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The development of digital technology has transformed trading patterns through the emergence of live shopping, a phenomenon that enables real-time interaction between sellers and consumers. Although it offers marketing efficiency, this model poses significant legal risks, including misleading information, promotional manipulation, and product non-conformity. This study aims to analyze legal certainty in consumer protection within live shopping transactions, examine the forms of business actors’ liability for consumer losses, and identify obstacles to regulatory implementation along with efforts to strengthen supervision.The research method employed is normative legal research using both a statute approach and a conceptual approach. The results indicate that legal certainty in consumer protection within this ecosystem is grounded in the integration of Law No. 8 of 1999 (Consumer Protection Law) as the lex generalis and Government Regulation No. 80 of 2019 (Electronic Commerce/PMSE) as the lex specialis, which recognizes the validity of real-time electronic contracts. The legal liability of business actors is strict liability in nature, in accordance with Article 19 of the Consumer Protection Law, and may also be construed as a tort (Article 1365 of the Civil Code) in cases involving distortion of visual information.However, the effectiveness of these regulations is hindered by the ephemeral nature of transactions and low levels of digital literacy. This study recommends the implementation of technology-based supervisory systems (suptech), strengthening the oversight function of platforms (PPMSE), and policy synchronization between the Ministry of Trade and the Ministry of Communication and Informatics in standardizing business actor verification to ensure the security of the digital commerce ecosystem.

Micro, Small, and Medium Enterprises (MSMEs) in Kemasantani Village have considerable potential in developing cassava-based products using MOCAF flour, particularly Antari chips. However, limited brand identity, unattractive packaging, and the underutilization of digital marketing have constrained product competitiveness and revenue growth. This community service program aimed to strengthen branding and optimize digital marketing strategies to support MSME revenue growth. A qualitative participatory approach was employed through surveys, interviews, observations, documentation, and direct assistance in brand identity development, packaging redesign, and social media management. The results indicate improvements in product image, packaging attractiveness, and market reach through digital platforms. The integrated application of branding and digital marketing successfully increased consumer engagement and led to an approximate 35% increase in MSME revenue within a three-month period. These findings suggest that strategic branding and digital marketing are effective approaches for enhancing competitiveness and ensuring the sustainability of locally based MSMEs in rural areas.

Background: Automatic segmentation of the thyroid gland in ultrasonography (USG) images using deep learning requires a user-friendly interface to support diagnostic and educational processes. Purpose: This study aims to develop and implement a Graphical User Interface (GUI) that integrates a deep learning U-Net model for interactive and efficient segmentation and visualization of thyroid USG images. Method: The development method employed the Rapid Application Development (RAD) approach using MATLAB programming language. The GUI is designed to load transverse and sagittal USG images, display automatic segmentation results, and calculate thyroid gland volume based on dimensions measured automatically from the segmentation output. Testing was conducted using USG image data from 15 volunteers, and GUI functionality was evaluated using black box testing. Result: The GUI successfully displayed USG images and segmentation results with a responsive 4-panel interface; zoom, pan, and image navigation features functioned well. Automatic segmentation occurred in real-time after image input, and volume measurement results appeared automatically. Black box testing evaluation showed all GUI features operated as expected. The average Dice Similarity Coefficient (DSC) of 0.91 indicates high performance of the U-Net model in thyroid segmentation, consistent with previous findings. Statistical testing confirmed no significant difference between volume measurements using the application and manual methods (p = 0.953). Conclusion: This GUI implementation facilitates users in performing deep learning-based segmentation and visualization of thyroid USG images, improving efficiency and accuracy in thyroid volume measurement. The GUI has potential applications in clinical practice and radiology education.

This study examines the factors influencing the phenomenon of quiet quitting in the context of contemporary organizations by reviewing empirical evidence published between 2021–2024. Quiet quitting refers to the psychological withdrawal of employees who remain formally employed but consciously limit their work involvement to the minimum required by the contract without any explicit intention to quit. Although this phenomenon has been widely discussed in academic literature, much of the research still examines quiet quitting in a fragmented way, lacking a comprehensive theoretical framework. This study adopts a systematic review approach following PRISMA 2020 guidelines, analyzing eight empirical articles indexed in Scopus and Google Scholar. The findings indicate that quiet quitting is influenced by individual factors such as burnout, emotional exhaustion, reduced job satisfaction, and low work engagement. Organizational factors include psychological contract violations, low perceived organizational support, unsupportive leadership styles, excessive workload, and an exclusive work culture. The findings can be understood within the Job Demands–Resources framework, where the imbalance between work demands and organizational resources triggers disengagement. This study contributes theoretically by integrating empirical findings into a more cohesive conceptual framework and provides practical implications for human resource management aimed at improving engagement and organizational sustainability.

