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This study aims to develop a financial saving application to improve the saving habits of students, particularly in Islamic boarding schools, through an adaptive challenge approach. The system integrates a mobile iOS application with a backend service and Large Language Model (LLM) processing via Ollama. Transaction data entered by users is processed by the backend to generate contextual and personalized saving challenges, applying Reinforcement Learning concepts in an adaptive and data-driven manner. The research adopts a descriptive quantitative method using surveys and system testing with 50 respondents. Results indicate that the application functions as designed, with no significant bugs detected. User evaluation shows high satisfaction, with an average score of 4.3 out of 5, covering ease of use, interface design, and increased awareness of saving. The combination of gamification, reward systems, and adaptive personalization successfully motivates users to save regularly. This system demonstrates the potential of integrating AI-driven personalization to strengthen financial literacy and healthy financial habits among students in a fun and interactive way.methods, and a summary of the results. The abstract should end with a comment about the significance of the results or conclusions brief.

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.

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.

Golf participation has increasingly become a lifestyle-oriented recreational activity that combines physical exercise, social interaction, and personal identity. However, participation decisions are not only shaped by individual interest, but also by demographic readiness, psychographic orientation, digital promotional exposure, and psychological commitment to the sport. This study aims to examine the influence of demographic factors, psychographic factors, and digital promotion on golf participation decisions in Bandar Lampung, with sport commitment as a mediating variable. A quantitative survey approach was employed using purposive sampling. Data were collected from 287 golf participants through a structured questionnaire measured with a five-point Likert scale. The data were analyzed using multiple linear regression and Sobel mediation testing. The findings show that demographic factors, psychographic factors, digital promotion, and sport commitment have positive and significant effects on golf participation decisions. Sport commitment was found to be the strongest predictor and significantly mediated the relationship between demographic factors, psychographic factors, digital promotion, and golf participation decisions. These results indicate that golf participation is influenced not only by access, lifestyle, and digital promotion, but also by the level of commitment developed by participants. This study contributes to sport marketing literature by integrating individual, psychological, and digital factors into one empirical model of golf participation behavior.

The enactment of Indonesia's Law Number 20 of 2025 on the Code of Criminal Procedure (KUHAP 2025), effective January 2, 2026, introduces a paradigmatic shift in the coordination between investigators and public prosecutors: Article 58 mandates active coordination from the investigation stage, fundamentally departing from the sequential-passive model of the former KUHAP, while Article 70 imposes a strict seven-day deadline for indictment drafting after case files are declared complete. This study examines two interconnected questions: (1) how the legal framework governing investigator–prosecutor coordination is structured under KUHAP 2025 and related legislation; and (2) how that framework is implemented in practice at the Purwakarta District Prosecutor's Office. A normative–empirical mixed-method design was employed, integrating statutory, conceptual, and case-study approaches. Data were gathered through in-depth interviews with prosecutors and investigators at Purwakarta District Prosecutor's Office and Purwakarta Police Resort, case document analysis, and field observation. The theoretical framework combines Lawrence M. Friedman's Legal System Theory and Soerjono Soekanto's Law Enforcement Theory. Findings reveal that KUHAP 2025 delivers substantial normative advancement yet harbours three critical regulatory gaps: the absence of binding technical protocols for implementing mandatory active coordination, the lack of uniform and measurable case-file completeness standards, and no formal mechanism for resolving institutional disagreements on legal interpretation. On the ground, coordination at Purwakarta still operates under the old sequential-passive pattern despite the new law: case-file returns (P-19) remain frequent, driven primarily by absent expert testimony, insufficient factual narration in examination records, and mismatches between charged articles and legal facts. A Friedman–Soekanto diagnostic reveals simultaneous dysfunction across all three legal system components substance, structure, and legal culture with the entrenched 'waiting culture' between the police and the prosecution identified as the most resistant obstacle to reform.

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.

