointly influence organizational commitment. While the amount of contribution is 54% to organizational commitment, while the remaining 46% of organizational commitment is influenced by other factors such as leadership style, motivation, competence, rewards, job satisfaction, and employee performance. Thus, this study confirms that to increase organizational commitment, government agencies need to strengthen a healthy work culture, enforce discipline, and encourage a professional attitude in the work environment.

This study aims to determine the effect of product quality and service quality on consumer purchasing decisions, both partially and simultaneously, at Chickenday Restaurant Trenggalek. The method used is a quantitative approach with data processing in numerical form to objectively test the relationship between variables. The research was conducted at Chickenday Restaurant, located on Jl. Soekarno Hatta No. 20 Trenggalek. The research population consists of all consumers during the period of May–June 2025, the exact number of which is unknown. The sampling technique utilized probability sampling with a simple random sampling method, resulting in 96 respondents based on the Lemeshow formula. The data include primary data through questionnaires and observations, as well as secondary data from various sources. Data analysis was performed using multiple linear regression, t-test, and F-test. The results indicate that product quality and service quality have a positive and significant effect on purchasing decisions, both partially and simultaneously, with service quality being the more dominant variable.

Information technology advancements have sparked a digital revolution in village government operations, especially in tracking and assessing village officials' performance. Performance evaluation is still done by hand in many village government organizations, which frequently causes reporting delays, ineffective data processing, and a lack of transparency in the evaluation process. In order to facilitate a more efficient and organized monitoring and performance evaluation process, an information system therefore required. In order to support the management village apparatus data, performance assessment procedures, and integrated reporting, this study intends design and develop a web-based monitoring system for assessing the performance village officials. To represent the system processes in an organized way, the system design is modeled using the Unified Modeling Language (UML), which includes use case diagrams, activity diagrams, and sequence diagrams. To make sure that every system function performs in accordance with user requirements, system testing is carried out using the Black Box Testing method. The findings demonstrate the system's ability handle village apparatus data, carry out automated performance evaluation procedures, compute final scores using assessment indicators, and produce precise performance reports. It is anticipated that the system's implementation will enhance the village officials' performance monitoring procedure's efficacy, efficiency, transparency, and structure.

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.

Information systems are an important element in the business world that helps businesses increase work efficiency and reduce human error. In practice, many businesses still produce financial reports manually. Manual preparation of financial reports is inefficient and tends to have a higher potential for delays, lateness, or errors in data entry. This makes the financial reports presented inaccurate and unreliable as a basis for making decisions. To address this problem, a community service program was implemented by designing an accounting system using Microsoft Access, which provides various features that can help users in data processing. The method carried out starts with observation, system design, and evaluation by the partner. The result of implementing this program is that the system designed in Microsoft Access successfully accelerated the work process for partners, making the preparation of financial reports more efficient and timely. The financial reports presented also became more accurate and reliable.      

The purpose of this study is to describe the marketing strategies and adaptability of “cakar bongkar” (second-hand clothing) traders in responding to market dynamics in Nabire City. This study employs a qualitative approach, as it is appropriate for addressing research problems that require an in-depth understanding of the socio-economic realities in Kalibobo Market and Karangtumaritis Market in Nabire City. The data used in this study consist of primary data as the main source and secondary data as supporting and relevant information. Data collection methods are carried out using the triangulation principle, namely through direct observation, in-depth interviews, and documentation. Informants are selected using snowball sampling and purposive sampling methods to obtain comprehensive and representative information. Data processing and analysis are conducted through the stages of data reduction, data presentation, and systematic conclusion drawing. The results of the study indicate that the informants apply five (5) marketing strategies in selling second-hand clothing (cakar bongkar), namely: (1) sourcing strategies, (2) pricing and promotion strategies, (3) word-of-mouth marketing strategies, (4) strategies for building social relationships with customers, and (5) product display strategies using prototype tables to attract consumer attention.

This study aims to analyze the influence of e-marketing and customer engagement on marketing performance. Analyze the influence of e-marketing and customer engagement, customer trust on marketing performance. The sample size was 78 respondents using SEM PLS for data processing. The findings of this study are that there is a significant positive influence between e-marketing on marketing performance; there is a significant positive influence between customer engagement on marketing performance; there is a significant positive influence between e-marketing on customer trust; there is a significant positive influence between customer engagement on customer trust; there is a significant positive influence between customer trust; on marketing performance

This research aims to see the effect of Total Asset Turnover and Debt To Equity Ratio (DER) on Return On Assets (ROA) at PT Indofood Sukses Makmur Tbk which is listed on the BEI in 2010 - 2023. The data used in this research is data secondary in the form of the annual financial report of the company under study. This research uses quantitative data and the data source used is secondary data with an analysis method using multiple linear regression methods with data processing using SPSS v.25. The results of hypothesis testing (T Test) partially state that the Total Asset Turnover variable does not have a significant and positive influence on Return On Assets (ROA) and the Debt To Equity Ratio (DER) variable has a significant and negative influence on Return On Assets (ROA). Simultaneous F Test results of the Total Asset Turnover and Debt To Equity Ratio (DER) variables show a significant influence between Total Asset Turnover and Debt To Equity Ratio (DER) on Return On Assets (ROA).