Information technology (IT) infrastructure planning plays a strategic role in supporting organizational performance and achieving business objectives. However, many organizations experience misalignment between IT infrastructure planning and organizational needs, leading to inefficiencies, limited system integration, and suboptimal utilization of IT investments (Maulana, 2024; Njanka et al., 2025). This study aims to analyze the suitability of IT infrastructure planning with organizational needs using a quantitative approach. The research framework integrates the Strategic Alignment Model (SAM), Strategic Alignment Maturity Model (SAMM), and COBIT 2019 governance principles. Data were collected through a structured questionnaire distributed to IT managers and system users. A total of 85 valid responses were analyzed using descriptive statistics and gap analysis. The results indicate that IT infrastructure planning is moderately aligned with organizational needs, particularly in supporting operational activities. Nevertheless, significant gaps were identified in system integration, scalability, and long-term infrastructure planning. These findings highlight the importance of continuous evaluation and governance-based IT planning to enhance alignment and ensure sustainable organizational performance.

This study explores the effectiveness of a time management intervention in reducing academic procrastination among first- and second-semester undergraduate students at several universities in Malang. A quasi-experimental method was employed involving two groups with a total of 20 participants: 10 students in the experimental group who received a time management workshop and 10 students in the control group. The research instrument was the Academic Procrastination Scale developed by Onwuegbuzie. The intervention was conducted over six sessions focusing on techniques such as the Pomodoro Technique, Eisenhower Matrix, distraction management, relaxation, reward systems, to-do lists, leisure time management, and weekly scheduling. Data were collected through questionnaires and analyzed using an independent samples t-test and the nonparametric Mann–Whitney test. Participants were selected using a random sampling technique, and the academic procrastination scale was used as the primary measurement instrument. The results indicate that the time management intervention was effective in reducing academic procrastination, suggesting important implications for the development of more effective educational strategies. This research is expected to contribute to the design of more effective intervention programs to increase the academic productivity of students in higher education.

In the rapidly evolving digital economy, the ability to anticipate transaction surges is a strategic asset for marketplace platforms to maintain operational efficiency. This research aims to build an accurate daily transaction volume forecasting system thru the implementation of an Extract, Transform, and Load (ETL) pipeline and Autoregressive Integrated Moving Average (ARIMA) predictive modeling. The dataset used is sourced from dataset_olshop.csv, which includes transaction history for the entire year of 2025. The ETL stage focused on data cleaning and handling missing values, while time series analysis began with the Augmented Dickey-Fuller (ADF) stationarity test, which yielded a significant p-value of 0.000006. The parameter model was optimized using the auto_arima algorithm, which determined the ARIMA(2,0,0) configuration as the best model. The evaluation results of the model show fairly stable performance with a Root Mean Squared Error (RMSE) value of 2.002 and a Mean Absolute Error (MAE) of 1.704 on the test data. Research findings reveal a consistently higher purchasing pattern during the mid-month and end-of-month periods, with an average of 5.52 daily transactions, compared to the beginning of the month, which saw 5.48 transactions. The 30-day forecast results provide valuable insights for online store managers to proactively adjust inventory and logistics workforce allocation strategies. This research concludes that integrating data engineering techniques and statistical analysis can provide predictive solutions for the dynamics of the digital market.

