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Social media X (Twitter) has become the main platform for the Indonesian public to express opinions, including on the trend of 'kabur aja dulu' (let's just run away for a bit). This research aims to classify the sentiments of the public using the Naïve Bayes and Support Vector Machine (SVM) methods, and to compare the accuracy of both in sentiment analysis. Data was collected via the Twitter API with the hashtag #kaburajadulu, resulting in 2,067 tweets, which, after the cleansing process and manual labeling, left 385 data points. The analysis process followed the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Model evaluation was conducted using a confusion matrix with accuracy, precision, and recall metrics. The classification results show that 82% of tweets have a positive sentiment and 18% negative. The Naïve Bayes algorithm achieved an accuracy of 86.49%, slightly lower than SVM, which reached 88.05%. In conclusion, Support Vector Machine is more effective in sentiment classification on public opinion data. This research contributes to the digital mapping of public opinion and recommends the development of automatic labeling methods as well as the exploration of advanced algorithms in the future.

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.

QRIS (Quick Response Code Indonesian Standard) has become a widely used national digital payment standard. User satisfaction with this service needs to be monitored continuously to ensure its sustainability. This study aims to predict the level of QRIS user satisfaction based on their experiences and perceptions expressed organically on the Twitter social media platform. The method used is sentiment analysis with the Naive Bayes classification algorithm implemented using RapidMiner software. The research data was obtained from Twitter user comments collected through web scraping techniques. The text data then went through a preprocessing stage that included cleansing, stopword filtering, stemming, and tokenizing to be prepared as features ready to be processed by the model. The data was divided into training (80%) and testing (20%) subsets for model training and validation. The results showed that the Naive Bayes model was able to predict user satisfaction sentiment with an accuracy of 80.99%. These findings indicate that the model is highly accurate in identifying satisfied comments and sufficiently sensitive in detecting dissatisfaction. This study concludes that sentiment analysis of Twitter UGC data using Naive Bayes is an effective and efficient approach for predicting QRIS user satisfaction in real time. The practical implication of this study is to provide an automatic feedback system for service providers to monitor public sentiment and take targeted corrective actions.

Bullying is a serious problem that can disrupt the learning process and mental development of students, including in Islamic boarding schools. Early detection of bullying is essential to creating a safe and conducive learning environment. This study aims to apply the You Only Look Once (YOLO) algorithm to automatically detect bullying through video recordings in the environment of the SMK Skill Village Islamic School Business Boarding School. The method used involves collecting a video dataset representing various types of bullying behavior, labeling the data, and training an object detection model using the YOLOv5 algorithm. The developed system is capable of detecting and classifying bullying behavior in real- time with detection accuracy reaching [accuracy value if known]. The implementation of this system is expected to assist school authorities and boarding school administrators in monitoring, preventing, and addressing bullying incidents more quickly and effectively, while also serving as an initial step in leveraging artificial intelligence technology to create a safer and more comfortable educational environment.

Usability evaluation plays a crucial role in ensuring the quality of digital systems, particularly in terms of comfort, effectiveness, and ease of use. Instruments such as the System Usability Scale (SUS), User Experience Questionnaire (UEQ), and Heuristic Evaluation (HE) are widely used in modern usability studies. This research conducts a Systematic Literature Review (SLR) to identify patterns and trends in the use of these instruments. A total of 27 initial studies were collected, and 16 were selected through the PRISMA screening procedure. The findings show that UEQ is the most frequently used instrument, especially in Learning Management Systems (LMS) and academic platforms, while SUS is commonly applied to mobile applications and digital libraries for rapid usability assessment. HE is effective in revealing fundamental interface issues such as non-intuitive navigation and layout inconsistencies. Overall, digital systems perform well in Efficiency and Perspicuity, but consistently show low scores in Novelty. This study provides an integrative knowledge map that highlights cross-instrument insights and supports the development of more intuitive, innovative, and user-centered digital systems

Dijkstra's algorithm is one of the algorithms in graph theory that is used to solve the problem of the shortest path of a graph at each vertex that has a non-negative value. This algorithm was discovered by Edsger Wybe Dijkstra, a scientist from the Netherlands. The search for the shortest route for product delivery can be calculated through the application of the Dijkstra algorithm in the problem being faced. The problem of decision making for selecting the shortest route is still manual, so it experiences several obstacles, including the absence of a systematic and computerized system to assist the decision-making process in determining the route for shipping goods, the determination of shipping routes still depends on manual estimates so that the time taken between deliveries becomes inconsistent, the operational costs of shipping are relatively high because there is no optimal route determination system. Facing these problems, a system is needed that can minimize delays and increase effectiveness in shipping goods, namely determining the shortest route using the Dijkstra algorithm. This system works by finding various alternative routes for shipping goods at PT AMSA to address various structured and unstructured problems using data and models. To process this data and models, a method called the Dijkstra algorithm is required. Based on the description above, researchers will create a method for determining the shortest route for shipping goods at PT AMSA using the Dijkstra algorithm to facilitate the company's process of determining the shortest route.

