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This study is motivated by the increasing complexity of global culture, which demands a value system capable of providing direction and meaning in human life. In this context, Islam is viewed as having the potential to serve as a global value system through a cultural concept grounded in revelatory values. This study aims to analyze the concept of culture from an Islamic perspective as a global value system by examining the thought of Ali Ahmad Madkur. The method used is a qualitative approach based on a Systematic Literature Review (SLR), systematically and critically examining various relevant scientific literature. The results of the study indicate that Islamic culture in Madkur’s thought is built upon two main dimensions: the normative dimension, which is derived from the Qur’an and Sunnah, and the practical dimension as its actualization in social life. Although this approach has strengths in maintaining the integrity of values, it still faces challenges in responding to the complex, dynamic, and hybrid nature of global cultural dynamics. Therefore, a more integrative approach is needed to ensure that Islamic cultural values remain relevant and operational in a global context. This study has implications for strengthening more contextual studies of Islamic culture and developing Islamic education that is adaptive to the changing times.

The development of digital technology has brought about significant changes in various aspects of life, including early childhood faith education. Today’s children are growing up in an environment familiar with digital media, making it necessary to adopt a catechetical approach that keeps pace with the times. This article aims to examine the use of digital catechesis as a tool for early childhood faith education. This study employs a qualitative method with a literature review approach, gathering data from books, scientific journals, research articles, Church documents, and other relevant sources. The data is analyzed using descriptive-qualitative methods to understand the benefits, challenges, and role of digital catechesis in fostering children’s faith. Research findings indicate that digital catechesis can serve as an effective, engaging, and interactive medium for helping children learn about the teachings of the faith from an early age. The use of animated videos, religious songs, educational images, and learning apps can enhance children’s interest in learning, attention, and understanding of Catholic faith values. Furthermore, digital catechesis also assists the Church, families, and schools in providing faith education that is more contextual and aligned with the world of today’s children. However, the use of digital media still requires the guidance of parents, teachers, and faith mentors so that children receive proper direction and are protected from the negative impacts of technology. Thus, digital catechesis is a relevant tool in the faith education of young children when used wisely and purposefully. The presence of digital media does not replace the role of faith educators but serves as a tool that enriches the process of proclaiming the faith in the modern era.

This study aims to analyze public sentiment toward the hashtag #KaburAjaDulu, which has circulated widely on the social media platform X (formerly Twitter). The hashtag reflects the growing anxiety among the public, especially younger generations, regarding socio-political issues in Indonesia. The data were collected using web scraping techniques, focusing on user-generated tweets that contain the hashtag. A comprehensive text preprocessing phase was conducted to clean the raw data by removing irrelevant elements such as URLs, emojis, numbers, and punctuation. The research applies a hybrid classification approach using a combination of Support Vector Machine (SVM) and Random Forest algorithms to categorize sentiment into three classes: positive, negative, and neutral. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the classification. The study aims to demonstrate that combining algorithms can improve classification performance compared to using a single algorithm. This research contributes to the field of sentiment analysis and provides valuable insights for researchers, policymakers, and social observers in understanding public opinion trends in digital media.

Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.

This study discusses a student concentration detection system using Convolutional Neural Network (CNN) with the MobileNetV2 architecture. The dataset was adapted from Classroom Student Behaviors and mapped into four concentration categories: highly focused, focused, less focused, and unfocused. The system was tested with a 720p webcam and produced real-time detection data. The evaluation results show an overall accuracy of 75.85%, with the highest precision achieved in the focused class (0.9859) and the highest recall in the highly focused (0.9739) and unfocused (0.9811) classes. The confusion matrix indicates that the focused class was detected most consistently, while highly focused and unfocused classes were often misclassified as focused, resulting in lower precision. In real-time testing, the system operated at an average of 7 FPS and worked optimally when students faced the camera directly with sufficient lighting, but its performance decreased significantly at face angles greater than 45°. User evaluation shows that 75% of students rated the detection results as accurate/very accurate with an average satisfaction score of 3.6 out of 5, and 75% felt assisted in recognizing their concentration level. From the teachers’ perspective, most stated that the results were consistent with classroom observations, and all expressed willingness to reuse the system.

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.

The development of social media, especially TWITTER, has become one of the main means for people to express opinions and criticism on various issues, including the performance of law in Indonesia. This study aims to analyze public sentiment towards the performance of law based on TWITTER user comments using the Naïve Bayes algorithm. The research data consists of 1004 comments collected from several videos related to legal topics. The analysis process includes the stages of data crawling, pre- processing (text cleaning, normalization, and tokenization), labeling sentiment into positive, negative, and neutral, and testing the Naïve Bayes model. The results show that the Naïve Bayes algorithm is able to classify sentiment with an accuracy level of 93.73%. The distribution of sentiment from 1004 comments shows that the majority of public opinion is (negative/positive/neutral), which indicates that public perception of the performance of law is still (critical/positive). These findings are expected to be input for related parties to understand public opinion and improve the quality of legal performance in