Stock price prediction remains a complex challenge due to the dynamic and non-linear nature of financial markets, especially for banking stocks like PT Bank Negara Indonesia (Persero) Tbk (BBNI). This study aims to optimize BBNI stock price forecasting by integrating an automated Extract, Transform, Load (ETL) pipeline with the Long Short-Term Memory (LSTM) algorithm within a data engineering framework. Historical data from 2019 to 2025 were processed through a structured ETL sequence—including data cleaning, feature engineering, and MinMaxScaler normalization—to ensure high data quality. The dataset was partitioned into 80% for model training and 20% for testing to ensure rigorous evaluation. The results demonstrate that the systematic ETL approach significantly enhances model stability and predictive accuracy compared to conventional methods. The LSTM model effectively captured long-term temporal dependencies, providing reliable trend forecasts with an impressive test accuracy, achieving a Root Mean Squared Error (RMSE) of 0.0354. This research underscores that integrating robust data engineering practices with deep learning is essential for building resilient financial decision-support systems.

The utilization of digital technology in government administration is a critical measure to improve employee performance and the efficiency of public services. However, the effectiveness of implementing this technology is highly determined by the degree to which employees accept the digital systems being used. This study aims to analyze the impact of digital technology acceptance on employee performance at the Mojoroto District Office in Kediri City. The research employs a quantitative method with an explanatory design. Information was obtained through a questionnaire survey distributed to all employees at the Mojoroto District Office, totaling 30 respondents, using total sampling techniques. Indicators of digital technology adoption were evaluated based on perceptions of benefits, operational ease, social pressure, and infrastructural support, while employee performance was assessed according to the quality of work output and responsibilities. Data analysis involved descriptive statistics, validity and reliability tests, and ordinal linear regression. The study findings indicate that digital technology acceptance has a positive and significant influence on employee performance. The coefficient of determination figure reveals that digital technology adoption can explain a large part of the fluctuations in employee performance, with the remaining variation influenced by variables outside the research model. These results imply that the greater the level of digital technology acceptance, the better the resulting employee performance. This study concludes that strengthening digital capabilities, technical guidance, and institutional support are key strategies for enhancing employee performance and public service quality at the district level.

Digital transformation has become a strategic necessity for both public and private organizations in improving efficiency, service quality, and competitiveness in the digital era. The success of digital transformation is greatly influenced by the readiness and planning of integrated and sustainable IT infrastructure. This study aims to analyze the role of IT infrastructure planning in supporting the implementation of organizational digital transformation. The method used is a systematic literature review of scientific publications relevant to the topic of IT infrastructure, strategic information system planning, and digital transformation. The review results indicate that IT infrastructure serves as the main foundation of digital transformation, encompassing hardware, software, networks, data centers, information security, and human resources. Poorly planned IT infrastructure has the potential to cause various issues, such as limited technology capacity, digital divides, cybersecurity risks, and investment inefficiencies. Therefore, strategic IT infrastructure planning that aligns with the organization's vision is required, supported by adequate funding, human resource competency development, and adaptive digital leadership to ensure the successful and sustainable implementation of digital transformation.

The use of Information Systems and Information Technology (IS/IT) is a crucial factor in improving the efficiency and accuracy of retail sector operations. PT XYZ, a retail-level staple goods provider, has implemented IS/IT to support sales recording, inventory management, security, and operational reporting. This study aims to assess the effectiveness, efficiency, security, and compliance of the information system used. The audit was conducted through observation, interviews, document review, and system testing, referring to the COBIT framework and the ISO/IEC 27001 standard. The audit results indicated that the system supported core operational functions, but weaknesses remained, including insufficient system documentation, limited access controls, and suboptimal data security and backup mechanisms. Furthermore, reliance on manual procedures has the potential to lead to errors and slow down service delivery. Therefore, system improvements, strengthening security controls, developing standard operating procedures, and enhancing the competency of human resources and IT infrastructure are needed. This audit is expected to support improved service quality and secure, effective, and sustainable IT governance at PT XYZ.

The phenomenon of pseudoexpertise on social media, namely the tendency to feel knowledgeable without adequate scientific grounding due to exposure to unrestricted information, has become a serious challenge for Islamic religious education. This study employs a phenomenological method through in-depth interviews with two Master's students of Islamic Education (PAI) at UIN Syarif Hidayatullah Jakarta, who come from different social backgrounds teaching at Islamic boarding schools in rural Bogor and urban South Jakarta to obtain diverse perspectives on how social context influences the emergence of pseudoexpertise. The selection of master's-level informants is based on their academic qualifications and teaching experience, which are considered capable of providing representative insights. Secondary sources include scientific articles and relevant literature. The findings indicate that globalization and pseudoexpertise constitute obstacles that are difficult for religious teachers to control, as students acquire much of their religious knowledge from outside the school without verification. Therefore, contextual Islamic Education (PAI) learning and teachers who serve as knowledge validators through healthy communication are required, enabling students to filter information and develop more mature religious literacy.

