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Nature tourism is one of the sectors that plays an important role in supporting the development of regional tourism, including in Lahat Regency, which has significant waterfall tourism potential. Currently, many visitors share their reviews and experiences through digital platforms such as Google Maps. This review can be used as a source of information to understand the public's evaluation of the quality of tourist attractions. This study aims to examine public perception of tourist attractions in Lahat Regency using the Support Vector Machine (SVM) method. Research data were collected through scraping from Google Maps, totaling 500 reviews from five tourist attractions, namely Curup Maung, Curup Buluh, Senyawe Waterfall, Panjang Waterfall, and Green Canyon. The research stages include data preprocessing, consisting of cleaning, case folding, normalization, tokenization, stopword removal, and stemming. After that, feature extraction was carried out using the TF-IDF method and the classification process using the SVM algorithm. Based on the research results, the Support Vector Machine (SVM) method is able to perform sentiment classification quite well, although the accuracy level varies for each tourist attraction. Curup Maung and Panjang Waterfall achieved the highest accuracy level of 90%. Nevertheless, most visitor reviews were dominated by negative sentiments. This indicates that there are still several aspects that need to be improved, particularly related to tourist facilities and services. This research is expected to serve as a consideration for tourism managers and local governments in efforts to improve management quality as well as the development of tourism in Lahat Regency.

This study was conducted to examine the integration of spiritual intelligence into Islamic Religious Education (PAI) learning as an effort to strengthen students’ character. At MA Al-Amien Bugih Pamekasan, some students still demonstrate a lack of discipline, responsibility, and honesty in the learning process. To address these issues, PAI teachers integrate spiritual values through religious activities and the cultivation of positive habits during classroom instruction. This study aims to describe the implementation of spiritual intelligence integration, the strategies used by teachers, and its implications for students’ character development. This research employed a descriptive qualitative approach. Data were collected through observations, interviews, and documentation involving purposively selected informants, namely PAI teachers and students. Data analysis followed the Miles, Huberman, and Saldana model, including data reduction, data display, and conclusion drawing. The validity of the findings was ensured through source and technique triangulation. The results indicate that the integration of spiritual intelligence in PAI learning has been implemented systematically through planning, implementation, and evaluation stages. Teachers incorporate spiritual values through religious practices, role modeling, learning reflections, discussions of moral values, and character assessments. These strategies contribute positively to the development of students’ character, particularly in fostering discipline, responsibility, honesty, cooperation, empathy, and independence. Although several challenges remain, including differences in students’ backgrounds, limited instructional time, and external environmental influences, the integration of spiritual values continues to support sustainable character development among students. The findings highlight the important role of spiritual intelligence in creating meaningful learning experiences and promoting positive character formation in Islamic educational settings.

This study aims to determine the production process and the level of consumer acceptance of Bandung nagasari cake utilizing stevia as a natural sweetener and butterfly pea flower extract (Clitoria ternatea) as a natural coloring agent. The research employed a quantitative approach with an experimental method through organoleptic testing involving 20 panelists. Data collection techniques consisted of literature review, questionnaires, and organoleptic evaluation covering taste, texture, aroma, and appearance. The obtained data were analyzed using descriptive quantitative analysis with percentage calculations. The findings indicated that the Bandung nagasari formulation containing stevia and butterfly pea flower extract was more preferred than the formulation using granulated sugar. Approximately 90% of panelists preferred the taste attribute due to its healthier perception, while 80% of panelists favored the texture, aroma, and appearance attributes because of the softer texture and the attractive natural coloration produced by the butterfly pea flower extract. Furthermore, the product demonstrated a shelf life of 12 hours at room temperature and up to 3 days under refrigerated storage conditions. The study concludes that the incorporation of stevia and butterfly pea flower extract may serve as an innovative development of traditional Bandung nagasari cake without eliminating its traditional characteristics and shows favorable consumer acceptance.

