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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

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 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.

Medication adherence among the elderly is an important factor in achieving successful therapy, particularly during the month of Ramadan when changes occur in meal patterns and medication schedules. Elderly patients with chronic diseases often experience difficulties in adjusting their medication timing, which may lead to poor adherence. This study aimed to implement a smart pill boxwith educational labels to improve knowledge and medication adherence among elderly individuals during fasting. The study involved 40 elderly respondents in a social care institution. The intervention consisted of training on the use of the smart pill box, educational labeling, and evaluation through observation, interviews, and checklist forms. The results showed that most respondents took medication twice daily (42.5%) and adjusted their medication schedules to sahur and iftar (45%). Before the intervention, 57.5% of respondents had not received information about medication use during fasting. The implementation of the smart pill boxwith educational labels improved the elderly’s understanding of medication use and supported better medication adherence during Ramadan.

Food Additives (BTP) in current consumer products increase the risk of non-communicable diseases such as diabetes, kidney failure, and cancer, particularly in children. Housewives play a central role in managing family consumption to minimize these risks. This community service activity aims to enhance the knowledge and awareness of 'Aisyiyah women in Buluspesantren, Kebumen Regency, regarding food safety and BTP regulations. The implementation method included preparing materials based on BPOM regulations, interactive education, product discussions, and simulations on reading nutrition labels. The results showed high enthusiasm from participants who are now more critical in distinguishing between natural and synthetic additives. Participants reported a significant improvement in their ability to identify food ingredients and additives in everyday products. The final evaluation recorded a 22.2-point increase in participant understanding. By establishing the habit of reading food labels, it is expected that the risk of non-communicable diseases within the family environment can be reduced, thus improving public health literacy and dietary choices within the community.

Since the announcement of the policy plan for a name transfer system in the sale of used mobile phones, the issue has attracted widespread public attention and discussion. People have expressed their opinions on social media platforms, particularly TikTok. This study aims to classify the sentiment of TikTok users using Naive Bayes and Support Vector Machine (SVM) algorithms. The data were collected through a comment scraping technique on related content.The research stages include text preprocessing, sentiment labeling into positive, negative, and neutral categories, and feature extraction using TF-IDF. The classification process employs Naive Bayes and Support Vector Machine algorithms, which are then evaluated based on accuracy, precision, recall, and F1-score. The results of this study indicate that both methods are capable of classifying sentiment effectively. However, the Support Vector Machine method is superior to the Naive Bayes method with an accuracy rate of 99.57% compared to 94.30%. This study is expected to help the government understand public responses to the planned policy of the used mobile phone name transfer system.

This study examines the paradoxical phenomenon among adolescents who actively consume mental health content on digital platforms such as Instagram and TikTok but are reluctant to utilize freely available school counseling services. The research aims to identify specific forms of stigma that hinder adolescents from utilizing school counseling services and analyze them using Erving Goffman's stigma theory. The research method employs a qualitative approach with a library research design, analyzing various scientific literature published from 2013-2025 through content analysis techniques. The research findings reveal six main forms of stigma: perception of school counselors as school police, labeling as troubled students, sign of personal weakness, doubts about confidentiality, distrust in counselor competence, and traumatic experiences. The paradox occurs because digital platforms provide anonymity that protects adolescents' virtual social identity, while face-to-face counseling services are perceived as threatening their social identity. Research implications indicate the need for transformation of school counseling services that integrate digital strategies, ongoing campaigns to change negative perceptions, and enhancement of counselor competence in understanding adolescent digital culture to reduce stigma and improve adolescent access to professional mental health services.

This study analyzes the strategic role of quality-based marketing models in enhancing the competitiveness of kombucha products, with a specific focus on Rumah SCOBY DBA, produced by the Yayasan Dharma Bintang Akademia. By integrating Total Quality Management (TQM), Quality Assurance (QA), and Quality Control (QC), the research explores how quality-driven frameworks contribute to marketing effectiveness, consumer trust, brand positioning, and sustainable performance in the functional beverage industry. Employing a Systematic Literature Review (SLR) combined with conceptual analysis, the study systematically examined publications from 2015 to 2025 across leading academic databases, focusing on themes of quality management, functional beverage marketing, kombucha production, consumer behavior, and digital strategies. The findings reveal that product quality—characterized by fermentation stability, microbiological safety, and nutritional consistency—serves as the primary driver of consumer purchase intention. Process quality, through standardized SOPs, hygiene protocols, and traceability systems, reinforces credibility, while service quality, including transparent labeling, health education, and digital engagement, strengthens brand trust. Integrating TQM principles into marketing fosters consumer loyalty, differentiates brands in competitive markets, and supports long-term sustainability. This study provides practical guidance for producers, community-based enterprises, and policymakers to adopt quality-driven marketing models, offering a novel conceptual framework tailored to kombucha products and mapping future research directions in functional beverage innovation.

