Publication Search

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.

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 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 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.

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.

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 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.

Gesture  recognition  technology  is  used  to  detect  movements  through  image processing,   enabling  computers  or digital devices to understand and interpret human  body  movements  as  input  or  commands.   This  technology  has  great potential  to bridge communication between the deaf community and individuals without   hearing   impairments,    enhancing  interaction  and  enriching  mutual understanding between the two.  However,  the accuracy ofgesture recognition is often  affected  by variations in the distance between hand landmarks.  Based on this problem,  this research proposes a methodfor stabilizing the measurement of distances between landmark points  in gesture recognition through a polynomial regression  approach.   Specifically,   the  distance  between  hand  landmarks  is calculated and stabilized using polynomial  regression to improve the accuracy of gesture recognition.  This method is implemented using the MediaPipeframework to detect and track hands in real-time,  and the OpenCV library to manage video. The  research  results  show  that  this  approach  can  significantly  improve  the stability  and accuracy  of gesture detection.   The developed system successfully detects gestures for  letters A  through F with a high accuracy  rate,  averaging above 98,3%.  The use ofpolynomial regression helps enhance detection accuracy by reducing noise in the landmark data.

Computers in one of the network companies at PT. Estrada uses the Fortinet operating system. The final result expected through this implementation is to comprehensively see the capabilities of the firewall on Fortinet in overcoming the problem of blocking social media applications and streaming platforms during working hours. Blocking the application in question is the ability to filter web processes such as Facebook, Instagram, YouTube, etc. In the tests carried out, web filtering was able to block applications on social media and streaming platforms, which proves that the performance of web filtering is quite good. In analyzing web filtering performance, use the office hour rule tool by carrying out the rule schedule in the Fortinet network and displaying all the information in detail. The final result obtained in the network application filtering simulation process using Fortinet is that every network sent cannot be entered (blocked) on both social media applications and streaming platforms.

The rapid growth of digital technology and social media has reshaped communication practices, consumer behavior, and marketing approaches, particularly among Generation Z, who are widely recognized as digital natives. For this generation, social media serves as a major channel for obtaining information and exploring product references before making purchase decisions. In this environment, influencer marketing has emerged as a prominent promotional approach because it can foster audience interaction, emotional connection, and trust more effectively than conventional advertising. This study examines the role of influencer marketing in shaping the digital behavior of Generation Z in Indonesia. A qualitative literature review method was employed by analyzing secondary sources drawn from academic journals, books, and related publications. The findings indicate that influencer marketing has a meaningful influence on information-seeking behavior, decision-making processes, and the level of digital engagement among Generation Z. Influencers are commonly viewed as more credible, relatable, and authentic sources of information. Furthermore, the effectiveness of influencer marketing is determined more by credibility, authenticity, and the quality of interaction than by follower count alone. These findings suggest that companies should adopt digital marketing strategies that are more interactive, personalized, and relationship-oriented in order to engage Generation Z more effectively.

The rapid digitalization in Indonesia, reaching 180 million active social media users, has not been accompanied by adequate security system resilience, thereby triggering massive data breach risks. This study aims to analyze the privacy navigation mechanisms of the digital society as an instrument for mitigating information leaks. The method used is descriptive qualitative with a literature study (library research) approach, which examines primary and secondary literature related to regulations, digital behavior, and user psychological factors. The research findings indicate that privacy navigation in the digital era has not operated optimally due to the dominance of social existence needs, which triggers the privacy paradox phenomenon. Although users possess knowledge regarding cyber risks, the desire for social validation through self-disclosure often overrides technical protection logic. The practice of using secondary accounts (second accounts) was found to be a form of manual navigation, yet its effectiveness remains dependent on individual digital literacy. The implications of this research emphasize that mitigating information leaks requires the integration of critical user awareness, platform governance transparency, and consistent law enforcement through the PDP Law. Digital awareness must transform into reflexive protective behavior to maintain informational sovereignty in cyberspace.  

