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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.

Increased caffeine intake is commonly used by students to cope with academic demands, especially during examinations and the completion of final projects. Excessive caffeine consumption may affected sleep quality and potentially influence nutritional status. This study aimed to determine the correlation between caffeine intake, sleep disturbances, and nutritional status among final – year unsergarduated students at Dr. Soekardjo University. This study used quantitative approach with an observational analytic and cross-sectinal design. A total of 108 students participated in the study. Data were collected using questionnaires and analyzed using the Spearman Rank correlations test. The findings indicated that the most of respondents with low caffeine intake experienced mild sleep disturbances, totaling 56 respondents (80%). Meanwhile, respondents with moderate and high caffeine intake mostly experienced moderated sleep disturbances, with 13 respondents (61.9%) and 9 respondents (52.9%), respectively. The results showed a significant correlation between caffeine intake and sleep disturbances (p-value=0.000). In all categories of caffeine intake, most respondents had normal nutritional status. However, no significant relationship was found between caffeine intake and nutritional status (p-value=0.351). In conclusion, caffeine intake was associated with sleep disturbances but not with nutritional status among final-year undergraduate students.

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.

Postpartum depression is a postpartum mental health disorder that significantly impacts maternal well-being, infant development, and family functioning. The high prevalence of postpartum depression in Indonesia is due to limited access to health services, low mental health literacy, and social stigma in the community. This indicates a significant gap between the need for maternal mental health services and the availability of existing interventions, making education a crucial component in efforts to prevent postpartum depression early. This study aims to analyze the prevention of postpartum depression in postpartum mothers through telenursing-based education and screening using the Edinburgh Postnatal Depression Scale (EPDS) in the community. A descriptive case study design was used, involving one respondent, a 25-year-old primigravida mother residing in the Bantul area. The intervention was implemented online via WhatsApp and video calls, including structured health education on postpartum psychological changes, adaptive coping strategies, and the importance of social support. The intervention also included daily remote monitoring of the respondent's condition via the WhatsApp mobile application. The EPDS was administered as a pre-test and post-test to evaluate changes in the respondent's psychological condition. The findings showed a significant decrease in the EPDS score from 16 (moderate depression) to 6 (minimal depression), indicating significant psychological improvement. These results imply that integrating EPDS screening, structured health education, and daily monitoring is an effective and accessible community-based approach to preventing postpartum depression, particularly for mothers with limited mobility and access to health services.

This study was motivated by the lack of standardized and objective assessment instruments for the teaching of the Syofyani Minang Payung Dance at the junior high school level, resulting in an assessment process that remains largely subjective and fails to measure psychomotor, affective, and cultural aspects in a balanced manner. This study aims to design and test the validity and reliability of a performance assessment rubric for the Syofyani Minang Payung Dance in cultural arts education at SMP Negeri 1 Bukittinggi. The research method used is a mixed-methods approach with a sequential explanatory design. The research subjects consisted of three dance instructors serving as expert judges, one cultural arts teacher, and 33 junior high school students. The research instrument was a performance-based assessment rubric covering five competency indicators: basic movement techniques; alignment with musical rhythm and dynamics; expression and character interpretation; mastery of payung props and movement safety; and accuracy of floor patterns and group synchronization. Quantitative data analysis was conducted using IBM SPSS Statistics through the Corrected Item-Total Correlation validity test and Cronbach’s Alpha reliability test, while qualitative data was analyzed using descriptive-interpretive methods. The research results show that all indicators have validity scores above 0.30 and are therefore considered valid, and the Cronbach’s Alpha reliability score is above 0.70, indicating good internal consistency of the instrument. Furthermore, the interview results indicate that the rubric is considered relevant, clear, and aligned with the learning characteristics of Syofyani’s Minang Payung Dance at the junior high school level. Consequently, the developed assessment rubric is deemed suitable for use as an objective, standardized, and contextually appropriate assessment instrument for dance education rooted in local culture.

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.

This study aims to analyze a comparison of coal stockpile volumes using the DJI Mavic 3 Pro Unmanned Aerial Vehicle (UAV) method versus the truck count method at PT. Mitra Barito. Data collection was conducted through aerial photography using a UAV at altitudes of 60 meters and 70 meters, as well as Ground Control Point (GCP) measurements using GPS. The aerial imagery data was processed using photogrammetry software to generate orthophotos and a Digital Elevation Model (DEM), followed by a geometric accuracy test based on the Geospatial Information Agency Regulation No. 6 of 2018, using the Circular Error 90% (CE90) and Linear Error 90% (LE90) parameters. The research results show that high-quality processing at an altitude of 60 meters yields a CE90 value of 2.1619 meters and an LE90 value of 4.3656 meters, thereby meeting the accuracy standards for RBI maps at a scale of 1:5,000, Class 3 for horizontal accuracy, and a scale of 1:10,000, Class 3 for vertical accuracy. Volume calculations of the stockpile using UAVs yielded a result of 22,750.900 m³, while the truck count method produced a volume of 23,503.300 m³. The volume difference between the two methods was 753.400 m³, with a deviation percentage of 3.2%. Based on the research results, the UAV method is considered capable of providing relatively accurate calculations of coal stockpile volume.