 Road infrastructure development is an important factor in supporting community mobility and equitable regional development. However, road construction data management in Palembang City still faces various problems, such as scattered data, lack of integration, and not yet presented in a map-based visual form. This study aims to implement a Geographic Information System (GIS) as a medium for location mapping and presenting road construction data information in Sematang Borang and Kalidoni Districts, Palembang City. The methods used include field observation, road construction data collection and verification, GPS coordinate point retrieval, spatial and non-spatial data processing, and the development of a GIS system based on interactive digital maps. The results of the study indicate that the system is able to present road construction information in a structured, accurate, and easily accessible manner. This GIS helps improve the efficiency of the monitoring process, data management, and preparation of road construction reports at the Palembang City Public Works and Housing Agency. Thus, the application of GIS can be a supporting solution in decision-making and encourage the digitalization of road infrastructure data management.

The rapid advancement of Information and Communication Technology (ICT) has accelerated the digital transformation of public services, including land administration. However, public complaint services at the Land Office of Southwest Sumba Regency still encounter challenges such as unstructured complaint procedures, manual data processing, risk of data loss, and limited public access to clear information. These issues highlight the need for an innovative and accessible complaint information system. This study aims to design and implement a chatbot-based public complaint service information system to enhance accessibility, transparency, and service effectiveness. A qualitative research method with a system development approach was applied. Data were obtained through interviews, observations, and documentation. The system was developed using a rule-based approach with a Finite State Machine (FSM) algorithm and implemented through the Typebot.io platform. The findings indicate that the chatbot provides structured, consistent, and user-friendly information, reduces manual workload, and improves public readiness before submitting complaints directly, while supporting future integration and system enhancement.

The assessment of students’ academic performance in higher education is generally still dominated by conventional numerical approaches, which are less capable of representing qualitative and subjective variables such as classroom activeness and student participation. These approaches often result in evaluations that are not holistic and do not fully reflect students’ overall academic achievements. Therefore, this study aims to analyze the concept of fuzzy logic as a support tool for assessing students’ academic performance in higher education, with a case study of students at Universitas Asahan. The research method employs a descriptive qualitative and quantitative approach by applying Mamdani fuzzy logic. The input variables consist of exam scores, assignment scores, and classroom activeness, while the system output is the category of academic performance, namely sufficient, good, and very good. The sample data consist of ten active undergraduate students from Universitas Asahan. The data processing stages include fuzzification, the construction of fuzzy rules (rule base), fuzzy inference, and defuzzification using the centroid method. The results indicate that fuzzy logic is able to integrate quantitative and qualitative variables and accommodate uncertainty in academic assessment. The resulting evaluation is more proportional and realistic compared to conventional assessment methods based solely on average scores. Therefore, fuzzy logic can be considered an effective and flexible alternative approach to support student academic performance assessment systems in higher education.

The quality of assessment instruments plays an important role in determining the accuracy of measuring student learning outcomes in science learning in elementary schools. A good test instrument must meet certain criteria, such as validity, reliability, difficulty level, and discrimination power. This study aims to analyze the quality of daily science test items in grade VIc elementary schools based on these four criteria. The study used a quantitative. The subjects were 19 sixth-grade students, while the instrument analyzed consisted of 25 multiple-choice questions. Data processing and analysis were carried out using Microsoft Excel to calculate item validity through item correlation with total score, test reliability using internal consistency, difficulty level index, and discrimination index. The analysis results showed that 17 questions (68%) were declared valid, while 8 questions (32%) were invalid and needed to be improved. The results of the reliability test indicated that the test instrument had good reliability and was suitable for use as a measuring tool for student learning outcomes. Judging from the level of difficulty, 20 questions (80%) were moderate and 5 questions (20%) were easy, indicating a relatively balanced level of difficulty. Based on the discrimination power, 16 questions (66%) had very good discrimination power, 4 questions (16%) were good, 4 questions (16%) were sufficient, and 1 question (4%) was poor. Based on these findings, it can be concluded that the quality of the sixth grade science daily test questions is classified as good and the test instrument is suitable for use, but improvements are still needed on invalid questions and those with low discrimination power so that the quality of the assessment is more optimal. This study emphasizes the importance of teachers' abilities in compiling and analyzing test items to ensure that the assessment of science learning is objective, valid, and reliable.

The primary focus of this research is to evaluate the influence of Good Corporate Governance (GCG), profitability levels, and entity scale on the market value of coal mining companies listed on the Indonesia Stock Exchange (IDX) between 2021 and 2023. This study adopts a quantitative design by utilizing secondary data from the official IDX website, where 8 companies were selected as samples from a total population of 34 coal sub-sector companies through purposive sampling techniques. Data processing was carried out through panel data regression analysis using Eviews 12 software. The research data indicates that, independently, the implementation of good corporate governance and the level of profit acquisition do not contribute significantly to determining the value of the entity. Conversely, company size is proven to have a significant negative impact. Simultaneous testing confirms that these three independent variables collectively have a significant effect on company value. These findings indicate the need for strategies that consider factors beyond good corporate governance and profitability in efforts to increase company value, such as operational efficiency and proper asset management.

