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The emergence of Internet of Things (IoT) technology has opened up considerable possibilities for automation systems aimed at enhancing energy efficiency in both home and office settings. This study focuses on the creation and deployment of an IoT-driven Smart Home system utilizing ESP32 for the automatic and manual supervision and regulation of electrical appliances within the Employee Dormitory of the Institute for Lecturer Performance Development (LPKD). The established system employs a Passive Infrared (PIR) sensor for detecting human presence and a Light Dependent Resistor (LDR) sensor to assess the intensity of light in the room. The data from these sensors is analyzed by the ESP32 microcontroller, which controls lights and fans automatically through relay modules based on the surrounding conditions. Furthermore, the system is linked to the Blynk application and the Telegram Bot API to enable remote operation and immediate notifications. The methodological approach taken in this research is Research and Development (R&D) employing a prototyping method. The assessment outcomes show that the system functions reliably, with success rates for sensor detection and appliance management exceeding 95%. The introduction of this system aims to boost electrical energy efficiency, improve user experience, and facilitate the adoption of IoT-centered smart facility management within the LPKD framework.

This study aims to analyze the influence of work motivation, transformational leadership style, and career development on employee performance through job satisfaction at the Dinas PUPR Kabupaten Kutai Timur. This research uses quantitative explanatory method with SEM-PLS (Structural Equation Modeling - Partial Least Squares) analysis. The population consists of 120 employees with a sample of 93 respondents determined using the Slovin formula with 5% margin of error and simple random sampling technique. Data were collected using a Likert scale questionnaire (1-5). The results show that career development has a significant positive effect on job satisfaction (original sample 0.574, p-value 0.000), and transformational leadership style has a significant positive effect on employee performance (original sample 0.540, p-value 0.000). However, work motivation and transformational leadership style do not significantly affect job satisfaction, and work motivation and career development do not directly affect employee performance. Furthermore, job satisfaction fails to mediate the relationship between work motivation, transformational leadership style, and career development on employee performance. The R-Square values indicate that the predictor variables explain 40.4% of job satisfaction variance and 40.1% of employee performance variance.

This study examines the effects of participative leadership, organizational culture, and work motivation on employee performance through job satisfaction at the Department of Industry and Trade of East Kutai Regency. A quantitative approach with an explanatory research design was employed. The population comprised 108 civil servants and regional contract employees, all of whom were included as research respondents. Data were collected through a structured questionnaire and analyzed using validity and reliability tests, multiple linear regression, t-test, F-test, and path analysis. The findings demonstrate that participative leadership, organizational culture, and work motivation have positive and significant effects on job satisfaction. Partially, these three variables also exert positive and significant effects on employee performance. In addition, job satisfaction was found to have a positive and significant effect on employee performance. The results of the path analysis further indicate that job satisfaction mediates the relationship between participative leadership, organizational culture, work motivation, and employee performance. These findings suggest that efforts to improve employee performance in public sector organizations require the strengthening of participative leadership practices, the development of a supportive organizational culture, the enhancement of work motivation, and the sustainable management of job satisfaction. Accordingly, job satisfaction constitutes an important mechanism through which organizational factors contribute to improved employee performance.

The rapid development of the digital era has significantly impacted adolescents by increasing academic stress due to academic demands, performance pressure, and technological distractions. This condition is often exacerbated by low levels of emotion regulation, which negatively affect students’ learning focus and psychological well-being. Therefore, effective interventions are needed to help students manage their emotions adaptively. This community service program aims to enhance emotional balance and reduce academic stress through a mindfulness-based intervention at MA Plus Nururrohmah. The program employed a participatory approach with stages of preparation, implementation, and evaluation. It was conducted in two main sessions: a mindfulness session delivered by Erlin Kurnia Sri Rejeki, M.Pd.I, and an interactive stress-release session facilitated by Estria Solihatun Nurjannah, M.Pd. Data were collected qualitatively through observation, documentation, and analysis of students’ responses and participation. The results show improved emotional awareness, better recognition of stress conditions, and increased student engagement. Additionally, students demonstrated more positive emotional states after the program. Thus, mindfulness implementation contributes positively to enhancing emotion regulation and reducing academic stress and has potential as a sustainable school-based mental health intervention model.

