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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.

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.

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 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.

Public Wi-Fi services (Wifi_STAKat) at the State Catholic College of Pontianak continue to face technical issues, such as network speed and connection stability, as well as non-technical challenges, including the responsiveness of administrators to user complaints. This study aims to analyze user satisfaction based on the five Servqual dimensions and to map improvement priorities using the Importance–Performance Analysis (IPA) method. The relationship between Servqual and IPA is explained by mapping GAP values (perception–expectation) into the four IPA quadrants to determine the urgency level of service improvements. A 4-point Likert scale was used to avoid neutral responses and strengthen the clarity of respondents’ perceptions. The results show that all dimensions have negative GAP scores, particularly responsiveness and reliability, which are directly related to technical indicators (speed, stability, coverage) and non-technical factors (responsiveness to complaints, ease of access. The study recommends integrating an IT-based monitoring system and increasing network capacity to improve service quality.

The Operational Aspect in Islam emphasizes that production activities must benefit humanity or be related to meeting human needs. For example, the selection of raw materials should not come from haram sources, the production process should be free from activities prohibited by Sharia, production should not be excessive, and there should be no waste. The Islamic view on human resources emphasizes that all workers are huan beings, not robots or business tools. A A Business Feasibility Study refers to an analysis of a business plan, both prior to its implementation and once the business is operating on a regular basis. The aim of this study is to evaluate the operational aspects and human resource management within the feasibility study of the Islamic-based convection business, Brand 57 Busana Pekalongan. This research employs a qualitative approach. The data were collected through field research, which involves conducting the study directly at the site where the phenomena related to the research problem occur. The techniques applied for data collection include observation, interviews, and documentation The result of this study indicate that the Brand 57 Busana pekalongan is feasible in terms of operational and human resource management aspects from a Sharia businesss feasibility study perspective because it has chosen a strategic location, good product quality, adequate production capacity, and technology utilization. In addition, the brand 57 Busana Pekalongan convection is deemed permissible for operational production activities from an Islamic perspective, such as production activities based on Islamic values and Maqashid Syariah. In the implementation of human resources management, the Brand 57 Busana Pekalongan convention has implemented job descriptions, a Muslim work ethic, a fair and decent salary distribution system.

This study aims to analyze the skills gap in the era of the Industrial Revolution 4.0 and the factors influencing it. The research employed a descriptive-analytical approach based on literature studies and the author’s analysis of the dynamics of formal education curricula and the development of industrial needs. The findings indicate that formal education curricula that are less adaptive to technological developments are the main factor causing the skills gap. In addition, the slow adaptation to technology in educational and industrial environments, as well as the unequal distribution of skills development across various regions in Indonesia, further widen the gap between graduates’ competencies and labor market demands. This condition affects the low readiness of the workforce in facing digital transformation and global competition. The study concludes that the skills gap is a strategic issue requiring serious attention from the government, educational institutions, and the industrial sector. Curriculum reform that is responsive to technological advancements, strengthening digital literacy, and ensuring equal access to training and skills development are essential steps to improve the competitiveness of Indonesian human resources. These efforts are expected to support poverty reduction, reduce social inequality, and achieve the long-term national development goals toward Indonesia Golden Vision 2045.

This study aims to analyze the environmental impact of waste generated by the gecko processing home industry based on environmental law regulations in Indonesia. In its processing activities such as lizards and snakes. These activities produce organic waste in the form of animal organs, blood, body fluids, and other waste in the materials that are later utilized as catfish feed. The research method used is qualitative with a normative juridical approach, statutory approach, and literature study. Data were obtained from laws and regulations , scientific journal, and environmental law literature. The results of the study indicate that the disposal of waste into rivers can increase Biological Oxyen Demand and Chemical Oxygen Demand (COD) levels, cause unpleassant odors, and reduce water quality and public health. The utilization of waste as catfish feed can reduce the amount of waste disposed of, however, it still requires hygienic processing to prevent biological risks. From the perspective of environmental law, the direc disposal of the waste into rivers is not in accordance with Law No. 32 of 2009 concerning Environmental Protection and Management and Government Regulation No. 22 of 2021 concerning the Implementation of Environmental Protection and Management. Therefore, better waste management is needed through proper waste treatment, increased awareness among business actors, and goverment supervision so that home industries can operate sustainably and in an environmentally friendly manner

