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This study examines the video content production process at Digital Hub Sinar Mas Land as a visual communication strategy to support branding for a digital innovation area. The background of this research stems from the increasing need for organizations to produce creative and informative content as social media becomes a primary channel for information distribution. The study aims to describe the stages of pre-production, production, and post-production, and to identify challenges that affect the effectiveness of video content creation. A qualitative descriptive method was used through active participation, observation, interviews, and literature review. The findings show that concept planning, scriptwriting, and storyboard development form the foundation of successful production. The production stage emphasizes camera techniques, talent coordination, and visual consistency aligned with communication goals. The post-production stage focuses on technical editing to strengthen the audiovisual message structure and prepare content for publication. Key challenges were found in storyboard development, talent performance during filming, and audio processing that required detailed adjustments. The results highlight the importance of thorough planning and coordinated teamwork to ensure that video content effectively supports branding strategies.

The National Student Arts Festival and Competition (FLS3N) is an event aimed at developing students’ talents and achievements in the arts, including solo vocal competitions. The assessment process in this category involves multiple criteria, which may lead to subjectivity in decision-making. This study aims to design and develop a web-based Decision Support System (DSS) for selecting non-academic students in the FLS3N solo vocal category using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The assessment criteria are based on the 2025 FLS3N Technical Guidelines, consisting of voice quality, vocal technique, expression, and performance. The TOPSIS method is applied to generate alternative rankings based on the highest preference value. The system is developed using a web-based software development approach and tested using participant data from both male and female categories. The results indicate that the system can provide objective and consistent ranking recommendations, thereby assisting schools in selecting the best students to represent them in the FLS3N competition.

Parental involvement is widely acknowledged as a key factor in student academic success, particularly in second language acquisition. However, its impact at the university level, especially among pre-service English teachers, remains underexplored. This study investigates the relationship between parental involvement and English language achievement among first-year English Education students at Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin (UNISKA). Employing a quantitative survey design, data were collected from 36 purposively selected participants using a structured Likert-scale questionnaire. The instrument measured both the forms and frequency of parental involvement and students’ self-reported performance in English-related subjects. Data were analyzed using descriptive statistics and Pearson Product-Moment Correlation via SPSS 26. Findings indicate that students generally perceive moderate to high levels of parental involvement, especially in providing academic resources, financial support, and attending university-related events. However, involvement in educational decision-making and career discussions was notably low. A significant positive correlation was found between parental involvement and students' academic performance, particularly in aspects such as discipline, assignment completion, and encouragement. Emotional impacts such as increased motivation or reduced anxiety showed more mixed responses. These findings highlight the enduring influence of parental support in higher education and suggest further exploration of its emotional dimensions.

A wall follower robot is a type of autonomous robot that is designed to move by following a wall at a certain distance. This research aims to design and build a Wall follower robot equipped with a Fuzzy-PID control system to improve navigation performance. The robot uses five HC-SR04 ultrasonic sensors to detect the distance to the wall and the surrounding obstacles. The data from the sensor is then processed by a Fuzzy-PID algorithm that combines the advantages of conventional PID control with fuzzy logic, resulting in a more adaptive response to environmental conditions. The test results showed that the robot with Fuzzy-PID control was able to maintain the stability of the distance to the wall more consistently compared to the pure PID control. In addition, the system exhibits better adaptability to complex environmental conditions, such as sharp turns, uneven wall surfaces, and the presence of resistance variations. The application of Fuzzy-PID control has been shown to improve the stability, response speed, and accuracy of the robot's navigation. These findings are expected to contribute to the development of robotic navigation systems for a wide range of practical applications, including automated cleaning robots, environmental exploration, and industrial systems that require reliable autonomous mobility.

Flower disease detection is a significant challenge in modern agriculture, particularly with factors such as changes in leaf color, petal shape and structure, and environmental conditions affecting the accuracy of conventional models. These factors make it difficult to achieve optimal results using traditional methods. Transfer learning is an effective solution to improve image detection performance, especially when data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The research process included data processing, increasing the data volume using augmentation techniques, model training, and evaluation of the results. Experimental results showed that the EfficientNet-B0 model produced the highest accuracy of 97.2%, significantly better than the CNN model built from scratch with an accuracy of 85.1%. This study demonstrates that transfer learning is highly effective in improving the accuracy of flower disease detection, making it a more reliable alternative to methods that do not utilize pre-trained models, especially for agricultural applications that require high levels of accuracy in disease detection.