The relatively high volatility of Robusta coffee prices creates uncertainty for farmers, business actors, and policymakers in making economic decisions. This study aims to analyze the price movement patterns of Robusta coffee, determine the most appropriate Autoregressive Integrated Moving Average (ARIMA) model, and conduct short- to medium-term price forecasting for Robusta coffee. The data used consist of monthly Robusta coffee price data from January 2023 to September 2025, sourced from the World Bank Commodity Price Data. The analytical method employed is ARIMA using EViews software, beginning with stationarity testing using the Augmented Dickey-Fuller (ADF) test, model identification through ACF and PACF, parameter estimation, and residual diagnostic testing. The results show that Robusta coffee price data are non-stationary at the level but become stationary at the first difference, indicating integration of order one I(1). Based on model identification and diagnostic testing, the ARIMA (0,1,0) model is found to be the most appropriate and satisfies the white noise assumption. Forecasting results indicate that Robusta coffee prices are projected to remain relatively stable with a moderate upward trend through December 2026. These findings are expected to serve as a reference for decision-making by farmers, business actors, and the government in responding to Robusta coffee price dynamics.

This study discusses the effectiveness of Indonesia's halal certification system in supporting international standards and increasing global consumer satisfaction. The study uses a descriptive qualitative method with a case study approach to analyze the implementation of the national halal certification system. The results show that the halal certification system has strengthened legal protection and consumer confidence in halal products through synergy between BPJPH, MUI, and LPH. However, there are still challenges in harmonizing halal standards between countries and increasing the recognition of Indonesian certification at the global level. The digitization of the certification process and integration with international standards are considered important to accelerate services and enhance the credibility of the national system. This study recommends strengthening collaboration among stakeholders, international halal diplomacy, and technology-based system innovation so that Indonesia can compete globally and play a role as a world halal certification center

The objective of this study is to investigate several facets of the role that educators play in the verbal abuse and bullying prevention of students at SMP Negeri 1 Tanjung Raja in Tanjung Raja District, Ogan Ilir Regency. The qualitative descriptive method is being used in this study. The researchers conduct observations and interviews as part of their data collection methods. There were seven informants in total for this investigation. This study is being conducted at Tanjung Raja sub-district, Jalan Sultan Mahmud Badaruddin II, in the Ogan Ilir Regency. The study's findings indicate that educators have a positive impact in educating students about bullying and how to protect themselves from various risks. In addition to this, the role of the teacher and the curriculum in the school is crucial in providing a safe and nurturing environment for the development of the students. It is anticipated that the research findings will be able to make a contribution to more effective education reform in SMP Negeri 1 Tanjung Raja and other schools.  

This study aims to provide a comprehensive overview of the implementation of green finance and its relationship with the financial performance and profitability of banking institutions in Indonesia. Although sustainable finance policies have been continuously strengthened by regulators and stakeholders, the contribution of green financing to overall banking performance is still developing gradually, making it important to conduct a more focused and systematic analysis of its effectiveness. This research specifically aims to describe the application of green financing practices, assess financial performance conditions, and analyze bank profitability during the 2020–2024 period. The study employs a descriptive quantitative approach using secondary data on green financing distribution, financial performance indicators such as the Capital Adequacy Ratio (CAR), Non-Performing Loans (NPL), and Loan to Deposit Ratio (LDR), as well as profitability measured through Return on Assets (ROA). The findings indicate that the implementation of green finance has the potential to enhance long-term financial stability and improve profitability in the banking sector. This study implies that expanding green financing can serve as a relevant and sustainable business strategy for the banking industry while simultaneously supporting national sustainability and environmental development objectives.