 This community service activity aimed to improve students’ Islamic banking literacy through an experiential learning approach based on a fiqh muamalah workshop at Latansa Darussalam Islamic Boarding School, Palembang. The activity involved 100 participants consisting of students and teachers as an effort to strengthen understanding of Islamic economic and banking principles. The methods used in the activity included lectures, discussions, question-and-answer sessions, and simulations of sharia transaction practices, enabling participants not only to gain theoretical understanding but also practical experience in implementing sharia concepts. The evaluation was conducted using pre-test and post-test instruments in the form of multiple-choice questions and Likert scales to measure participants’ level of understanding before and after the activity. The evaluation results showed an increase in the participants’ average score from 55 to 82, with a gain score of 0.6, which falls into the moderate to high category. These findings indicate that an experience-based learning approach is effective in improving conceptual and practical understanding related to Islamic economics and is recommended for broader implementation in Islamic educational institutions.

Diarrhea has the potential to cause an Extraordinary Event (KLB) and is currently a leading cause of mortality in Indonesia, especially in children. Antibiotics are necessary for diarrhea caused by bacteria, but irrational antibiotic prescribing can increase the risk of antibiotic resistance. This literature review was conducted to provide an overview of the level of rationality of antibiotic use in pediatric patients with diarrhea in several Indonesian health facilities. The literature review was conducted by reviewing articles obtained through a Google Scholar search using the keywords "rationality of antibiotic use," "diarrhea," "children," and "evaluation of antibiotic use." Articles were selected using inclusion and exclusion criteria, resulting in 10 suitable articles. The study results indicate that the evaluation methods for antibiotic use include the Gyssens criteria, World Health Organization criteria, Ministry of Health Decree Standards, the 2012 World Gastroenterology Organization Guidelines, and the Rational Drug Use Module. Commonly used evaluation parameters include appropriate indication, appropriate drug, appropriate dose, and appropriate patient. Most parameters have obtained rational results, but there are several studies that are not rational in the appropriate indication, appropriate drug, and appropriate dose categories. Variations in the results of rational antibiotic use at each health facility are influenced by patient characteristics, policies at the health care facility, and the level of compliance of health workers with therapy guidelines.

Diarrhea has the potential to cause an Extraordinary Event (KLB) and is currently a leading cause of mortality in Indonesia, especially in children. Antibiotics are necessary for diarrhea caused by bacteria, but irrational antibiotic prescribing can increase the risk of antibiotic resistance. This literature review was conducted to provide an overview of the level of rationality of antibiotic use in pediatric patients with diarrhea in several Indonesian health facilities. The literature review was conducted by reviewing articles obtained through a Google Scholar search using the keywords "rationality of antibiotic use," "diarrhea," "children," and "evaluation of antibiotic use." Articles were selected using inclusion and exclusion criteria, resulting in 10 suitable articles. The study results indicate that the evaluation methods for antibiotic use include the Gyssens criteria, World Health Organization criteria, Ministry of Health Decree Standards, the 2012 World Gastroenterology Organization Guidelines, and the Rational Drug Use Module. Commonly used evaluation parameters include appropriate indication, appropriate drug, appropriate dose, and appropriate patient. Most parameters have obtained rational results, but there are several studies that are not rational in the appropriate indication, appropriate drug, and appropriate dose categories. Variations in the results of rational antibiotic use at each health facility are influenced by patient characteristics, policies at the health care facility, and the level of compliance of health workers with therapy guidelines.