This study proposes a computer vision-based system for automatically verifying the use of Personal Protective Equipment (PPE) in industrial environments. The system integrates the YOLOv8 object detection model, OpenCV for image processing, and a State Machine mechanism to manage the verification workflow. The verification process begins with employee identification through ID card scanning, followed by real-time detection of the head, safety helmet, and face mask using a camera, before generating a final PASS or FAIL decision. System evaluation was conducted using 250 testing scenarios to assess both model and system performance. The results show that the YOLOv8 model achieved an mAP@50 of 93.9%, while the overall verification system obtained 98.4% accuracy, 98.4% precision, 100% recall, and a 99.2% F1-score. The implementation of the State Machine contributed to a more stable and consistent verification process by ensuring that each inspection stage was executed in the correct sequence. These findings demonstrate that the proposed system can effectively support automated PPE compliance monitoring and has the potential to enhance occupational safety management in industrial workplaces. 

VIP Driving Course is a driving course institution that focuses on safe and effective learning. This course has experienced problems in the last five months, there has been a decrease in the number of students, recorded from January to May, respectively, there were 47, 33, 29, 25, and 21 students. This indicates a customer satisfaction issue related to service quality. The purpose of this study was to identify service quality, analyze the gap value and identify service dimensions or attributes that are prioritized for improvement. The number of respondents used was 112 respondents. This study used the SERVQUAL method to measure the gap between student expectations and perceptions, and importance performance analysis to determine improvement priorities. The results showed six attributes with negative gap values ​​including comfort of facilities, clarity of material, availability of cars, responsiveness of service, clarity of information, and attention to safety, and seven attributes with positive gap values ​​including vehicle cleanliness, neatness of instructors, time discipline, fast administration, friendly service, openness to input, and orientation to student needs. SERVQUAL analysis shows 4 dimensions with negative values ​​including tangibles of -0.21, reliability of -0.32, responsiveness of -0.29, and assurance of -0.24, while the empathy dimension obtained a positive gap value of 0.06. This indicates that most services have not met student expectations. Based on the IPA analysis, there are 4 attributes in quadrant I that are the main priority for improvement, with suggestions including rearranging the waiting room, lighting, air circulation, and supporting facilities. Increasing the number of training cars, optimizing the delivery of schedule information, procedures through digital media, and increasing attention to driving safety. Improvement efforts are made to increase customer satisfaction.

This study examines the financial performance of PT Unilever Indonesia Tbk during the 2023–2025 period by evaluating key financial indicators, namely the Current Ratio (CR), Debt to Equity Ratio (DER), Return on Assets (ROA), and Return on Equity (ROE). The study aims to assess the company's financial condition and analyze the impact of its business transformation strategy on financial performance. A descriptive quantitative approach was employed using secondary data obtained from the company's published annual financial reports. Data analysis focused on comparing financial ratio trends over the three-year period to evaluate liquidity, solvency, and profitability performance. The findings indicate that the company's financial performance experienced fluctuations during the business transformation process. Liquidity and solvency gradually improved toward the end of the observation period, reflecting stronger short-term financial capability and a healthier capital structure. Profitability also demonstrated increased efficiency in utilizing company assets, although changes in equity returns indicated adjustments in capital management during the transformation process. Overall, the implementation of the company's transformation strategy contributed positively to strengthening financial performance and improving resilience in responding to changing business conditions and market competition. This study provides useful insights for management, investors, and other stakeholders in evaluating the effectiveness of corporate transformation strategies through financial ratio analysis and highlights the importance of maintaining financial stability to support sustainable business growth.

This community-based project aimed to analyze the cash management system of the micro, small, and medium enterprise (MSME) Bakpia Juwara Satoe and develop recommendations to improve the company's financial performance. Cash management is a crucial but often overlooked aspect of MSME financial management, which can lead to cash flow problems, inaccurate record keeping, and poor management decisions. The methodology used included qualitative data collection through direct observation, in-depth interviews, and financial statement analysis. The project findings revealed that Bakpia Juwara Satoe lacked a functioning cash management system. There was no separation between cash receipts and disbursements, no standardized system for handling cash, and transaction recording remained manual and inconsistent. As part of the project, standard operating procedures (SOP) for cash management were developed, financial accounting training was conducted, and support was provided in creating simple cash flow statements. Following the implementation of these measures, record accuracy increased by 78%, and cash losses were reduced. It was determined that the implementation of efficient cash management contributed significantly to the improvement of Bakpia Juwara Satoe's financial performance.