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

Effective communication is essential for teamwork effectiveness in healthcare, particularly in inpatient units where patient care requires coordination among multiple professionals. Internal data from Hospital X showed that more than 50% of medical incidents in the last two years were related to miscommunication among healthcare workers. In addition, the 2024 employee satisfaction survey reported that workplace communication satisfaction was only 62%, below the hospital target of 75%. This study aimed to examine the influence of effective communication on teamwork effectiveness with interprofessional collaboration as an intervening variable at Hospital X.This study used a quantitative cross-sectional design involving 226 healthcare workers, including doctors, nurses, and medical support staff. Data were analyzed using the Three-Box Method and PLS-SEM. The findings revealed that effective communication significantly influenced teamwork effectiveness both directly and indirectly through interprofessional collaboration. Interprofessional collaboration also acted as a significant mediating variable between communication and teamwork effectiveness. These results indicate that communication is a key foundation for strengthening collaboration and improving teamwork in hospitals. Therefore, hospitals should enhance communication training and interprofessional collaboration programs to improve healthcare quality and patient safety

Tourism in Bali, as the backbone of the regional economy, absorbs more than 50% of the workforce and gives rise to complex labor dynamics. Although Law No. 13 of 2003 on Manpower and Law No. 21 of 2000 on Trade Unions guarantee worker protection and freedom of association, the reality on the ground is still marked by the misuse of fixed-term employment contracts (PKWT), violations of outsourcing regulations, and both structural and cultural barriers to union formation. This article employs a normative juridical approach, using statute, conceptual, and case-based methods to analyze the regulatory framework and the responses of workers, employers, and government actors in addressing industrial relations disputes within Bali’s tourism sector. The findings reveal a significant gap between written legal norms and actual practice: repeated extensions of PKWT, outsourcing of core tasks such animal caretaking, union-busting through dismissal of union members, and weak law enforcement at regional level. Trade unions play a role in mediation and advocacy, but their performance is hindered by employer intimidation, low legal literacy among workers, and limited institutional support. Bridging this gap requires strict oversight, legal education for workers, as well as the strengthening of union capacity and the authority of local governments.

The phenomenon of tourist destinations experiencing a decline in popularity after a viral phase (post-viral stagnation) poses significant challenges to the sustainability of urban tourism. This study aims to test a structural model for destination reactivation in Kampung Pelangi, Semarang, focusing on the role of digital transformation in strengthening destination resilience through the mediation of competitive advantage. Using a quantitative approach, data were collected from 150 respondents and analyzed using Structural Equation Modeling (SEM-PLS). The results of the hypothesis testing indicate that digital transformation has a positive and significant influence on competitive advantage (β = 0.495; t = 5.820; p < 0.001) and destination resilience (β = 0.312; t = 3.450; p < 0.001). Furthermore, competitive advantage was found to have a strong impact on resilience (β = 0.542; t = 7.115; p < 0.001). A mediation test demonstrated that competitive advantage significantly mediated the relationship between digital transformation and resilience (β = 0.268; t = 4.890; p < 0.001). This model demonstrated a predictive power of 61.2% (R2 = 0.612) for destination resilience. This finding emphasizes that post-pandemic destination reactivation requires the integration of digital capabilities that can restore differentiation and unique value propositions to achieve long-term resilience.

This study examines the effectiveness of mediation in resolving divorce cases at the Banda Aceh Sharia Court and the Jantho Sharia Court based on Supreme Court Regulation No. 1 of 2016. This study aims to analyze the implementation of mediation, identify factors influencing its effectiveness, and determine efforts to improve the effectiveness of mediation in divorce cases. The method used is a legal-empirical approach with a qualitative methodology through interviews and literature review. The research results indicate that the implementation of mediation has been in accordance with the provisions of Supreme Court Regulation No. 1 of 2016; however, its success rate remains low, rendering it ineffective. Factors influencing the effectiveness of mediation include the mediator’s competence, the parties’ strong desire to divorce, prolonged domestic conflicts, and the influence of religious figures within the community. Efforts to improve the effectiveness of mediation are carried out by enhancing the competence of mediators, educating the parties on the benefits of mediation, and optimizing the mediation process within the Sharia Court.