This study aims to analyze the influence of macroeconomic variables in the form of the dollar exchange rate, inflation, and Gross Domestic Product (GDP) on the Composite Stock Price Index (JCI) in Indonesia for the period 2010–2024. The research method used is a quantitative approach with multiple linear regression analysis using time series data obtained from Bank Indonesia, the Central Statistics Agency (BPS), and the Indonesia Stock Exchange (IDX). The data analysis technique was carried out through classical assumption tests and hypothesis testing to determine the relationship between variables. The results of the study show that partially GDP has a significant effect on the JCI, while inflation and the dollar exchange rate tend not to have a significant effect. However, simultaneously these three variables have a significant influence on the JCI. These findings show that macroeconomic stability is very important in maintaining the performance of the capital market in Indonesia and can be a reference for investors in making investment decisions. In addition, the results of the study confirm that national economic growth is the main indicator that market participants pay attention to in assessing investment prospects. Therefore, the government needs to maintain economic stability through effective and sustainable fiscal and monetary policies.

The COVID-19 pandemic has increased public interest in investing, especially in the banking sector, which is known for its stability. However, many investors still lack an understanding of fundamental analysis. This study aims to examine the effect of Return on Asset (ROA), Return on Equity (ROE), and Net Profit Margin (NPM) on stock prices of banking companies listed on the Indonesia Stock Exchange during the 2011–2023 period. The research used a quantitative approach with purposive sampling and multiple linear regression analysis using SPSS. The results show that ROA has no significant effect on stock prices. In contrast, ROE has a significant negative effect, while NPM has a significant positive effect on stock prices. These findings indicate that investors tend to consider net profit margins more than asset efficiency, and that high ROE may be perceived as a signal of high leverage risk. This research is expected to provide insights for investors in assessing banking performance before making investment decisions.

The rapid growth of e-commerce platforms in Indonesia, particularly Tokopedia, has resulted in a large volume of consumer reviews containing valuable information regarding customer perceptions and satisfaction. However, manual analysis of such reviews is inefficient and prone to subjectivity, necessitating an automated approach based on machine learning. This study aims to classify the sentiment of sports product reviews on Tokopedia into positive, negative, and neutral categories by applying Logistic Regression, Support Vector Machine (SVM), and Random Forest using the Term Frequency–Inverse Document Frequency (TF-IDF) approach. The data were collected through web scraping of Indonesian-language sports product reviews and processed through several preprocessing stages, including data cleaning, case folding, tokenization, stopword removal, and stemming. Feature representation was performed using TF-IDF to transform textual data into numerical vectors, after which the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the application of TF-IDF significantly improves the performance of all models, with SVM consistently achieving the most optimal performance compared to Logistic Regression and Random Forest. These findings demonstrate that classical machine learning algorithms combined with TF-IDF remain highly effective for sentiment analysis of Indonesian-language text. The implications of this study are expected to assist sellers in understanding customer opinions, support consumers in making informed purchasing decisions, and serve as a foundation for the development of sentiment analysis and recommendation systems on e-commerce platforms.

This study investigates the impact of board characteristics on the financial performance of non-financial companies listed in the JII70 index in Indonesia. Motivated by the ongoing debate on the effectiveness of corporate governance mechanisms in enhancing firm outcomes, particularly within Sharia-compliant markets, this study focuses on three key board attributes: board size, board independence, and female representation on the board. Using a quantitative causal approach and panel data from 25 companies over the period 2020–2023, the study employs a fixed effect model to evaluate the relationship between board structure and financial performance measured by Return on Assets (ROA). The results show that board size has a positive and significant effect on firm performance, indicating that larger boards may enhance oversight capacity and provide broader resources beneficial to strategic decision-making. Conversely, board independence and board female representation do not exhibit significant effects on financial performance, suggesting that their roles may be more symbolic or constrained by institutional and contextual factors in the sampled companies. These findings highlight the importance of understanding corporate governance not merely in structural terms, but in relation to functional effectiveness and contextual maturity. The study offers implications for regulators, companies, and governance reform initiatives, particularly regarding strengthening substantive roles of independent and female commissioners in improving firm performance within Sharia-compliant markets.

This study explores how the organizational culture of Sanggar Kartika Budaya strengthens local artistic values through identity building, leadership, training strategies, and adaptive creativity. Rooted in a commitment to traditional arts, the sanggar positions local cultural expression not only as heritage but also as a living space for innovation. The research aims to uncover how these cultural elements shape member behavior, sustain artistic traditions, and support the regeneration of young artists. Using a qualitative approach with document analysis, this study examines official profiles, program descriptions, and relevant scholarly sources. The findings reveal that the sanggar’s cultural identity centered on the motto “Pegang Teguh Seni Tradisi Siap Berkreasi”serves as the backbone of its learning system and creative ecosystem. Leadership plays a central role in directing artistic vision while safeguarding cultural authenticity. Structured training, literacy activities, and collaborative performances effectively embed traditional values in new members. The sanggar also demonstrates an ability to evolve with modern trends through creative choreography, multimedia integration, and active participation in contemporary festivals, all while maintaining strong roots in local heritage. These findings highlight how a well-structured organizational culture can act as a powerful engine for cultural preservation and artistic resilience. The implications suggest that cultural institutions can remain relevant in a fast-changing era by blending heritage with innovation, ensuring that tradition continues to live meaningfully in the hands of future generations.