This study aimed to improve students' speaking skills in news text learning through the implementation of the Two Stay Two Stray (TSTS) learning model. The research employed Classroom Action Research (CAR) conducted in two cycles involving Grade VIII students of SMP Negeri Satu Atap Kujiratu. Data were collected through observation, performance assessment, and learning outcome tests. The findings indicated that the application of the TSTS model contributed positively to students’ speaking skills and learning outcomes. In Cycle I, 16 students achieved mastery learning, while 13 students did not, with a class average score of 43.15. In Cycle II, learning outcomes improved, with 18 students achieving mastery learning and 11 students not yet meeting the minimum criteria, resulting in an average score of 73.11. The improvement was supported by increased student participation, confidence in expressing ideas, and collaborative interaction during group discussions. The study concludes that the Two Stay Two Stray learning model is effective in enhancing students’ speaking skills in news text learning and can serve as an alternative cooperative learning strategy in Indonesian language instruction.

This study aims to develop and evaluate a VisualCommunication Design (VCD) strategy for onlinepromotion through Facebook at CV. Cahaya Grafikain Demak. The research applied a design-basedapproach, combined with audience perception testingusing a Likert-scale questionnaire distributed to 30respondents aged 18–44 years. The developmentproduced five main outputs: Facebook photo posts,profile and cover design, group posts, story-formatcontent, and adaptation for printed banner media. Theresults show significant improvement in visualattractiveness, message clarity, brand recognition,and engagement performance. The findings indicatethat systematic visual optimization enhancespromotional effectiveness in both digital and printmedia.

This study aims to compare the performance of three boosting algorithms, namely XGBoost, LightGBM, and CatBoost, to predict the success of MSMEs. The data used consists of 250 entries with 13 attributes that include business actor characteristics, initial capital, industry experience, financial record-keeping, internet utilization, business planning, partnerships, and the target variable success. The pre-processing stage includes checking for missing values, standardizing numerical attributes, and splitting the data into 80% training data and 20% test data. The evaluation results show that XGBoost provides the best performance with an accuracy of 0.92, precision of 0.8333, recall of 0.8333, F1-score of 0.8333, and ROC-AUC of 0.9715. LightGBM has an accuracy of 0.88, while CatBoost achieves an accuracy of 0.90. The research results show that XGBoost has the best ability to classify successful and unsuccessful MSMEs. The feature importance results also show that the success of MSMEs is influenced by a combination of several key factors. This research emphasizes that boosting algorithms are effectively used as predictive models to support the analysis of MSME success.

Exports as a component of international trade contribute significantly to the development and growth of a country’s economy. In Indonesia, export activities are considered one of the key drivers in increasing national economic performance. This research seeks to evaluate the role of Indonesia’s exports to the United States within the Harmonized System (HS) Code 71 commodity classification, encompassing pearls, gemstones, precious metals, and jewelry products, in influencing Indonesia’s economic growth as reflected by Gross Domestic Product (GDP) at constant prices.The analysis focuses on three specific commodity groups: Pearls and Precious Stones (HS 71.01–71.05), Precious Metals and Metals Clad with Precious Metals (HS 71.06–71.12), and Jewelry and Related Articles (HS 71.13–71.18). A descriptive quantitative research design was employed, utilizing secondary time-series data collected for the 2015–2024 period. Data sources included the Indonesian Central Bureau of Statistics (BPS) and Trade Map. To assess the relationship between variables, this study adopted multiple linear regression analysis accompanied by classical assumption testing and hypothesis testing using both t-test and F-test procedures. The empirical findings demonstrate that export performance within the HS Code 71 category underwent considerable fluctuations throughout the observation period, reaching its peak in 2024.Furthermore, the statistical results indicate that each commodity sub-category contributes differently and exerts varying effects on Indonesia’s real GDP, both individually and collectively. The outcomes of this research are expected to provide useful references for government institutions in developing trade cooperation strategies and policy decisions, while also supporting business actors in strengthening the international competitiveness of Indonesian products.