Given the important objectives that banks strive to achieve through financial stability and their role in ensuring its continuity and ability to face various economic challenges, many have expanded their policies beyond their traditional functions by adopting a range of additional practices and activities that contribute to strengthening their developmental role in society. Among the most prominent of these practices are corporate social responsibility (CSR) activities, which have become a crucial aspect of the work of contemporary financial institutions. In this context, this research highlights CSR practices in banks. It relied on a sample of nine Iraqi banks listed on the Iraq Stock Exchange, which are characterized by their continued banking operations and regular publication of their annual financial reports. The research period was set from 2014 to 2023, and included a set of statistical tests that incorporated a number of financial determinants as control variables to determine their contribution to enhancing the impact of CSR when included alongside it, and to define the nature of the relationship between the research variables. We have reached a number of conclusions, most notably that when regulatory variables are included in the analysis model, this effect becomes statistically insignificant, which indicates that banks’ interest in internal financial factors still outweighs their interest in social aspects.

Sign language recognition is one of the areas of image recognition and image processing technology that is developing rapidly in human-computer interaction. This technology really helps the deaf and speech impaired in communicating with non-disabled people. This research aims to examine the optimization of an object tracking system in sign language using the Gaussian Mixture Model (GMM) and Kalman Filter by including the Region of Interest (ROI). The proposed system consists of three main components, namely hand detection, object extraction, and classification. Hand detection is done using the Kalman Filter to track hand movements accurately. Next, Region of Interest (ROI) features, such as shape, direction and movement features, are extracted from the detected part of the hand. These features are fed into a Gaussian Mixture Model (GMM) classifier, which can recognize sign language based on the extracted features. With the combination of GMM and Kalman Filter in this research, it can increase accuracy in object tracking, reduce interference from the background, and ensure the tracking focus remains on important objects. The dataset used is in the form os SIBI alphabet symbols, namely A-Z with the amount of data for each class, namely 620 images. Based on the research result, model testing using GMM, Kalman Filter and ROI produces higher accuracy of 99%, while model testing using GMM and ROI produces accuracy of 90%.

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.

This study aims to describe the implementation of instilling Islamic Religious Education values in the aspects of the Qur’an and Aqidah at TPQ Fattuhul Qulub, Doyo Baru District. The background of this research is based on the importance of Islamic religious education in shaping children’s character and morals from an early age amid the decline in the morality of the younger generation. TPQ, as a non-formal educational institution, has an important role in instilling Islamic values through learning the Qur’an, aqidah, and morals. However, in its implementation, several obstacles are still found, such as the low understanding of students regarding religious values, varying abilities in reading the Qur’an, and the lack of support from family and community environments.This study employed a descriptive qualitative approach. Data were collected through observation, interviews, and documentation at TPQ Fattuhul Qulub, Doyo Baru District. The focus of the research includes the planning, implementation, and evaluation processes of instilling Islamic Religious Education values in the aspects of the Qur’an and Aqidah.The results of the study indicate that the instillation of Islamic Religious Education values has been carried out through structured stages of planning, implementation, and evaluation. The learning process was conducted using the methods of Iqra’, tahsin, tahfidz, talaqqi, lectures, role modeling, habituation, and exemplary stories. In the Qur’anic aspect, students were guided to read the Qur’an according to tajwid rules and memorize short surahs, while in the Aqidah aspect, students were taught the pillars of faith, the attributes of Allah and His Messenger, and the formation of Islamic morals. The evaluation results showed improvements in Qur’anic reading skills, memorization, understanding of aqidah, and changes in students’ religious behavior, such as becoming more disciplined, polite, and diligent in worship. This success was supported by the exemplary behavior of the teachers and the involvement of parents in the learning process.

