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.

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.

Inclusive education aims to provide equal learning opportunities for all students, including those with special needs, within regular educational settings. However, mathematics learning in inclusive classrooms remains challenging because mathematical concepts are often abstract and require logical reasoning that may not be easily accessible to learners with diverse cognitive characteristics. Ethnomathematics has emerged as an alternative approach by integrating cultural practices, local wisdom, and students’ daily experiences into mathematics instruction, creating more meaningful and accessible learning environments. This study aims to analyze the development, implementation patterns, opportunities, and research gaps related to ethnomathematics in inclusive mathematics learning. A literature review method was employed by examining scientific publications from 2020–2025 obtained from Google Scholar, Scopus, ERIC, Springer, and ProQuest databases. Data were analyzed through content analysis involving reduction, classification, interpretation, and synthesis. The findings indicate that ethnomathematics has been implemented through cultural artifacts, digital teaching materials, and project-based contextual learning. The approach supports inclusive learning through multi-representational access, instructional adaptations, scaffolding strategies, and collaborative teaching practices aligned with Universal Design for Learning principles. Furthermore, ethnomathematics enhances students’ motivation, conceptual understanding, mathematical literacy, and cultural identity. Nevertheless, studies focusing on disability-specific adaptations and long-term learning outcomes remain limited and require further investigation.

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.

The growth of information technology has driven changes in consumer behavior, one of which is through e-commerce platforms such as Shopee. This phenomenon has generated a large number of customer reviews, including those for local cosmetic products such as Wardah. These reviews serve as an important source of information for understanding customer perceptions and satisfaction levels. However, manual analysis of large and linguistically diverse datasets is inefficient and potentially subjective. This study aims to implement the multi-category Naive Bayes algorithm to classify the sentiment of Wardah product reviews on Shopee into three categories: positive, negative, and neutral. The data were collected using a web scraping technique and processed through a series of preprocessing stages including case folding, tokenization, stopword removal, stemming, and text cleaning. Subsequently, term weighting was performed using the TF-IDF method prior to classification. Model performance was evaluated using a confusion matrix as well as accuracy, precision, and recall metrics. The results indicate that the multi-category Naive Bayes algorithm achieved an accuracy of 86.00%, a precision of 86.63%, and a recall of 98.24%. This approach can assist business practitioners in objectively understanding customer opinions and support decision-making in business strategy and product development.

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.

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.

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.

This study investigates the decoupling phenomenon between ESG (Environmental, Social, and Governance) sustainability reporting and communal Tri Hita Karana (THK) sustainability practices in a Rural Bank in Bali. Through Ethnographic Content Analysis (ECA) of official documents from BPR Luhur Damai covering 2023–2025, this study identifies that the Sustainability Report (SR), prepared strictly according to Financial Services Authority Regulation (POJK) 51/2017, does not incorporate substantial THK practices, namely banten (ceremonial offerings) Rp131.6 million, dana punia (religious donations) Rp8.5 million, and monthly banjar (communal community unit) contributions, producing a Hindu religious expenditure to formal Social and Environmental Responsibility (SER) ratio of 10:1. Drawing on the Institutional Logics perspective, this study identifies four decoupling mechanisms: (1) cognitive, namely THK as taken-for-granted, not perceived as “sustainability”; (2) administrative, namely departmental silos between Compliance and General Affairs; (3) template, namely POJK 51/2017 provides no space for local wisdom; and (4) capacity, namely limited Human Resources (HR) and institutional capacity. These findings lead to the concept of “invisible sustainability,” that is, real sustainability contributions that are invisible to conventional reporting frameworks, and “cultural accounting gap,” that is, the absence of accounting categories for local cultural-religious contributions. The theoretical contribution is demonstrating that decoupling in Global South contexts is not merely symbolic compliance but results from structural misalignment between transnational and communal logics that renders local sustainability contributions institutionally invisible.

This study aims to describe the implementation of instilling Islamic Religious Education values in the aspects of the Qur’an and Aqidah at TPQ Fattuhul Qulub, Doyo Baru District. The background of this research is based on the importance of Islamic religious education in shaping children’s character and morals from an early age amid the decline in the morality of the younger generation. TPQ, as a non-formal educational institution, has an important role in instilling Islamic values through learning the Qur’an, aqidah, and morals. However, in its implementation, several obstacles are still found, such as the low understanding of students regarding religious values, varying abilities in reading the Qur’an, and the lack of support from family and community environments.This study employed a descriptive qualitative approach. Data were collected through observation, interviews, and documentation at TPQ Fattuhul Qulub, Doyo Baru District. The focus of the research includes the planning, implementation, and evaluation processes of instilling Islamic Religious Education values in the aspects of the Qur’an and Aqidah.The results of the study indicate that the instillation of Islamic Religious Education values has been carried out through structured stages of planning, implementation, and evaluation. The learning process was conducted using the methods of Iqra’, tahsin, tahfidz, talaqqi, lectures, role modeling, habituation, and exemplary stories. In the Qur’anic aspect, students were guided to read the Qur’an according to tajwid rules and memorize short surahs, while in the Aqidah aspect, students were taught the pillars of faith, the attributes of Allah and His Messenger, and the formation of Islamic morals. The evaluation results showed improvements in Qur’anic reading skills, memorization, understanding of aqidah, and changes in students’ religious behavior, such as becoming more disciplined, polite, and diligent in worship. This success was supported by the exemplary behavior of the teachers and the involvement of parents in the learning process.

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.

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.

Village governance plays an important role in supporting the effectiveness of development planning and improving community welfare. This study aims to analyze financial management governance and the process of preparing the Village Revenue and Expenditure Budget (APBKal) in Kalurahan Poncosari, Bantul Regency, for the 2025 fiscal year. This research employs a qualitative approach using a case study method, involving in-depth interviews with key informants and documentation analysis of relevant regulations and financial reports. The results show that financial management in Kalurahan Poncosari has been implemented systematically through the stages of planning, implementation, administration, reporting, and accountability in accordance with applicable regulations. The planning process is conducted in a participatory manner through tiered community deliberations, such as hamlet-level deliberations and village development planning deliberations, involving residents. In addition, the use of digital systems such as E-RAB and Siskeudes supports transparency and administrative order. However, challenges remain, including limited budget flexibility due to mandatory programs from central and regional governments, limited human resource capacity, and shifts in community participation patterns. In conclusion, the governance of APBKal in Kalurahan Poncosari demonstrates compliance and accountability; however, improvements in administrative capacity and fiscal flexibility are needed to better respond to community needs.

Usability evaluation plays a crucial role in ensuring the quality of digital systems, particularly in terms of comfort, effectiveness, and ease of use. Instruments such as the System Usability Scale (SUS), User Experience Questionnaire (UEQ), and Heuristic Evaluation (HE) are widely used in modern usability studies. This research conducts a Systematic Literature Review (SLR) to identify patterns and trends in the use of these instruments. A total of 27 initial studies were collected, and 16 were selected through the PRISMA screening procedure. The findings show that UEQ is the most frequently used instrument, especially in Learning Management Systems (LMS) and academic platforms, while SUS is commonly applied to mobile applications and digital libraries for rapid usability assessment. HE is effective in revealing fundamental interface issues such as non-intuitive navigation and layout inconsistencies. Overall, digital systems perform well in Efficiency and Perspicuity, but consistently show low scores in Novelty. This study provides an integrative knowledge map that highlights cross-instrument insights and supports the development of more intuitive, innovative, and user-centered digital systems