This study aims to analyze the influence of fundamental and technical factors on stock price movements in the Indonesian capital market. The method used is a literature review with a qualitative descriptive approach, utilizing secondary data obtained from various scientific literature sources as well as stock price data from the Investing platform. The results indicate that there are significant variations in stock prices among companies, reflecting differences in fundamental performance such as profitability, capital structure, and company prospects. In addition, from a technical perspective, stock price movements exhibit dynamic fluctuations, including both increases and decreases, influenced by market sentiment and investor responses to available information. The varying levels of stock volatility also indicate differences in investment risk. Trading volume plays an important role as a key indicator in technical analysis, where high volume reflects strong liquidity and investor interest, while low volume indicates limited market activity. Overall, the findings suggest that fundamental and technical factors have a complementary influence in determining stock price movements. Therefore, investors are advised to integrate both approaches in making investment decisions in order to achieve optimal returns while minimizing risk. This study is expected to contribute to the development of capital market research and serve as a reference for investors and academics.

Tomato (Solanum lycopersicum) is a high-value horticultural commodity in Indonesia, yet its cultivation is frequently disrupted by leaf diseases that are difficult to distinguish visually. Diseases such as Bacterial Spot, Early Blight, and Tomato Yellow Leaf Curl Virus often present overlapping visual symptoms, making early and accurate diagnosis a significant challenge for farmers. The manual identification methods currently in use are inefficient and error-prone, ultimately leading to reduced crop yield  and quality. The general objective of this study is to develop software capable of automatically classifying tomato  leaf diseases. Specifically, this research aims to implement the MobileNetV3 Small architecture based on Convolutional Neural  Network (CNN) with ImageNet pre-trained weights to classify 10 types of tomato leaf diseases. The research methodology encompasses dataset collection from Kaggle comprising 10,000 images (1,000 per class), image pre-processing through resizing to 224x224 pixels, and normalization, as well as hyperparameter optimization (optimizer, learning rate, epoch, batch size) via scheduler. Model performance is evaluated using a confusion matrix encompassing accuracy, precision, recall, and F1-score.

The purpose of this study is to analyze the influence of Corporate Social Responsibility (CSR), Financial Distress, and Altman Z-Score on Dividend Decisions in automotive sector companies listed on the Indonesia Stock Exchange (IDX) for the 2020–2025 period. This study is motivated by fluctuations in the Dividend Payout Ratio (DPR) in the automotive sector, which indicates changes in company dividend policy due to economic conditions, financial performance, and non-financial factors that influence management decision-making. The research method used is a quantitative approach with a causal associative research type to examine the relationship between the independent and dependent variables. The study population consists of automotive sector companies listed on the IDX, while the sample was determined using a purposive sampling technique based on certain criteria. Research data were obtained from annual reports and company financial statements for the 2020–2025 period. Data analysis was carried out using the Dividend Payout Ratio (DPR) as a proxy for dividend decisions and statistical testing to determine the effect of CSR, Financial Distress, and Altman Z-Score on company dividend, the data were processed using SPSS.

Changes in work patterns driven by digital technology development and post-pandemic dynamics have encouraged organizations to implement Work From Home (WFH) as an alternative working system. This policy has attracted considerable attention because it affects employee productivity and well-being, which are essential for organizational performance. However, previous studies have reported inconsistent findings regarding the effectiveness of WFH. Therefore, this study aims to analyze the impact of WFH on employee productivity and well-being. A qualitative approach was employee using the Systematic Literature Review (SLR) method by reviewing 19 scientific articles related to productivity, job satisfaction, work-life balance, and mental health. The findings show that 89,5% of articles reported positive effects of WFH through greater flexibility, work comfort, time efficiency, and improved work-life balance, wich contribute to higher productivity and well-being. Meanwhile, 10,5% identified negative effects due to inadequate work facilities, internet disruptions, burnout, and coordination difficulties. These findings indicate that WFH effectiveness is influenced by technological support, job characteristics, and organizational culture. The study implies that organizations should adopt adaptive work policies, strengthen digital infrastructure, and provide mental health support to optimize employee productivity and well-being.