The high volume of fishery waste often poses an environmental burden if not managed appropriately. Conversely, the circular economy concept offers sustainable solutions aligned with Maqasid Shariah principles in environmental preservation (Hifz al-Alam). This study aims to educate and implement the processing of fish waste into high-quality animal feed that meets halal standards (halalan thayyiban). This research employs a qualitative approach using the Asset-Based Community Development (ABCD) method. The stages include socializing the concept of Sharia circular economy, technical training on waste processing, and nutritional content testing of the feed. The findings indicate an 80% increase in community understanding regarding the economic value of waste. Technically, the fish waste processing successfully produced an alternative animal feed with high protein content, free from najis (impurity) through a purification process according to Islamic jurisprudence (fiqh). This education proves that the integration of circular economy and Sharia principles not only reduces environmental impact but also creates sustainable economic added value for local communities.

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.

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.

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.

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.

Choux pastry relies heavily on eggs to form its cavity structure and sensory characteristics. The use of duck eggs can improve physical quality due to their higher protein and fat content compared to chicken eggs. This study aims to analyze the differences in organoleptic quality between choux pastry with 100% chicken eggs and 25% duck egg substitution. The experimental quantitative study involved 50 panelists using a hedonic scale questionnaire and analyzed descriptively. The results showed that 25% duck egg substitution significantly improved texture firmness and provided a more savory taste. However, in terms of aroma, the sample with duck egg substitution scored lower due to a sharper fishy aroma. Although 25% duck egg substitution effectively improved the physical structure, it is recommended to add aromatic ingredients to reduce the distinctive duck egg aroma. These findings provide insights into the development of choux pastry with duck egg substitution to improve physical quality without sacrificing desired organoleptic characteristics.

The high volume of fishery waste often poses an environmental burden if not managed appropriately. Conversely, the circular economy concept offers sustainable solutions aligned with Maqasid Shariah principles in environmental preservation (Hifz al-Alam). This study aims to educate and implement the processing of fish waste into high-quality animal feed that meets halal standards (halalan thayyiban). This research employs a qualitative approach using the Asset-Based Community Development (ABCD) method. The stages include socializing the concept of Sharia circular economy, technical training on waste processing, and nutritional content testing of the feed. The findings indicate an 80% increase in community understanding regarding the economic value of waste. Technically, the fish waste processing successfully produced an alternative animal feed with high protein content, free from najis (impurity) through a purification process according to Islamic jurisprudence (fiqh). This education proves that the integration of circular economy and Sharia principles not only reduces environmental impact but also creates sustainable economic added value for local communities.

Corona Virus Disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The first case was identified in Wuhan, China, in December 2019. The World Health Organization declared COVID-19 a Public Health Emergency of International Concern (PHEIC) on January 30, 2020, and a pandemic on March 11, 2020. This study aimed to determine the results of CT Value images on the first and second swab tests of children with COVID-19 in Indramayu. A descriptive design was used with total sampling, involving 54 respondents. Secondary data were analyzed using univariate analysis. On the first swab test, 27 children (50.0%) had a strong positive Ct Value, 26 children (48.1%) were positive, and 1 child (1.9%) had a weak positive Ct Value. On the second swab test, 23 children (71.9%) were positive, 6 children (18.8%) were strong positive, and 3 children (9.4%) were weak positive. All children tested positive on the first swab, while 32 (59.3%) remained positive and 22 (40.7%) became negative on the second swab. Most children showed decreased infection severity, although some remained positive.

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.

Based on the 2023 Indonesian Health Survey, the prevalence of overweight among adults reached 23.4% nationally and 24.5% in East Java Province. This condition highlights the need for food innovation, particularly high-fiber snacks suitable for overweight adults. This study aimed to develop cereal based on okara (soy pulp) flour and mung bean flour as an alternative high-fiber food product. Organoleptic testing was conducted on 30 semi-trained panelists evaluating color, aroma, texture, and taste using hedonic and hedonic quality tests. Data were analyzed using the Kruskal–Wallis test followed by the Mann–Whitney test, while nutrient content analysis was analyzed using One Way ANOVA followed by Duncan’s post hoc test. The results showed that in the hedonic test, color, texture, and taste parameters were significantly different (p<0.05), while aroma was not significantly different (p>0.05). In the hedonic quality test, color and taste showed significant differences (p<0.05), whereas aroma and texture did not (p>0.05). Overall, formulation 922 was the most preferred by panelists. The nutritional content of the cereal ranged from 407–410 kcal for energy, 76.44–77.53% carbohydrates, 8.91–9.66% protein, 6.80–7.30% fat, 5.20–5.51% moisture, 1.26–1.41% ash, and 9.72–10.90% total dietary fiber. The developed cereal is classified as a high-fiber food and has potential as a healthy snack for overweight adults.