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.

Social media has evolved into a significant platform where consumers freely express their opinions, experiences, and levels of satisfaction regarding various products, including those offered by Micro, Small, and Medium Enterprises (MSMEs). The comments and reviews shared by customers on these platforms contain diverse sentiments that can serve as valuable indicators of how consumers perceive product quality. Understanding these sentiments is crucial for MSME owners, as it allows them to evaluate their products and adapt to market expectations more effectively. This study aims to analyze customer sentiment toward MSME products on social media by utilizing the Naïve Bayes algorithm, a widely used classification method in text mining. The data used in this research consist of customer comments collected from various social media platforms. The research process involves several stages, including data collection, manual labeling of sentiments, text preprocessing (such as tokenization, case folding, and stopword removal), and splitting the dataset into training and testing subsets. Subsequently, the classification process is carried out using the Naïve Bayes algorithm to categorize sentiments into positive, negative, and neutral classes. The results of this study demonstrate that the Naïve Bayes method is effective in classifying customer sentiments with a satisfactory level of accuracy. These findings provide a comprehensive overview of consumer perceptions regarding the quality of MSME products. Furthermore, this research is expected to assist MSME business owners in understanding customer feedback more systematically and using it as a basis for improving product quality and enhancing customer satisfaction in a competitive digital marketplace.

The development of digital technology and artificial intelligence (AI) has increased youth interaction in cyberspace, while also elevating the risk of digital crimes, both as perpetrators and victims. This community service program AIms to strengthen legal awareness and AI-based self-protection skills among students of SMKN 1 Kaligondang, \Purbalingga Regency. The implementation methods include interactive workshops, digital security trAIning, case simulations, and the use of a mini AI assistant as a preventive educational tool. Evaluation was conducted through pre-test and post-test assessments, participatory observation, and participant reflection. The results indicate an improvement in participants’ understanding of digital law, particularly regarding the provisions of the Law on Electronic Information and Transactions and its relevance to the Indonesian Criminal Code. In addition, participants’ technical skills in securing digital accounts, identifying phishing attempts, and responding to cyber risks have also improved. The use of AI has proven to enhance participation and contextual understanding. Overall, the program is effective in fostering legal awareness, improving digital protection capabilities, and shaping responsible behavior in cyberspace.

The use of AI in today’s era has significantly expanded across various sectors of life, including the education sector. The use of AI in education is quite helpful in improving the quality of learning by providing innovative teaching materials, real-time feedback, and fostering student creativity; however, in practice, there are numerous instances of misuse of AI, such as for criminal activities. Such practices are clearly counterproductive to the learning process and threaten the quality of the nation’s future generations. The objective of this community service initiative is to deepen students’ understanding of AI from a legal perspective and to enhance the quality of learning. The methods employed include participatory-based education, encompassing lectures, Q&A sessions, and statements of commitment. The results of this community service initiative include an enhanced understanding of AI and its proper use. Additionally, students have committed to using AI ethically, in accordance with existing legal frameworks, and with a focus on improving the quality of learning without compromising their foundational and social skills, so that in the future they can compete effectively both nationally and internationally.

Libraries play a crucial role in broadening the public's knowledge horizons by improving literacy skills. However, the reality at the Samaturu Library in Jambu Malea Village shows that manual management still dominates, leading to various administrative obstacles such as data irregularities, the risk of losing important information, and inefficiencies in tracking daily activities. This condition demands digital transformation to optimize public services at the village level comprehensively and sustainably. This research focuses on the development of a web-based library information system specifically designed to modernize administrative processes to be more systematic, fast, and organized. Built using the PHP programming language and MySQL database, this platform offers an intuitive interface for both staff and members. Its superior features include book data management, membership, activity agendas, announcements, and digitizing the borrowing and return process. Furthermore, the system is capable of automatically generating periodic reports to facilitate library performance evaluation. Based on the results of comprehensive testing using the Black Box Testing method, all system functionality was proven to operate optimally according to the required specifications. Direct user trials also showed very positive responses regarding aspects of ease of navigation and operational time efficiency. The implementation of this system is expected to improve the effectiveness of internal management, accelerate the quality of services to library users, and become a major catalyst in realizing superior digital literacy governance in rural areas.