The optimal therapeutic impact of local vaginal drug delivery systems is strongly influenced by the physical characteristics balance of Solid Vaginal Suppositories. A comprehensive review regarding the comparison of mechanical profiles, specifically melting time and crushing strength parameters, from various base classifications constitutes the primary objective of this literature research. The implementation of a Literature Review study design was executed through the extraction of empirical data from twelve experimental journals published within the last ten years. Excessively rapid phase transformation characteristics at physiological basal temperatures and low compression resistance were consistently demonstrated by lipophilic bases such as Oleum Cacao. The risk of structural deformation during the distribution process is highly susceptible to unmodified lipid preparations. High surface elasticity accompanied by a delay in molecular hydration duration reaching 120 minutes was recorded in the utilization of Glycerinated Gelatin Base. Structural rigidity exceeding 4 kgF and disintegration time efficiency under 60 minutes were optimally demonstrated by Polyethylene Glycol (PEG) Base. An enhancement in mechanical resistance against external shocks during the storage period is offered by the thorough modification of the synthetic polymer ratios. Therefore, the determination of the PEG base as the most optimal material is recommended to maintain the quality stability of pharmaceutical products. Compendial regulation standards regarding the physical strength testing of pharmaceutical preparations must be obeyed by every institution to ensure long-term treatment effectiveness. Thus, the alignment between active substance release duration and physical preparation endurance can be realized for absolute patient comfort.

This study aims to analyze trends in scientific publications related to the application of green banking in financing products within Islamic banking in Indonesia during the 2019–2024 period. Using a bibliometric analysis method based on Google Scholar data and mapping via VOSviewer software, this study evaluates 60 selected articles. The study results indicate a significant annual increase in publications, with a primary focus on integrating green banking principles into Islamic financing policies, their impact on profitability, and the role of technology in supporting green banking. Additionally, the study found that environmental sustainability, green financing, and digital transformation are the most dominant themes in the development of green banking research within Islamic banking. Bibliometric network analysis indicates a strong interconnection between the concepts of green finance, sustainable banking, and Islamic banking in supporting sustainable economic development. This study also identifies opportunities for further research related to the effectiveness of green banking implementation on the financial performance and social responsibility of Islamic banking. These findings contribute to the development of green finance literature in the Islamic finance sector and serve as a strategic reference for regulators and practitioners in implementing sustainable banking policies in the future.

This study analyzes normative conflicts in the evolution of local government regulations from Law No. 22 of 1999 to Law No. 23 of 2014, along with the changes following the Job Creation Law. The research background is grounded in the phenomenon of policy pendulum swings, which indicate a shift from radical decentralization toward structured centralization, as well as the disruptions caused by the omnibus law approach. The research aims to identify and classify vertical and horizontal normative conflicts and formulate recommendations for regulatory harmonization. The method used is normative legal research with a legislative and conceptual approach, employing Hans Kelsen’s theory of antinomy as an analytical tool. The research findings identified two main forms of normative conflict: first, a vertical conflict between Law No. 22/1999, which advocates for the broadest possible autonomy, and Law No. 23/2014, which strengthens central control through the concepts of absolute affairs and NSPK, creating a paradox of centralization within decentralization. Second, a horizontal conflict following the Job Creation Law, which centralizes regional licensing authority through the OSS system and revives Article 251 regarding the annulment of regional regulations that have been declared unconstitutional by the Constitutional Court. The implications of this research recommend revising problematic articles and strengthening the role of regions within a just decentralization system.