The popularity of the boys love (BL) genre, a key representation of the LGBTQ community, raises concerns about its potential negative impact on social pathology related to LGBTQ behaviors among fans. Despite the stigma labeling BL enthusiasts as exhibiting deviant sexual behavior (homosexuality), early observations and field findings show that most fans are heterosexual and do not exhibit such behavior. Instead, their engagement represents a cumulative effort to support LGBTQ representation. This research uses a qualitative case study approach, conducted on social media X over four months. Key findings suggest that the primary curative efforts by fans to address the social pathology of LGBTQ are self-control and the establishment of self-boundaries. Self-control is demonstrated through cognitive and decision-making control, while self-boundaries are seen in fans' understanding that fictional content should not be translated into heteronormative reality. Strengthening self-control and self-boundaries lays the foundation for further curative actions, preventing fans from becoming fully integrated into the LGBTQ community.

Budget efficiency is an important issue in state financial management because it is directly related to government spending priorities and their impact on public service programs. Discussions about budget efficiency policies are widespread on social media platform X, generating diverse public responses, thus necessitating an automated approach to understand public opinion trends more quickly and objectively. This research aims to analyze the sentiment of Indonesian people toward budget efficiency policies and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment. The research data used 10,909 Indonesian-language tweets sourced from a public dataset, which were then processed thru the preprocessing stages including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling is performed automatically using the Indonesian Sentiment Lexicon (InSet) approach to categorize data into positive, negative, and neutral sentiments. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and then the data was divided into training and testing sets with an 80:20 ratio. Model performance evaluation was conducted using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The research results show that sentiment distribution is dominated by negative sentiment at 56.78%, followed by positive sentiment at 37.40%, and neutral sentiment at 5.83%. In the classification stage, SVM performed best with an accuracy of 86%, while Naïve Bayes achieved an accuracy of 74%. These findings indicate that SVM is more optimal for sentiment classification on social media text data and can be utilized to more effectively support the analysis of public response to budget efficiency policies.

The development of social media-based e-commerce, especially TikTok Shop, has created new challenges and opportunities in building customer loyalty, especially in the highly competitive local cosmetics industry. One of the most popular local brands is Emina, which targets young consumers with an affordable price approach and halal label. However, the level of customer loyalty is still a crucial issue that needs to be strengthened so that business sustainability is maintained. The urgency of this research lies in the need to understand how live streaming and halal labeling, as two relevant marketing strategies in the digital era, are able to shape purchasing interest that leads to customer loyalty. This study uses a quantitative approach with the PLS-SEM technique to test the relationship between variables with 115 TikTok Shop user respondents in Semarang City. The results show that live streaming and halal labeling have a significant effect on purchasing interest and customer loyalty, and purchasing interest is proven to mediate the relationship between the two variables and customer loyalty. These findings indicate the importance of integrating interactive visual approaches and religious belief values in digital marketing strategies for cosmetic products.

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *    

The development of social media platforms like TikTok has created new spaces for digital economic activities, including the practive of thrifting, which has now become a trend among the public. However, government policies that block these activities have sparked various public reactions. This study aims to analyze public sentiment regarding the issue of thrifting bans on the TikTok platform using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. This method was chosen because it can understand text context from both directions, allowing it to capture deeper semantic meaning. The dataset consist of 4,000 TikTok user comments collected through a crawling process. The research stages include data preprocessing, sentiment labeling, splitting training and test data, training the Bi-LSTM model, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The research results show that the Bi-LSTM model achieved an accuracy of 86.15%, with stable classification performance and minimal error rate. These findings indicate that Bi-LSTM is effective for sentiment analysis of public opinions on Indonesian language social media, particularly on context specific policy issues. Further development can be carried out by adding pre-trained embeddings or attention mechanisms to improve the model’s performance.