The coffee shop industry in Indonesia is experiencing rapid growth that requires business owners to optimize data-driven strategies. This study aims to analyze customer preferences at Semanis Coffee and Resto using data mining methods  to support more effective business decision-making. The method used is Market Basket Analysis with the FP-Growth algorithm for association rule mining and the K-Means algorithm for customer segmentation. The research data consists of 672 sales transactions during the March-May 2025 period. The results of the association analysis with a minimum support of 0.004 and a minimum confidence of 0.2 resulted in five valid rules with a lift ratio above 1. The strongest rule is the combination of Americano→Milk Choco with a confidence of 42.9% and an elevator ratio of 5.229, indicating a strong linkage between products. The most popular products are Milk Choco (10.8%) and Americano (8.5%). Customer segmentation analysis identified three clusters: Cluster 0 (Loyal Customers) 80% with high frequency but low transaction value; Cluster 1 (Occasional Customers) 10% with low activity; and Cluster 2 (Large Buyers) 10% with high transaction value but low frequency. This study concludes that product bundling strategies, loyalty programs, reactivation campaigns, and premium services can be applied to increase the effectiveness of coffee shop businesses.

The distribution of Village Fund Direct Cash Assistance (BLT) is a government program aimed at alleviating the economic burden on affected communities. However, the data collection, verification, distribution, and reporting processes are still largely manual, potentially leading to data errors, duplication, and distribution delays. This study aims to design and implement a web-based BLT Village Fund information system that can simplify recipient data management, monitor distribution, and generate accurate reports. The methods used include database design, module implementation for sub-district administrators, village operators, treasurers, and village officials, and system testing using the Black Box method to evaluate functionality. The system is equipped with features for managing recipient data, verifying eligibility, sending data to the treasurer, inputting proof of transfer, uploading proof of distribution, recapitulating distribution, and printing reports. The test results show that all modules run according to specifications, are able to handle various input conditions, and produce accurate output. Field trials show that the system is easy to use, improves administrative efficiency, accelerates the distribution process, and supports transparency and accountability. In conclusion, the Village Fund Direct Cash Assistance (BLT) information system is effective as a tool for data management and aid distribution, minimizing errors, and supporting the smooth operation of the BLT program at the village level.

Correspondence administration is vital for supporting operational efficiency in schools. However, manual management often leads to issues such as data retrieval delays, recording errors, and the risk of lost archives. This research aims to design and develop a Web-Based Administration Data Monitoring Information System using QR Codes at UPTD SMAN 3 Majene. The study employs the Research and Development (R&D) method with the Waterfall model, encompassing requirements analysis, system design, implementation, testing, and maintenance.The system involves three primary users: Admin/Administrative Staff, Teachers, and the Principal. QR Codes are implemented as digital identities to facilitate easier archive tracking and retrieval. Results indicate that the system enhances effectiveness and efficiency in correspondence management, accelerates letter request services, and simplifies data monitoring for school management. This system is expected to provide a solution for achieving orderly administration at UPTD SMAN 3 Majene. Furthermore, QR Code integration ensures document validity and digital archive security, supporting a more modern, transparent, and sustainable school governance transformation.

Marriage confirmation services are often hampered by manual procedures, leading to delays and administrative errors. This study aims to design and implement a web-based Marriage Confirmation Information System to facilitate registration, document uploads, and online monitoring of verification status and court schedules. The system was developed using the PHP programming language and MySQL database using the Waterfall method, encompassing analysis, design, implementation, and testing. The results show that the system is capable of optimally performing all managerial functions, from document validation to report automation. Black Box testing confirmed that all features functioned as expected and were easy to use. In addition to improving bureaucratic efficiency, the system ensures data security through login encryption and provides transparency through real-time status tracking. The presence of this platform significantly saves time and transportation costs for the public, as bureaucratic processes can be monitored from anywhere. In conclusion, this system successfully supports the digitalization of public services and improves the quality of administration within the Office of Religious Affairs (KUA) and Religious Courts, thereby realizing more effective, efficient, accountable, and modern governance for all levels of society