This study aims to analyze the effect of artificial intelligence (AI)-based demand forecasting and big data analytics on inventory optimization through prediction accuracy among e-commerce actors or marketplace sellers in Curug. This research employed an explanatory quantitative approach involving 100 respondents selected through purposive sampling based on predefined criteria relevant to the research objectives. Data were collected using a structured Likert-scale questionnaire and analyzed using Structural Equation Modeling Partial Least Squares (SEM-PLS) by evaluating the measurement model, structural model, and mediation effects. The findings reveal that AI-based demand forecasting and big data analytics have a positive and significant effect on prediction accuracy. Furthermore, prediction accuracy has a positive and significant effect on inventory optimization and significantly mediates the relationship between AI-based demand forecasting, big data analytics, and inventory optimization. These findings indicate that the integration of AI and big data analytics contributes to more accurate demand prediction, leading to improved inventory management performance. The implication of this study suggests that e-commerce actors should improve data quality, analytical capability, and the adoption of predictive technologies to support more accurate, efficient, and responsive inventory decisions, thereby enhancing operational performance and competitiveness in responding to dynamic market demand.      

This study aims to analyze the effect of Additional Employee Income (TPP) and Work Motivationon Employee Performance, both directly and indirectly through Discipline, as well as to analyze thesimultaneous effect of Additional Employee Income (TPP) and Work Motivation on Work Disciplineand Employee Performance at Bappeda of East Kutai Regency.The research method used was aquantitative approach, with data collected through questionnaires distributed to 103 civil servants atBappeda of East Kutai Regency. The data analysis technique used Structural Equation Modeling(SEM) based on Partial Least Square (PLS).The results show that Additional Employee Income(TPP) and Work Motivation individually do not have a significant effect on Employee Performanceat Bappeda of East Kutai Regency. In contrast, Additional Employee Income (TPP) and WorkMotivation have a significant effect on Work Discipline, and Work Discipline significantly affectsEmployee Performance. Furthermore, Work Discipline is proven to act as a mediating variable in therelationship between Additional Employee Income (TPP) and Work Motivation on EmployeePerformance. Simultaneously, Additional Employee Income (TPP) and Work Motivation have asignificant effect on Work Discipline and Employee Performance at Bappeda of East Kutai Regency.

This study examines the implementation of Environmental, Social, and Governance (ESG) principles as a sustainability instrument for consumer cooperatives from the perspective of Islamic economics. Consumer cooperatives, as people-based economic institutions, face significant challenges in maintaining sustainability amid rapid economic transformation and increasing demands for corporate accountability. ESG, as a global framework for measuring sustainability performance, is considered relevant to the values of Islamic economics which emphasize justice, transparency, social benefit, and environmental responsibility. This research uses a qualitative approach through literature study and document analysis to formulate a conceptual model of ESG implementation in consumer cooperatives based on Islamic economic principles. The findings show that ESG dimensions are in line with maqashid al-syariah, namely the protection of religion, life, intellect, lineage, and property. Environmental aspects correlate with the stewardship principle of khalifah fil ardh, while social aspects align with the concept of takaful ijtima’i, and governance reflects the values of amanah and shura. This study offers a conceptual framework for integrating ESG within the cooperative institutional governance system as a strategic step toward sustainable and equitable economic development.

The rapid development of digital technology has transformed the way organizations conduct business activities and manage human resources. These changes require organizations to have employees who are not only competent but also possess a high level of employee engagement to adapt effectively to the dynamic work environment. This study aims to analyze the role of transformational leadership in enhancing employee engagement among employees of Ranch Market in the digital era. Transformational leadership is regarded as a leadership style that inspires, motivates, and encourages employees to develop their potential through a clear vision, effective communication, and individualized consideration. This research employed a quantitative approach by collecting data through questionnaires distributed to Ranch Market employees. Data analysis was conducted to examine the relationship between transformational leadership dimensions and employee engagement, which includes vigor, dedication, and employee involvement in their work. The findings indicate that transformational leadership plays a positive and significant role in improving employee engagement. Leaders who are able to inspire, build trust, encourage innovation, and support employee development have been proven to strengthen employees’ attachment to the organization. Furthermore, transformational leadership fosters a supportive and adaptive work environment, enabling employees to respond effectively to technological changes and evolving work systems. The results suggest that the implementation of transformational leadership can serve as an effective strategy for organizations in addressing the challenges of the digital era while promoting productivity, employee commitment, and sustainable organizational performance. Therefore, transformational leadership remains a crucial factor in maintaining a highly engaged workforce and achieving long-term competitive advantage.