This study investigates the effect of total risk on firm performance and sustained growth among consumer non-cyclicals manufacturing companies listed on the Indonesia Stock Exchange (IDX) over the period 2019–2023. Total risk is operationalized through the systematic risk proxy (Beta/β), estimated via the Capital Asset Pricing Model (CAPM) framework as the covariance between individual stock returns and the market return divided by the variance of market returns, using the Jakarta Composite Index (JCI) as the market benchmark. Firm performance is measured through Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q, while sustained growth is operationalized following Gerson et al. (2025) as SG = b × ROE, where b denotes the earnings retention ratio. Panel data regression analysis is applied to 225 firm-year observations drawn from 45 companies, with model selection guided by the Chow and Hausman specification tests. The Fixed Effect Model (FEM) is adopted for ROA, ROE, and SG, while the Random Effect Model (REM) is applied for Tobin’s Q. Results indicate that systematic risk exerts a significant negative effect on ROA (β = −0.312; p < 0.01) and ROE (β = −0.278; p < 0.01), but is statistically non-significant for Tobin’s Q, suggesting that capital market pricing in Indonesia does not fully incorporate systematic risk information. Critically, systematic risk exerts the largest and most significant negative effect on sustained growth (β = −0.347; p < 0.01), revealing a dual transmission mechanism through which risk suppresses ROE while simultaneously inducing more conservative dividend policies, both of which constrain long-run growth sustainability. These findings carry important implications for corporate risk management strategy and empirically enrich the literature on risk, performance, and growth in emerging capital markets.

This study aims to analyze the implementation of legal responsibility by goat farmers in managing waste from Etawa goat farming based on a circular economy model at Nusantara Farm, Sidoarjo. The study employs both normative juridical and empirical juridical approaches, with data collection techniques including literature review, interviews, and field observations.The results indicate that waste management is carried out through direct utilization, such as using livestock manure as organic fertilizer, selling waste, and distributing it to the surrounding community. These practices demonstrate that waste is not disposed of carelessly but rather reused, thereby creating economic and functional value.From a legal perspective, this condition reflects the fulfillment of the farmers' responsibilities in accordance with applicable laws and regulations, particularly in efforts to prevent environmental pollution. Furthermore, these waste management practices partially embody the principles of the circular economy, especially in terms of reuse.However, the current waste management practices remain conventional and are not yet optimally integrated. Therefore, there is a need to develop a waste management model based on an integrated closed-loop system to enhance the economic value of waste while ensuring more effective environmental sustainability.

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.

The completeness of nursing care documentation serves as a primary indicator of hospital service quality and remains a critical component of patient safety, clinical communication, and legal accountability. Despite its importance, empirical evidence from various healthcare facilities indicates that nursing records are frequently suboptimal, incomplete, or inconsistent. This study aims to analyze the complex interrelationships between burnout and coaching on the completeness of nursing care documentation, specifically examining the role of nursing competence as a mediating (intervening) variable at the Dr. Chasbullah Abdulmadjid General Hospital in Bekasi City. Utilizing a quantitative research framework with a cross-sectional design, this study sampled practicing nurses stationed across inpatient and intensive care units. Primary data were gathered through a validated, structured questionnaire designed to measure psychological burnout, the frequency of clinical coaching, perceived professional competence, and the objective completeness of documentation. The data were subjected to rigorous analysis using Structural Equation Modeling–Partial Least Squares (SEM-PLS) to test the hypothesized pathways within the conceptual model. The results demonstrated that burnout exerts a significant negative influence on both nursing competence and the quality of documentation, suggesting that emotional exhaustion hinders professional performance. Conversely, systematic coaching was found to have a significant positive impact, directly improving both competence levels and documentation adherence. Critically, the analysis confirmed that nursing competence acts as a vital intervening variable; it effectively mediates and strengthens the influence of both burnout reduction and coaching interventions on the overall completeness of nursing care records. This study concludes that proactive burnout management and the institutionalization of structured coaching programs are essential strategic priorities. By addressing these factors, hospital management can enhance individual nurse competence, thereby ensuring high-quality, comprehensive nursing documentation that supports patient safety and institutional integrity.