Abstract: Detecting chili leaf diseases remains challenging due to the non-uniform manifestation of symptoms, local discoloration, small lesion regions, and visual similarity between disease patterns and natural leaf background variations. Although YOLO-based detectors provide favorable computational efficiency, lightweight variants often struggle to distinguish subtle lesion characteristics, while conventional attention mechanisms such as CBAM primarily rely on global feature aggregation and may overlook regional activation variability. To address these limitations, this study proposes a YOLOv9s-based detection framework integrated with a Region-Dispersion Channel Spatial Attention (RDCSA) module. The proposed module incorporates regional dispersion statistics, namely mean, standard deviation, and range, as channel descriptors to capture inter-region feature variability before applying spatial attention refinement. Experiments were conducted on the COLD dataset containing 532 original images from five chili leaf condition categories using a split-before-augmentation protocol to ensure objective evaluation. RDCSA was integrated at the P5 feature level and evaluated through attention placement analysis, component-wise ablation, sensitivity analysis, stability assessment, and comparison with modern attention mechanisms. The proposed YOLOv9s + RDCSA model achieved an mAP@50 of 0.894, mAP@50–95 of 0.773, precision of 0.858, recall of 0.861, and an F1-score of 0.859 with only a marginal increase in model parameters. The results suggest that regional dispersion-based attention improves feature discrimination while preserving computational efficiency, particularly for disease symptoms characterized by heterogeneous spatial patterns. Nevertheless, performance remains influenced by visually ambiguous symptom categories, indicating that further validation across multiple datasets and field conditions is required. Overall, the proposed RDCSA module enhances detection capability without substantially increasing computational overhead, making it a promising attention mechanism for lightweight plant disease detection systems.

 Education in vocational high schools (SMKs) requires effective data management to improve students’ academic achievement and discipline. At SMK Islam Secang, students’ academic scores and attendance data have so far functioned merely as administrative archives, making it difficult to identify patterns of student performance. This study aims to classify students based on academic achievement and discipline by applying the K-Means Clustering algorithm using RapidMiner. The data used in this study consist of scores from six subjects and attendance records of 35 students from the Light Vehicle Engineering (TKR) department over two semesters. The data were obtained from original school records, compiled using Microsoft Excel, and processed in RapidMiner. The clustering process employed four clusters for academic achievement and two clusters for discipline, with Euclidean Distance used as the similarity measure. The results show that in the first semester, students were grouped into four academic achievement clusters: high achievement (6 students), moderate achievement (7 students), potentially problematic (14 students), and problematic (8 students). In the second semester, the distribution changed to high achievement (19 students), moderate achievement (14 students), potentially problematic (4 students), and problematic (1 student). Meanwhile, student discipline was divided into two clusters: disciplined (31 students) and undisciplined (4 students). These results demonstrate that K-Means Clustering is effective in mapping student conditions, revealing patterns in academic performance and attendance, and supporting educational evaluation, learning planning, and early detection of students who require academic or disciplinary intervention. Keywords: Data Mining, K-Means Clustering, Academic Achievement, Discipline, RapidMiner, Vocational High School (SMK)

Instagram social media addiction has become an increasingly common phenomenon among university students. Excessive use of Instagram can negatively affect mental, social, and academic well-being, and may increase the risk of social anxiety. Students who experience Instagram addiction tend to have reduced direct social interaction, and fear of negative evaluation from others, which are key factors contributing to social anxiety. This study aims to determine the relationship between Instagram social media addiction and social anxiety among students. This study used a quantitative approach with a cross-sectional design. The sample consisted of 105 respondents selected using purposive sampling. The instruments used were the Instagram Addiction Scale (TIAS) and the Social Anxiety Scale for Social Media Users (SAS-SMU). Data analysis was conducted using the Chi-Square test. The results showed that most respondents had a moderate level of Instagram addiction (56.2%) and a moderate level of social anxiety (68.6%). The Chi-Square test indicated a significant relationship between Instagram addiction and social anxiety, with a p-value < 0.001 (p < 0.05). There is a significant relationship between Instagram addiction and social anxiety among students. However, the relationship is not always linear, as higher levels of addiction do not necessarily correspond to higher levels of social anxiety.

Instagram social media addiction has become an increasingly common phenomenon among university students. Excessive use of Instagram can negatively affect mental, social, and academic well-being, and may increase the risk of social anxiety. Students who experience Instagram addiction tend to have reduced direct social interaction, and fear of negative evaluation from others, which are key factors contributing to social anxiety. This study aims to determine the relationship between Instagram social media addiction and social anxiety among students. This study used a quantitative approach with a cross-sectional design. The sample consisted of 105 respondents selected using purposive sampling. The instruments used were the Instagram Addiction Scale (TIAS) and the Social Anxiety Scale for Social Media Users (SAS-SMU). Data analysis was conducted using the Chi-Square test. The results showed that most respondents had a moderate level of Instagram addiction (56.2%) and a moderate level of social anxiety (68.6%). The Chi-Square test indicated a significant relationship between Instagram addiction and social anxiety, with a p-value < 0.001 (p < 0.05). There is a significant relationship between Instagram addiction and social anxiety among students. However, the relationship is not always linear, as higher levels of addiction do not necessarily correspond to higher levels of social anxiety.