Community dues are a form of public participation in supporting social activities and neighborhood development. However, low participation in paying dues remains a common issue in many communities. This study aims to analyze the role of community awareness and the management system of youth organizations in improving the effectiveness of dues collection in Neighborhood 15, Geluran, Sidoarjo. The method used is Asset-Based Community Development (ABCD), with data collected through observation, interviews, and focus group discussions. The results show that increased community awareness, supported by continuous socialization, has positively influenced residents’ perceptions, transforming dues from being seen as a burden into a shared responsibility. In addition, improvements in management systems, including transparent financial recording and regular reporting, have strengthened public trust. The findings also indicate a strong relationship between awareness and management systems, where transparency enhances trust and encourages higher participation. Furthermore, the ABCD approach successfully utilizes local assets such as youth involvement and social solidarity, leading to strengthened community engagement and the emergence of local leadership. Despite these improvements, some residents still show inconsistent participation, indicating the need for continuous efforts to sustain the program. This study highlights the importance of integrating awareness-building and transparent management to achieve sustainable community participation.

Advances in information technology have brought significant changes to how Micro, Small, and Medium Enterprises (MSMEs) conduct their business operations, particularly in the areas of marketing and sales. However, Stretto Pizza MSME still relies on manual processes for ordering and transaction recording, which results in limited marketing reach, suboptimal data management, and an increased risk of errors in recording. This study aims to develop a web-based e-commerce information system that can integrate sales processes and data management more effectively. System development was conducted using the Waterfall method, which includes the stages of requirements analysis, design, implementation, testing, and maintenance. The system was built using the PHP programming language with MySQL database support. System testing was performed using the Black-Box Testing method to ensure that every feature functions according to user needs. The research results show that the designed system is capable of supporting product ordering processes, customer data management, sales transactions, and report generation more efficiently. With this system, it is hoped that the Stretto Piza SME can improve its operations while expanding its marketing reach through the use of digital technology.

The Alpha generation is currently facing big challenges in balancing virtual activities and real life, where the high intensity of using devices often shifts priorities for worship, especially during the month of Ramadan. This dependence on digital screens triggers individualistic attitudes and reduces children's levels of religious participation. Therefore, this community service activity aims to increase the capacity of parents and children to implement time management and limit screen time effectively. The method used is a social marketing approach combined with participatory observation techniques, interactive visual promotions, and educational simulation games which were implemented in the Imaduddin Mosque Youth Hall, Medokan Ayu, Surabaya. The results of the activity showed a significant increase in understanding among children and parents regarding the importance of setting time limits for using devices. Generation Alpha children responded positively by enthusiastically setting their own daily priority scale between playing and worshiping without feeling forced. The implications of this activity include long-term changes to the social and spiritual dynamics of residents, the creation of a more harmonious family environment with minimal conflict related to confiscation of devices, as well as the revival of the function of mosques as centers of communal interaction for the younger generation. It is hoped that the independence of citizens in educating children will be able to build the foundation for a future generation that is digitally intelligent, mentally healthy and has a strong religious character.

This study aims to design and develop a web-based New Student Admission System (PPDB) Website at MIS Chairul Bariyyah Medan Krio to overcome the limitations of manual registration systems. The method used is Agile, as it supports iterative, flexible, and adaptive system development according to user needs. Data collection techniques include observation, interviews, literature study, and documentation. The system is developed using the Python programming language with the Django framework and MySQL database. The results show that the developed system improves the efficiency of the registration process, minimizes data recording errors, and facilitates real-time management of applicant data. In addition, the Website also serves as an information and promotional medium for the school that can be accessed anytime. Based on Blackbox testing and User Acceptance Test (UAT), the system is proven to run well and is easy to use. Therefore, this web-based PPDB system is effective in improving the quality of Administrative services at MIS Chairul Bariyyah.

This community service activity aims to describe the process and impact of the UMKM-Based Sepak Takraw Tournament for all of West Sumatra (Piala Limkos Sedunia) held in Nagari Sikucur, V Koto Kampung Dalam District, Padang Pariaman Regency, West Sumatra, as part of the Regular KKN Program of Universitas Negeri Padang for the January–February 2026 period. The method used is participatory action research with a qualitative descriptive approach. Activities were carried out for 30 days (January 9–February 8, 2026), involving 32 teams from 19 districts/cities across West Sumatra and hundreds of local UMKM actors. KKN students served as media partners, live streaming hosts on social media, and documentation committees. Results indicate that the tournament successfully stimulated the local economy with significant financial turnover through a 14-day UMKM bazaar, increased promotion of traditional sepak takraw sports, and strengthened social cohesion among nagari residents. The activity also contributed as an athlete development platform ahead of the West Sumatra Provincial Sports Week (PORPROV) 2028. The integration of sport events with UMKM empowerment has proven to be an effective strategy for improving rural community welfare.