Hijaiyah letters have varying shapes, and some of them are very similar, often causing errors in the manual character recognition process. This study aims to classify Hijaiyah letters based on digital images using the Convolutional Neural Network (CNN) method. This method was used in this study with a dataset consisting of 28 letter classes and a total of 4,480 images obtained from various public sources and private data. All images underwent a preprocessing stage that included labeling, resizing, normalization, and augmentation, then were divided into three parts, namely training data, validation data, and test data with a ratio of 70:20:10. The training process was carried out using the Python programming language with the help of the TensorFlow and Keras libraries on the Google Colab platform. The test results showed that the CNN model achieved an accuracy of 97.10%, with an average precision, recall, and F1-score of 0.97, respectively. Classification errors only occurred in letters that had similar shapes, such as Syin and Sin. Based on these results, the CNN method proved to be effective, efficient, and accurate in recognizing Hijaiyah letter image patterns, so it can be used as a basis for developing classification models with higher accuracy in the future.

Sugar consumption in Indonesia remains high, with diabetes affecting 20.4 million people. This condition has prompted the government to introduce an excise policy on Minuman Berpemanis Dalam Kemasan (MBDK) to reduce sugar intake. Social media, particularly the X platform, serves as a medium for the public to express their opinions regarding this policy. This study aims to analyze public sentiment toward the MBDK excise policy using a lexicon-based approach for data labeling and the Multinomial Naive Bayes algorithm with unigram and bigram feature extraction. The initial results show that the highest performance was achieved using 5-Fold Cross Validation, with an average accuracy of 83%, precision of 84%, recall of 75%, and an F1-Score of 77%. After applying data balancing using Stratified Cross Validation combined with Borderline-SMOTE and limiting the features to the 700 most frequent terms, the model’s performance improved. The best results were obtained with 10-Fold Cross Validation, achieving 86% accuracy, 84% precision, 83% recall, and an F1-Score of 83%. These findings indicate that the Multinomial Naive Bayes model can effectively classify public sentiment regarding the MBDK excise policy after the data balancing process.

This study analyzes the quality control of lamb rendang products at CV. Mitra Tani Farm using three Statistical Quality Control (SQC) tools: the checksheet, Pareto diagram, and Fishbone diagram. The research was conducted to identify major product defects and their root causes in the canned lamb rendang production process. A descriptive quantitative method was applied through direct observation, interviews, and documentation. Data were collected using a checksheet to record defect types and frequencies, then analyzed with Pareto and Fishbone diagrams to determine dominant issues and underlying factors. The results showed that labeling errors were the most frequent defect, followed by inaccurate sterilization and machine overload. These problems were mainly caused by limited operator skills, non-standardized procedures, and inadequate machine performance. The findings indicate that improving operator training, refining standard procedures, and maintaining equipment are essential to enhance product quality and consistency.  

Technological developments in the world of social media have had a significant impact on digital marketing, particularly in the cosmetics industry with the use of digital influencers. The digital influencer approach has become a force capable of influencing customer preferences and purchasing intentions. The focus of this study analyzes the influence of digital influencers on the use of the "Tasya Farasya approved" labeling on purchasing intentions. This study uses a quantitative approach and involves 100 respondents selected through a purposive sampling technique. Data were collected through a questionnaire distributed via Google Form with validity and reliability tests. Data analysis revealed a moderate relationship between digital influencers and purchasing intentions. Therefore, the results of this study, based on hypothesis testing and coefficient of determination tests, state that the digital influencer variable has a significant influence on purchasing intentions. Thus, the marketing strategy of collaborating with digital influencers on the use of the "Tasya Farasya approved" labeling on Instagram is an effective strategy to influence purchasing intentions for the Luxcrime Ultra Light Lip Stain product.

This study aims to explore how religious Muslim students interpret halal labels on product packaging and how these interpretations influence their consumption behavior. In the context of increasing awareness of halal product importance, halal labeling is not just a symbol of religious law, but also a guarantee of quality, safety, and trust. The research uses a descriptive qualitative method phenomenological approach, involving students from UIN Prof. K.H. Saifuddin Zuhri Purwokerto who were selected through purposive sampling. Data was collected through semi-structured interviews, literature study, and documentation, then analyzed using thematic analysis. The findings reveal that highly religious students tend to be more selective, careful, and responsible in choosing products, prioritizing those with halal certification even when priced higher. The halal label provides psychological comfort and certainty that the product aligns with Islamic principles. Additionally, a strong understanding of halal labels increases consumer loyalty and supports the growth of the halal industry through rising demand for certified products. Thus, the halal label plays a significant role in shaping the consumption patterns of religious Muslim students and contributes to the broader development of the halal industry in Indonesia.