SR12 Herbal Cosmetics is a company engaged in the field of herbal and skin care. Founded in 2015 byToni Firmansyah, S. Farm., Apt. and Asrianty Salam, Farm. This company has a vision to provide benefits to many people through the herbal and skin care products they produce. SR12 Herbal Cosmetics products are formulated based on research from certified scientists, and have been tested at the Sucofindo Laboratory, are free of mercury and hydroquinone, and have been registered with the Indonesian Food and Drug Supervisory Agency (BPOM RI). SR12 Herbal Cosmetics has several factories in West Java Province and has an extensive distribution network with hundreds of distributors and tens of thousands of partners throughout Indonesia. The goal to be achieved is to produce a management information system model including a management information system for PT SR12 Herbal Cosmetics. The research object chosen is a company in the field of cosmetics and skin care which has its head office in Gunung Sindur, West Java. This selection aims to form a management information system design model that is able to produce relevant and timely information for planning, controlling, decision making and evaluating the performance of activities. For the Web-Based Instagram Content Management Information System Design project to Support SR12 Herbal Cosmetics' Brand Awareness, I used Agile (Scrum) due to the dynamic nature of digital marketing and potential changes to the Instagram API or business needs. This allowed SR12 to get core functionality faster and provide iterative feedback, ensuring the system built was truly relevant to their brand awareness needs.

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.

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.

The growth of information technology has driven changes in consumer behavior, one of which is through e-commerce platforms such as Shopee. This phenomenon has generated a large number of customer reviews, including those for local cosmetic products such as Wardah. These reviews serve as an important source of information for understanding customer perceptions and satisfaction levels. However, manual analysis of large and linguistically diverse datasets is inefficient and potentially subjective. This study aims to implement the multi-category Naive Bayes algorithm to classify the sentiment of Wardah product reviews on Shopee into three categories: positive, negative, and neutral. The data were collected using a web scraping technique and processed through a series of preprocessing stages including case folding, tokenization, stopword removal, stemming, and text cleaning. Subsequently, term weighting was performed using the TF-IDF method prior to classification. Model performance was evaluated using a confusion matrix as well as accuracy, precision, and recall metrics. The results indicate that the multi-category Naive Bayes algorithm achieved an accuracy of 86.00%, a precision of 86.63%, and a recall of 98.24%. This approach can assist business practitioners in objectively understanding customer opinions and support decision-making in business strategy and product development.

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 rise of social media has created a digital public sphere that enables users to express their opinions on social and political issues openly and in real-time. One of the most discussed topics on social media platform X is the trending hashtag #IndonesiaGelap, which reflects public concern and criticism regarding various governmental and societal conditions. This study aims to conduct sentiment analysis on tweets containing the hashtag to determine the overall sentiment trend among users. The method employed in this research is the Naive Bayes classification algorithm, known for its simplicity and effectiveness in text classification. To enhance the model’s performance, Particle Swarm Optimization (PSO) is applied to optimize feature selection and parameter tuning. The dataset consists of public tweets collected via the Twitter API, followed by preprocessing, feature extraction using TF-IDF, and sentiment classification into three categories: positive, negative, and neutral. The results indicate that the integration of PSO significantly improves the classification accuracy of the Naive Bayes model compared to the baseline. The majority of tweets related to #IndonesiaGelap exhibit a negative sentiment, indicating widespread public dissatisfaction and criticism. This research is expected to contribute to a better understanding of public perception and serve as valuable input for stakeholders in addressing social issues in the digital age.

Gesture  recognition  technology  is  used  to  detect  movements  through  image processing,   enabling  computers  or digital devices to understand and interpret human  body  movements  as  input  or  commands.   This  technology  has  great potential  to bridge communication between the deaf community and individuals without   hearing   impairments,    enhancing  interaction  and  enriching  mutual understanding between the two.  However,  the accuracy ofgesture recognition is often  affected  by variations in the distance between hand landmarks.  Based on this problem,  this research proposes a methodfor stabilizing the measurement of distances between landmark points  in gesture recognition through a polynomial regression  approach.   Specifically,   the  distance  between  hand  landmarks  is calculated and stabilized using polynomial  regression to improve the accuracy of gesture recognition.  This method is implemented using the MediaPipeframework to detect and track hands in real-time,  and the OpenCV library to manage video. The  research  results  show  that  this  approach  can  significantly  improve  the stability  and accuracy  of gesture detection.   The developed system successfully detects gestures for  letters A  through F with a high accuracy  rate,  averaging above 98,3%.  The use ofpolynomial regression helps enhance detection accuracy by reducing noise in the landmark data.

Along with the times, computer technology is developing very rapidly. The increasingly rapid development of computer technology means that everyone is required to utilize computer technology in their daily lives. Utilization of technology is one of the implementation roles of scientific disciplines. The reason behind the formation of this research is so that in the future it will become a fun learning concept in the introduction of objects and shapes in children and the motor development of children. children are usually more interested in seeing pictorial text, or pictures that contain lots of color. The Viola Jones method itself was chosen as the research completion algorithm. The Viola Jones method is usually used as a method in research that discusses the detection of objects, faces and others. The Viola Jones method was chosen because it has a high level of accuracy that can reach 100% probability.

