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The presence of Generation Z, who grew up entirely in the digital era, has triggered a fundamental transformation in Muslim religious practices, where social media has replaced conventional religious institutions as the primary source of religious information. This condition creates a paradox: Gen Z has become a generation that is highly religious online, yet is often disconnected from physical communities and traditional religious authorities. This study aims to analyze the character of Muslim Gen Z religiosity, identify its forming factors, and examine the impact of the digital era on their religiosity. This study employed a descriptive qualitative approach using a library research method. The findings indicate that Muslim Gen Z religiosity is characterized by personalization, flexibility, and digital spirituality, strongly influenced by social media. These characteristics are shaped by massive digital technology accessibility, the effectiveness of contextual Islamic preaching on platforms such as TikTok, as well as spiritual needs and social pressure from the digital environment. The digital era brings positive impacts in the form of increased accessibility and religious literacy, but also negative impacts including shallow religious understanding, vulnerability to information bias, and potential exposure to extreme ideologies. This study implies the importance of an integrated digital religious literacy strategy through critical thinking-based Islamic Religious Education curriculum reform, enhancement of educators' digital capacity, and cross-sector collaboration to strengthen Gen Z's moderate and reflective religious understanding.

This research aims to analyze the implications of the transition in lease accounting standards to PSAK 73 on tax efficiency levels and net income structures within the retail industry, focusing on PT Mitra Adiperkasa Tbk as case studies. Employing a descriptive quantitative method, this research compares financial statement data from the 2017-2024 period to evaluate shifts before and after the regulation's enforcement. The results reveal that the implementation of PSAK 73 successfully improved corporate tax efficiency, characterized by a decrease in the Effective Tax Rate (ETR) below the statutory corporate tax rate. This was achieved by leveraging temporary differences that resulted in the recognition of deferred tax assets, providing a strategic advantage in the form of tax deferral. On the other hand, the application of this standard caused significant pressure on net profit during the initial transition phase due to the front-loading expense pattern derived from right-of-use asset depreciation and lease liability interest.

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.

The rapid growth of digital commerce has encouraged companies to integrate digital and physical marketing channels to create seamless and consistent customer experiences. This study aims to analyze the role of integrated marketing channels through omnichannel strategies, the utilization of Artificial intelligence (AI), and their impact on customer experience in the context of digital commerce. The study employs a Systematic literature review (SLR) approach by examining relevant scholarly articles related to omnichannel marketing, AI technologies, and customer experience. The findings indicate that integrated marketing channels supported by AI enhance service personalization, customer engagement, operational efficiency, and the quality of interactions between companies and customers. Furthermore, the implementation of omnichannel strategies contributes to higher customer satisfaction and loyalty by providing a more connected experience across multiple customer touchpoints. However, the implementation of integrated marketing channels still faces several challenges, including fragmented channel integration, technological complexity, high investment requirements, and concerns regarding customer data privacy and security. Therefore, effective management of integrated marketing channels is essential for improving customer experience while creating sustainable competitive advantages for companies in an increasingly dynamic digital era.

This research was designed to explore the extent to which public beliefs influence the implementation of Digital traceability  systems in the halal industrial sector. The approach used was quantitative with a survey method, where questionnaires were distributed to 60 respondents who were consumers of halal products in Indonesia. Data were analyzed using simple linear regression via Microsoft Excel. Research findings indicate that public confidence has a positive and significant influence on the adoption of Digital traceability  systems, with a regression coefficient of 0.476 and a significance level of 0.000 (<0.05). In addition, the coefficient of determination (R Square) value of 0.219 indicates that public confidence contributes 21.9% to the implementation of the Digital traceability  system, while the rest is determined by other factors that were not researched. These findings confirm that public trust is an important element in encouraging acceptance of digital technology, especially in the halal industry which relies heavily on transparency and consumer confidence. Thus, implementing a Digital traceability  system that is supported by information openness and easy access to technology can be an effective strategy to strengthen consumer trust while expanding technology adoption.

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.

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.

This research is motivated by the problems in manual attendance systems at schools, which remain vulnerable to fraud, time-consuming, and inefficient. The expected solution is to develop an automated attendance system based on face recognition that can operate in realtime with high accuracy. The research object is vocational high school students, with the applied method implementing the YOLO v10 algorithm for face detection, followed by the face_recognition library for identification. The instruments used include an Imou CCTV camera as the input device, a mid-range laptop as the hardware platform, and Python with SQLite as the software environment for data processing and attendance storage. The results show that the developed system achieved an average face detection accuracy of 96% under normal lighting and 91% under low lighting, with an average processing speed of 27 FPS. The implementation of an anti-duplication feature also ensured data validity by allowing each student to be recorded only once per day. In conclusion, the use of YOLO v10 in face-based attendance proved to be effective, efficient, and capable of reducing fraud. The implication of this study is that the system can be applied in both Islamic boarding schools and general schools as a modernization of attendance systems, with a recommendation for further development through web-based application and cloud database integration.