Antimicrobial resistance is a persistent threat to hospital care, particularly when empirical therapy relies on broad-spectrum antibiotics without continuous evaluation of local use and susceptibility patterns. This literature review aimed to synthesize evidence from four Indonesian hospital-based studies regarding antibiotic utilization, rationality assessment, and bacterial resistance profiles. A structured narrative review was conducted using four selected articles provided by the author. Data were extracted for study design, setting, population, antibiotic evaluation method, dominant antibiotic classes, rationality indicators, resistance profile, and stewardship implications. The four studies were descriptive and hospital-based, using retrospective records, concurrent observation, ATC/DDD with DU 90%, qualitative rationality criteria, and antibiogram data. The synthesis showed a consistent concentration of antibiotic use in broad-spectrum groups, especially third-generation cephalosporins, penicillins, quinolones, and macrolides. In a Bandung public hospital, total antibiotic consumption reached 95,719.01 DDD, with penicillins, cephalosporins, quinolones, macrolides, and sulfonamides included in the DU 90% segment. In intensive care, ceftriaxone was the most frequently used antibiotic and most rationality indicators were appropriate, although clinically significant drug interactions were still identified. In pediatric acute respiratory infection inpatients, cefotaxime and ceftriaxone dominated empirical therapy. Resistance mapping in Denpasar highlighted relevant Gram-positive and Gram-negative pathogens and recommended antibiotics according to susceptibility levels. Overall, the reviewed evidence supports an integrated antimicrobial stewardship model combining ATC/DDD-DU 90% surveillance, qualitative rationality evaluation, antibiogram-based empirical guidance, and periodic feedback to prescribers.

This research was motivated by the importance of public relations management in building cooperation among schools, parents, and communities to support the improvement of educational quality in elementary schools. This study aimed to analyze public relations management in increasing parent and community participation in elementary schools. The research used a qualitative approach with a descriptive research design. Data collection techniques were conducted through interviews, observations, and documentation involving the principal, teachers, parents, and community members as informants. Data analysis was carried out through data reduction, data presentation, and conclusion drawing. The results showed that public relations management was implemented through direct communication in parent meetings, the use of digital media such as WhatsApp Groups, parental involvement in school activities, and collaboration with the community in supporting educational programs. Parent participation was reflected in mutual cooperation activities, assisting students’ learning, and supporting school programs. The obstacles found included parents’ limited time, low digital literacy among some parents, and the lack of optimal evaluation of school public relations programs. The implications of this study indicate that effective public relations management can increase parent and community involvement in supporting the educational process in elementary schools.

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 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.

The dissemination of personal data through digital media has increased significantly alongside the growing use of Quick Response (QR) Codes for various purposes, such as electronic tickets, certificates, and digital identities. Conventional QR Codes are open and can be easily scanned, copied, or manipulated by unauthorized parties. The personal data referred to in this study includes sensitive information such as full name, identity number (NIK/National ID), date of birth, address, phone number, and email address. This research proposes a layered security system that combines the Advanced Encryption Standard (AES) cryptographic algorithm with steganography using the Discrete Cosine Transform (DCT) method. The process begins with encrypting personal data using AES, converting the encrypted result into a QR Code, and embedding the QR Code into a digital image using DCT, hiding it in the image’s frequency domain. The digital images used are of fixed size and formats that preserve visual quality. System evaluation is carried out by testing the visual quality of the stego image, the success rate of QR Code extraction, and the integrity of the encrypted data. The results are expected to conceal sensitive information visually while maintaining its confidentiality, with potential applications in electronic ID cards, digital certificates, e-tickets, and other confidential documents.