Learning the History of Islamic Culture plays an important role within Malaysia’s education system. Its purpose is to help students develop a strong identity and love for their nation. In this subject, students are not merely taught facts or knowledge about past events; they are also taught moral values, ethics, and a wholesome Islamic worldview. Integrating this subject into the education curriculum aims to provide children with a comprehensive understanding of the development of Islam, both across the Nusantara region and throughout Southeast Asia as a whole. This way, students gain a broad perspective. Furthermore, this subject is designed to foster a love for knowledge in children and to encourage them to uphold Islamic teachings throughout their lives. Its teaching methods are adapted to the changing times, ensuring that the material taught remains relevant to current needs. Through learning the History of Islamic Culture, it is hoped that children will understand the role and contributions of Muslim communities in Southeast Asia, and grow up to be individuals of good character, useful to society, and able to live in harmony with others in accordance with Islamic teachings.

The increasing complexity of urban transportation systems demands intelligent and measurable navigation methods. Medan City, the capital of North Sumatra Province, has a dense road network with multiple route options that often confuse road users. Dijkstra's Algorithm, developed by Edsger Wybe Dijkstra in 1959, is a greedy-based computational approach proven effective for solving the shortest path problem on non-negative weighted graphs. This study applies Dijkstra's Algorithm to determine the shortest route from Medan Railway Station to Universitas Negeri Medan (UNIMED). The road network was modeled as an undirected weighted graph with 15 nodes and 16 edges, where edge weights represent actual road distances measured via Google Maps. The graph has a density of 0.152, confirming its sparse graph characteristic. Three alternative routes were identified and analyzed. The algorithm was implemented in Python 3 using the heapq module as a priority queue. Results show that the optimal route is A → B → C → E → F → M → N → O via Jl. M.T. Haryono, Jl. Aipda KS Tubun, Jl. Madong Lubis, and Jl. Prof. H.M. Yamin, with a total distance of 6.64 km. This achieves 99.1% accuracy compared to Google Maps, with a deviation of only 0.06 km. The optimal route is 6.25% more efficient than Alternative Route 1 (7.30 km) and 11.9% more efficient than Alternative Route 2 (7.54 km). The algorithm executes in under 1 millisecond with time complexity O((V+E) log V). These findings confirm Dijkstra's Algorithm as highly effective for medium-scale urban road network optimization.

This study aims to evaluate how petty cash management practices contribute to improving operational efficiency at PT Anugerah Langgeng Berkat Abadi. This research focuses on examining the implementation of the petty cash management system, applied procedures, and its impact on the smooth execution of daily operational activities. The study employs a descriptive qualitative approach, with data collected through interviews, direct observation, and documentation during the internship period. The collected data were analyzed systematically to describe the actual condition of petty cash management within the company. The results indicate that PT Anugerah Langgeng Berkat Abadi implements a fluctuating fund system in managing petty cash. Expenditures are initially recorded manually and then re-entered into the company’s internal digital system to maintain control and accountability. Petty cash is used to finance routine and urgent operational needs, such as office stationery, transportation costs, and other short-term expenditures. The company has established standard operating procedures governing the use, recording, and accountability of petty cash. Several challenges were identified, including delays in the disbursement and reimbursement process, which may affect time efficiency. However, overall, the petty cash management system is considered effective in supporting short-term operational needs without disrupting the stability of the company’s main cash. This study concludes that systematic and well-controlled petty cash management plays an important role in the company’s cost efficiency strategy and supports daily operational activities. These findings align with strategic management principles, where appropriate financial decision-making contributes to the achievement of long-term organizational objectives.

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 research investigates the effect of the amount of MSMEs, the number of buildings, and GRDP on regional tax revenue in selected cities and regencies in South Sulawesi, driven by the inconsistency between the growth of economic potential and the realization of tax revenue, where increases in MSMEs, buildings, and GRDP are not always followed by higher tax receipts. The study aims to analyze the effect of these variables and identify the most significant factors contributing to regional fiscal capacity. A quantitative approach is employed using panel data that combine time series and cross-sectional data from 2015-2024, analyzed through panel data regression with model selection based on Chow, Hausman, and Lagrange Multiplier test. The results show that partially, MSMEs and the number of buildings do not have a significant effect on tax revenue, while GRDP has a positive significant impact; however, simultaneously, all variables significantly influence tax revenue, as indicated by a high Adjusted R-squared value. These findings suggest that economic growth, as proxied by GRDP, plays a more dominant role in increasing tax revenue compared to the mere increase in the number of MSMEs and buildings, implying that optimizing tax revenue requires not only expanding economic potential but also enhancing tax compliance, administrative efficiency, and the quality of economic growth.