The implementation of Electronic Medical Records (EMR) is a key component of healthcare digital transformation, as mandated by the Minister of Health Regulation Number 24 of 2022. EMR is expected to enhance efficiency, accuracy, and the quality of nursing services. However, its implementation at M Hospital continues to face challenges, particularly in terms of infrastructure readiness and the digital literacy of nursing staff. This study aims to analyze the effects of infrastructure readiness and digital literacy on the implementation of nursing EMR, with perceived usefulness as an intervening variable. A quantitative cross-sectional design was employed, involving all nurses at M Hospital, with a total sample of 100 respondents using total sampling. Data were collected through structured questionnaires and analyzed using SEM-PLS. The results indicate that infrastructure readiness and digital literacy have both direct and indirect effects on EMR implementation through perceived usefulness (p < 0.05). Among the independent variables, infrastructure readiness demonstrates the strongest influence. These findings support existing theories and prior research. From a managerial perspective, the results highlight the importance of improving infrastructure to optimize EMR implementation. In conclusion, infrastructure readiness and digital literacy significantly influence the successful implementation of nursing EMR, with perceived usefulness acting as a key mediating factor.

This research examines digital charity practices within Christian media communication on YouTube, focusing on two distinct donation formats: marapthon live stream donations (e.g., 24‑hour fundraising events) and sermon‑based donations (offerings collected during or after online worship services). Despite the rapid growth of faith‑based online giving, a critical problem remains: the absence of an integrated system that aligns these two donation models with Christian values of transparency, accountability, and community stewardship. Existing platforms often treat live marapthon and sermon donations separately, leading to fragmented donor experiences and inefficient fund utilization. Therefore, this study aims to develop a conceptual framework for an integrated digital charity system by comparatively analyzing media communication strategies in both donation contexts. The proposed method is a netnographic comparative analysis, involving systematic observation of YouTube comments, chat logs, and video descriptions from 10 Christian channels (5 marapthon‑focused, 5 sermon‑focused) over six months, supplemented by semi‑structured interviews with content creators and donors. The main findings reveal that marapthon donations emphasize urgency and real‑time social proof, while sermon donations rely on theological framing and pastoral trust. The synthesis proposes a hybrid system architecture incorporating real‑time donation tracking, automated acknowledgment, and weekly theological reflection modules. In conclusion, integrating both models into a single development framework enhances donor engagement and aligns digital charity with Christian communication ethics, offering practical guidelines for church‑based YouTubers and platform developers.

The swift proliferation of short-video-centric social media, notably TikTok, has revolutionized the educational landscape by facilitating novel methods of knowledge production, dissemination, and interpretation. This phenomenon denotes a transition in media and signifies an epistemological transformation in educational practices within the digital age. This study seeks to analyze the representation and interpretation of knowledge in TikTok educational content using a qualitative methodology grounded in an interpretive case study framework. Data were gathered via digital participant observation, comprehensive interviews, and document analysis involving 12 participants, comprising educational content creators and active TikTok users in higher education settings. Thematic data analysis was performed utilizing a Multimodal Critical Discourse Analysis framework to elucidate the interplay among visual, verbal, and auditory components in the construction of meaning. The results show three main patterns: the conflict between quick understanding and deep knowledge, the importance of emotional multimodal experiences in learning, and the negotiation of knowledge authority in changing digital spaces. These results indicate that learning via TikTok encompasses not only cognitive aspects but also intricate emotional, aesthetic, and social dimensions. This study theoretically enhances multimodal discourse analysis by integrating users' subjective experiences, while practically informing the advancement of critical digital literacy and the design of social media-based learning. Moreover, this study facilitates additional investigation into algorithmic dynamics, digital identity, and the evolution of learning methodologies within platform-centric contexts.