Sarcasm is a literary device and one of the most expressive forms of figurative language, often used to convey humor, criticism, or emotional tension in both daily conversation and literature. This study explores the use of sarcasm in William Shakespeare‟s Much Ado About Nothing by applying Elizabeth Camp‟s (2011) typology, which classifies sarcasm into four types: propositional, lexical, illocutionary, and like-prefixed sarcasm. Using a qualitative descriptive method, the researchers collected all sarcastic utterances from the play, classified them according to Camp‟s framework, and analyzed their pragmatic functions in the dramatic context. The findings reveal a total of 50 sarcastic utterances, with propositional sarcasm being the most frequent (42%), followed by illocutionary sarcasm (28%), lexical sarcasm (24%), and like-prefixed sarcasm (6%). These results indicate that sarcasm serves as both a comedic and dramatic device, shaping character interactions, driving conflicts, and reinforcing Elizabethan cultural norms. Beatrice and Benedick‟s witty verbal duels exemplify how sarcasm fosters humor and intimacy, while Claudio‟s sarcasm highlights themes of honor and social tension. Overall, the study demonstrates that sarcasm in Shakespeare‟s play is not merely humorous banter but a sophisticated rhetorical strategy that enhances characterization, thematic depth, and audience engagement.

This study examines the comparative volatility of gold and Bitcoin over the period January 2020 to August 2025, using monthly data and employing descriptive statistics, the Augmented Dickey-Fuller (ADF) test, GARCH (1,1), and the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model estimated with EViews 13. The results show that Bitcoin is characterized by extreme and persistent volatility, reflecting its speculative nature, whereas gold remains stable and functions as a conventional safe-haven asset. Correlation analysis indicates that the relationship between gold and Bitcoin is generally weak but dynamic, as the strength and direction of their co-movements change across different market conditions. These findings highlight the potential role of gold as a hedge and Bitcoin as a speculative diversifier, offering insights for portfolio diversification and risk management. These results also suggest that investors should carefully consider their risk tolerance and investment horizon when allocating assets between traditional and digital commodities.

Sarcasm is a literary device and one of the most expressive forms of figurative language, often used to convey humor, criticism, or emotional tension in both daily conversation and literature. This study explores the use of sarcasm in William Shakespeare‟s Much Ado About Nothing by applying Elizabeth Camp‟s (2011) typology, which classifies sarcasm into four types: propositional, lexical, illocutionary, and like-prefixed sarcasm. Using a qualitative descriptive method, the researchers collected all sarcastic utterances from the play, classified them according to Camp‟s framework, and analyzed their pragmatic functions in the dramatic context. The findings reveal a total of 50 sarcastic utterances, with propositional sarcasm being the most frequent (42%), followed by illocutionary sarcasm (28%), lexical sarcasm (24%), and like-prefixed sarcasm (6%). These results indicate that sarcasm serves as both a comedic and dramatic device, shaping character interactions, driving conflicts, and reinforcing Elizabethan cultural norms. Beatrice and Benedick‟s witty verbal duels exemplify how sarcasm fosters humor and intimacy, while Claudio‟s sarcasm highlights themes of honor and social tension. Overall, the study demonstrates that sarcasm in Shakespeare‟s play is not merely humorous banter but a sophisticated rhetorical strategy that enhances characterization, thematic depth, and audience engagement.

Rubber prices experience significant and prolonged fluctuations, which impact farmer incomes and management decisions. Understanding historical patterns and price predictions is considered crucial for production planning, marketing, and farmer protection policies. This study aims to identify the characteristics of rubber price time series in Lahat Regency and develop a reliable forecasting model to support short- to medium-term decision-making. This study uses secondary data on monthly average producer prices for the period January 2019–December 2023. The analysis includes the Augmented Dickey–Fuller stationarity test to determine the need for transformation, differencing, and/or logarithmic transformation when necessary, identification of autocorrelation patterns using ACF/PACF, model estimation on the processed data, and evaluation of residual diagnostics (Ljung–Box, normality test) and forecasting accuracy metrics (RMSE, MAE, MAPE, Theil). The level data shows non-stationarity and becomes stationary after the first differencing; The model on log-transformed data had significant parameters and higher explanatory power than the model on de-differenced data, with RMSE and MAPE values ​​within a reasonable range. Forecast confidence intervals widened at longer time horizons, indicating increased projection uncertainty. Conclusion: Validated forecasts can inform farmers and policymakers to manage price risk and design market interventions.