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.

The field of education, especially Islamic Religious Education (PAI), has seen substantial changes as a result of the advancement of digital technology and artificial intelligence (AI). The existence of digital media and AI offers numerous opportunities to improve the effectiveness, efficiency, and quality of education through interactive, flexible, and easily accessible learning resources. The purpose of this article is to analyze how artificial intelligence (AI) and digital media are used in Islamic Religious Education. This includes the idea of AI and digital media, AI-based learning materials, the use of AI in educational activities, ethical considerations of AI use, the effects and difficulties of integrating AI, and the Islamic viewpoint on technological advancement. This study employed a library research method by collecting data from relevant journals, books, and other scholarly sources. The findings indicate that the use of AI and digital media in PAI learning can enhance access to information, personalize learning experiences, improve evaluation effectiveness, and increase student engagement in the learning process. However, the implementation of AI also presents several challenges, such as limited digital literacy among teachers, inadequate facilities and infrastructure, ethical concerns, and the potential decline of direct educational interaction between teachers and students. Therefore, the utilization of AI in Islamic Religious Education should be carried out wisely while maintaining Islamic values, ethics, and the primary goal of Islamic education in shaping students’ character.

This study was conducted based on the low level of student activeness and understanding in IPAS learning at the elementary school level. The objective of this research was to determine the effectiveness of the role play method in improving students’ activeness and learning outcomes. This study employed a classroom action research approach involving 27 students. The data were collected through observation and evaluation of student learning outcomes. The findings revealed a significant improvement in both student participation and understanding after the implementation of the role play method. Students became more active in asking questions, responding to the teacher, and participating in classroom activities. In terms of learning outcomes, 85.2% of students were able to answer questions correctly and demonstrated good understanding, while the remaining 14.8% also achieved the minimum criteria. These results indicate that learning through direct experience enhances students’ comprehension. The implication of this study suggests that the role play method can be used as an effective alternative teaching strategy to create an interactive, engaging, and student-centered learning environment in elementary education.

This community service program addressed the limited capacity of educators to use artificial intelligence in a structured and pedagogically responsible way for textbook development. The program focused on empowering educators through NotebookLM in the Ngabuburit AI 2026 activity. Its objective was to improve participants’ understanding and practical skills in organizing sources, designing textbook structures, developing chapter content, and generating interactive learning media. The program used a participatory training approach consisting of presentation, demonstration, guided practice, discussion, and feedback-based evaluation. The results showed that participants gained a clearer understanding of AI-assisted academic writing workflows and recognized NotebookLM as a useful tool for integrating source analysis, textbook writing, and learning media preparation. The activity also fostered new awareness that artificial intelligence can strengthen, rather than replace, educators’ academic roles in producing more systematic and interactive teaching materials.

The use of synthetic dyes in the textile industry has caused various environmental problems due to waste that is difficult to degrade and has the potential to contaminate water and soil. Therefore, the development of environmentally friendly natural dyes is an important alternative to be explored. One potential but underutilized source of natural dye is kirinyuh weed (Chromolaena odorata L.), which is known to contain natural pigment compounds such as tannins and flavonoids. This study aims to analyze the dyeing quality of primissima cotton fabric using kirinyuh leaf extract with variations of alum and ferrous mordants through a mordanting process. The research employed an experimental method, including hot extraction of kirinyuh leaves, dyeing of primissima cotton fabric, and mordanting using alum and ferrous salts. The evaluation of dyeing quality focused on washing color fastness and light color fastness tests based on standard textile testing methods. The results indicate that the type of mordant significantly affects the resulting color quality. Alum mordant produced relatively lighter colors with good washing fastness, while ferrous mordant resulted in darker shades with similarly good washing fastness. However, color fastness to light showed relatively lower values, particularly in fabrics treated with alum mordant. Based on these findings, it can be concluded that kirinyuh leaf extract has the potential to be used as a natural dye for primissima cotton fabric, although further optimization is required to improve color resistance to light exposure.