This study develops a computational-based system to identify individual potential through the analysis of signature patterns using Artificial Neural Networks (ANN) and the Backpropagation algorithm. The research aims to explore and examine the effectiveness of applying ANN in recognizing and identifying signature patterns that are assumed to be related to an individual’s potential. In the data processing stage, Principal Component Analysis (PCA) is employed as a dimensionality reduction and feature extraction technique to optimally obtain the main characteristics of signature images. The system performance evaluation is conducted using a total of 80 signature images, consisting of 60 training data and 20 testing data. This study analyzes two network architecture configurations, namely a model with one hidden layer and a model with two hidden layers. The experimental results show that both network configurations achieve the same accuracy level of 92.5%. These findings indicate that the use of Artificial Neural Networks with the Backpropagation algorithm is effective in producing high accuracy in the signature pattern recognition process. Furthermore, the developed system has broad potential applications in the field of personal identification, such as employee evaluation, selection systems, and other applications across various organizational and industrial sectors.

Real time decision making applications, such as those used in autonomous vehicles, smart cities, and industrial IoT, require fast, scalable, and accurate analytics to ensure timely responses and optimized operations. Traditional cloud-based systems face significant challenges in meeting these requirements due to high latency, limited scalability, and bottlenecks in data processing. This study explores the use of a hybrid Edge Cloud architecture to optimize End to end machine learning (ML) pipelines for real time applications. The proposed system offloads time-sensitive tasks to edge devices, while computationally intensive processes are handled by the cloud, ensuring efficient use of resources and reduced latency. Experimental results demonstrate that the hybrid model reduces inference latency by up to 70% compared to cloud-only systems, while maintaining model accuracy and increasing throughput. Additionally, the scalability of the hybrid architecture is highlighted, as it can handle large-scale data streams and adapt to varying workloads. The findings show that hybrid Edge Cloud architectures are well-suited for applications where fast decision making is critical, such as autonomous systems and real time analytics in smart cities. However, challenges remain in managing resources across edge and cloud systems, particularly in balancing computational loads and ensuring system reliability. Future research should focus on optimizing task partitioning, integrating advanced edge AI models, and exploring the use of 5G networks to enhance performance further. Overall, the study demonstrates the potential of hybrid Edge Cloud systems in overcoming the limitations of traditional cloud-based ML pipelines and provides insights into the future of real time data processing.

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

The increasing demand for real time parallel processing in cloud computing environments necessitates the development of more efficient and fault-tolerant scheduling algorithms. Traditional scheduling methods, such as static algorithms, often fall short when handling dynamic workloads and system failures, leading to increased task latency and reduced system performance. In contrast, adaptive scheduling algorithms dynamically adjust to changes in system conditions and workloads, ensuring timely task completion and optimized resource utilization. This study evaluates the performance of adaptive scheduling algorithms in real time cloud environments, focusing on key factors such as task latency, system resilience, and fault tolerance. Simulation experiments were conducted using cloud computing models that incorporate fault injection scenarios, including network failures and virtual machine crashes. The results show that adaptive algorithms significantly outperform traditional static schedulers in terms of task latency reduction and improved system resilience. These algorithms demonstrated better fault recovery times and ensured consistent real time performance, even under failure conditions. The findings highlight the advantages of adaptive scheduling in cloud environments, particularly for applications requiring rapid data processing and high system reliability. Despite the promising results, challenges remain regarding the scalability and complexity of these algorithms in large-scale cloud systems. Further research is needed to optimize adaptive scheduling algorithms for efficiency, scalability, and comprehensive performance evaluation, taking into account factors such as energy consumption, cost, and reliability. This research contributes to advancing cloud computing infrastructures that can dynamically handle real time tasks and maintain high performance under varying workloads and failures.

This study compares the scalability and maintainability of three prominent programming paradigms-functional programming (FP), object-oriented programming (OOP), and declarative programming (DP)-in the context of distributed data processing systems. The research aims to evaluate how each paradigm performs under increased data volume and its ability to handle complex operations, while also assessing the ease of maintenance through code readability, modularity, and the flexibility of updating and debugging. The study employs a comparative experimental design, implementing identical data processing tasks, such as data aggregation, filtering, and transformation, across each paradigm. Key findings indicate that FP and DP outperform OOP in terms of scalability, with their stateless nature and high-level abstractions enabling efficient parallel processing and task distribution. FP, with its emphasis on immutability and concurrency, and DP, with its focus on describing desired outcomes rather than implementation specifics, both demonstrate superior performance in handling large datasets. However, while OOP excels in modularity and flexibility, its reliance on mutable state and shared resources hampers its scalability in distributed environments. In terms of maintainability, both FP and DP offer clearer, more maintainable code due to their abstraction levels, making them easier to update and extend. OOP, while modular, presents challenges in managing mutable state, complicating maintenance. This paper concludes with practical recommendations for developers on when to use each paradigm based on system requirements and suggests areas for future research, such as hybrid paradigms and long-term maintainability studies in real-world applications.