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 aims to examine the debate regarding the effectiveness of traditional learning methods in science education at Madrasah Ibtidaiyah (MI) amid the development of educational digitalization. The study employed a qualitative approach with a case study design conducted in Jember Regency for three months, from February to April 2026. The research informants consisted of 16 participants, including madrasa principals, teachers, parents, and community members. Data collection techniques were carried out through interviews, observations, and documentation, which were then analyzed using descriptive qualitative techniques. The findings revealed that traditional methods are still considered effective in helping students understand basic science concepts because the learning process is systematic and easy to comprehend. However, limited access to technology in several schools remains an obstacle to the equal implementation of digital learning. In addition, although digital learning can increase students’ motivation and engagement, it does not necessarily lead to an optimal improvement in conceptual understanding. Therefore, this study concludes that a combination of traditional and digital learning methods is the most appropriate approach in science learning at elementary schools and Madrasah Ibtidaiyah, considering students’ needs as well as the availability of facilities and infrastructure. structure.

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.

The physiological changes that occur during pregnancy, both physically and mentally, may be rather uncomfortable, particularly in the second and third trimesters. Pregnant women often report back discomfort, muscular aches, trouble sleeping, excessive exhaustion, and irregular sleep patterns. Mothers' physical and mental health as well as the health of their unborn children may be significantly impacted by inadequate sleep quality during pregnancy. To enhance comfort and the quality of sleep during pregnancy, one non-pharmacological technique is to use an aromatherapy maternity pillow. An ergonomic and ecologically sustainable invention to enhance mother comfort during pregnancy was the goal of this research, which intended to produce a Pregnancy Pillow Therapy product with pineapple leaf fiber and aromatherapy. A descriptive research design using a prototype creation technique was used in this study. The stages of the research included problem identification, literature review, product design, material selection, prototype manufacturing, and product evaluation. Data were collected through literature studies and observations related to sleep discomfort in pregnancy, maternity pillow utilization, aromatherapy therapy, and pineapple leaf fiber characteristics. The developed product was designed ergonomically to support the back, abdomen, waist, and legs of pregnant women. The addition of aromatherapy was intended to provide a relaxing effect and improve sleep quality. The use of pineapple leaf fiber also supports environmentally friendly product innovation through agricultural waste utilization. The results indicate that Pregnancy Pillow Therapy has the potential to become a supportive product for improving comfort and sleep quality among pregnant women. Further studies are recommended to evaluate product effectiveness directly among pregnant women.

 Background: Perineal pain due to episiotomy is a common complaint experienced by postpartum mothers and can affect patient comfort, mobility, and recovery. Nonpharmacological pain management is needed to help reduce discomfort with minimal risk of side effects. Objective: This study aimed to determine the effectiveness of applying cold compresses using ice packs in reducing perineal pain intensity in postpartum mothers with episiotomy. Methods: The study used a descriptive case study design in three vaginal postpartum patients with episiotomy who were treated in the postpartum ward. The intervention involved applying cold compresses using ice packs to the perineal area for 10–15 minutes, as per nursing procedures. Pain was measured using the Numeric Rating Scale (NRS) before and after the intervention. Findings: The results showed a decrease in pain intensity in all patients after the application of cold compresses. Patient P1 experienced a decrease in pain score from 5 to 4, patient P2 from 6 to 5, and patient P3 from 5 to 4, with an average decrease of 1 point. Implications: Cold compresses using ice packs have the potential to be an effective non-pharmacological nursing intervention to help reduce perineal pain and improve the comfort of postpartum mothers with episiotomies during the care period.

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 research is grounded in the understanding that employees are the core of organizational sustainability, and their job satisfaction is shaped not only by daily tasks but also by the organization’s ability to manage workload and support balance between work demands and personal life. This study aims to analyze the effect of workload and work-life balance on employee job satisfaction at the Class I Correctional Center (Bapas) Makassar. Using a quantitative approach, data were collected through questionnaires distributed to 54 employees and analyzed using multiple linear regression assisted by SPSS Statistics 25. The research variables consist of workload and work-life balance as independent variables, and job satisfaction as the dependent variable. The findings indicate that workload has a positive and significant effect on job satisfaction, suggesting that employees’ perception of being able to complete tasks effectively can increase their comfort and confidence at work. Work-life balance also shows a positive and significant influence, indicating that the ability to manage both work responsibilities and personal life contributes directly to greater feelings of satisfaction, stability, and motivation in performing duties. Simultaneously, both variables significantly affect job satisfaction, emphasizing the importance for organizations to regulate workload proportionally while providing space for employees to maintain a healthy life balance. These findings highlight that effective workload management and support for work-life balance are crucial organizational investments to foster a healthy, productive, and employee-centered work environment.

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.