The development of digital technology and internet penetration are driving changes in consumer behavior and demanding that MSMEs not only adapt but also understand the operational motives and marketing channel integration strategies to face competition in the digital era. This study aims to examine the operational motives and technical adaptation processes of Micro, Small, and Medium Enterprises (MSMEs) in Bali in integrating online and offline marketing channels through an omnichannel strategy to respond to intense market competition in the digital era. This descriptive qualitative research uses a multiple case study approach in three different MSME sectors: goods maintenance services, creative services, and fashion retail. Data collection was conducted through direct observation, in-depth interviews, and a review of digital activities, then analyzed through single-case and cross-case stages. The results show that MSMEs optimize digital channels as instruments to expand reach and trigger consumer emotional engagement through a visual storytelling approach, while physical stores are crucially maintained as points of sensory validation. Functional integration has been proven to accommodate the shift in consumer behavior towards a hybrid decision-making pattern. In this pattern, consumers conduct information searches and initial evaluations online, but tend to shift to offline interactions to validate quality and finalize transactions to minimize purchase risk. In conclusion, cross-channel operational synergy has proven essential in boosting the competitive advantage of MSMEs.

This study examines the implementation of Islamic business ethics in e-commerce transactions, with a specific focus on the Shopee platform. The research is motivated by the increasing growth of digital commerce accompanied by various ethical issues such as product misrepresentation, lack of transparency, and weak accountability of sellers. The objective of this study is to analyze how ethical principles, including honesty, fairness, and responsibility, are applied in online transactions. This research employs a qualitative approach using a systematic literature review method by analyzing 20 relevant scholarly articles published between 2020 and 2026. The findings reveal that although the platform provides adequate transactional systems, ethical violations are still prevalent, primarily due to information asymmetry between sellers and consumers. Trust emerges as the most influential factor in purchasing decisions, while perceived risk and security play supporting roles in shaping consumer behavior. Additionally, economic incentives such as promotions often override ethical considerations in influencing consumer decisions. The study implies that strengthening ethical enforcement, enhancing platform supervision, and improving consumer digital literacy are essential to ensure a sustainable and trustworthy e-commerce ecosystem.

This study aims to develop a sentiment classification method for YouTube user comments related to the game Love and Deepspace using the Naïve Bayes algorithm, focusing on improving the text data processing and understanding user perceptions. Comment data were collected through scraping from YouTube videos, followed by preprocessing including text cleaning, normalization, stopword removal, stemming, and translation into English. Initial labeling was conducted using TextBlob, then the data were randomly sampled for training the Naïve Bayes model. Evaluation involved comparing sentiment distributions and visualization using Word Cloud and bar charts. The Naïve Bayes model achieved an accuracy of 77.36% in sentiment classification. The sentiment distribution shows differences between TextBlob (positive: 1,011, neutral: 1,312, negative: 575) and Naïve Bayes (positive: 901, neutral: 1,627, negative: 370), with Naïve Bayes being more conservative. The Word Cloud visualization identifies dominant words such as "bang," "game," and "main," while the bar chart shows the largest proportion of neutral sentiment. Naïve Bayes is effective for sentiment classification on informal comment data, with significant differences from rule-based methods like TextBlob. This research contributes to the development of text data processing techniques and user perception analysis, as well as opening up optimization opportunities with other algorithms like SVM for better accuracy.

Public health is an important indicator of the success of regional development and the quality of public services. Local governments play a strategic role in the provision of healthcare services through adaptive, effective, and community-oriented policies. This study aims to analyze local government policies in addressing public health issues through a literature review approach. The research method employed a literature study by examining scientific journals, laws and regulations, books, and policy documents related to regional health policies in Indonesia. The findings indicate that regional health policies are influenced by decentralization, fiscal capacity, the quality of governance, and inter-institutional synergy. Policy implementation continues to face several challenges, including limited budgets, low quality of healthcare human resources, unequal access to healthcare services, and weak intersectoral coordination. However, public service innovation and the strengthening of good governance have proven effective in improving healthcare service delivery. This study emphasizes that the success of regional health policies is determined by government commitment, community participation, integration between central and local policies, institutional strengthening, budget optimization, and service innovations based on community needs.