Physical fitness is a fundamental determinant of athletic performance and is strongly influenced by dietary intake and lifestyle behaviors. Adequate protein consumption is essential for muscle development and energy metabolism, whereas smoking has detrimental effects on lung function and aerobic capacity. This study aimed to explore the relationship between protein intake and smoking habits with physical fitness, measured by maximal oxygen uptake, among athletes of Persela Football Academy under-eighteen. A quantitative approach with a cross-sectional design was applied, involving adolescent male athletes. Protein intake was assessed using a semi-quantitative food frequency questionnaire, smoking habits were obtained through structured interviews, and maximal oxygen uptake was measured using the multistage fitness test. Findings revealed that the average daily protein intake of athletes was relatively high, while the mean maximal oxygen uptake score fell within the good category. Correlation analysis demonstrated a significant association between protein intake and aerobic fitness, as well as between smoking habits and aerobic fitness. The results indicate that lower protein intake and higher smoking frequency are linked to reduced physical fitness capacity. This study highlights the importance of nutritional interventions and healthy lifestyle promotion as integral components in the development of youth athletes to optimize performance and prevent decline in fitness.

Gestational Diabetes Mellitus (GDM) is a pregnancy-related metabolic disorder that poses health risks to both mother and fetus if not detected early, requiring accurate prediction methods for early screening and clinical decision-making. This study applies the Random Forest algorithm to detect GDM risk using clinical data from the Pima Indian Dataset. Data preprocessing included handling missing values, standardization, feature engineering, and a 70:30 train–test split. Two models were developed: a baseline and an optimized model using GridSearchCV hyperparameter tuning, validated with 5-fold cross-validation. Performance was assessed using a classification report, confusion matrix, and ROC–AUC. Results show that the optimized model outperforms the baseline, achieving 88% accuracy, an AUC of  93%, and average recall of 81%–85%. Compared to previous studies, this approach demonstrates improved predictive performance. The findings indicate that combining Random Forest with comprehensive preprocessing, feature engineering, and model optimization is effective and feasible for developing a medical decision support system for early GDM risk screening.

Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in Indonesia's economic development, particularly in developing products based on local wisdom. Toko Jayabaru is an MSME that markets Maumere's signature souvenirs made from traditional ikat woven fabric, facing various challenges and opportunities amid business and tourism dynamics. This study aims to identify internal and external factors affecting Toko Jayabaru's performance and formulate appropriate business development strategies through SWOT analysis. The research employs a qualitative descriptive approach with data collection through in-depth interviews with the owner and management of Toko Jayabaru. Data analysis uses the SWOT framework (Strengths, Weaknesses, Opportunities, Threats) to identify the business's strategic position and formulate alternative development strategies. Toko Jayabaru's main strengths lie in strong local cultural identity, authentic product quality, and adequate business experience. Weaknesses include limited digital marketing, restricted production capacity, and lack of derivative product variations. Opportunities encompass Flores tourism sector growth, increasing interest in local products, and potential strategic collaborations. Main threats are competition from similar products, influx of imitation products, and dependence on tourism sector fluctuations. Recommended development strategies include strengthening branding through storytelling, diversifying woven derivative products, optimizing digital marketing, strategic collaboration with tourism stakeholders, enhancing human resource capacity, and implementing customer loyalty programs. Implementation of these strategies is expected to increase MSME competitiveness while preserving Maumere's local culture.

End-to-end autonomous driving has emerged as a promising paradigm in which deep neural networks directly map raw visual inputs to continuous control actions. Despite its effectiveness, this approach suffers from limited transparency, posing significant challenges for deployment in safety-critical driving scenarios. This study addresses the lack of interpretability in vision-based end-to-end autonomous driving systems and aims to analyze model decision-making behavior under critical conditions such as sharp steering maneuvers and abrupt control transitions. To this end, an explainable end-to-end autonomous driving framework is proposed, combining a convolutional neural network trained via imitation learning with gradient-based visual attribution techniques, including Grad-CAM. The model predicts continuous steering, throttle, and braking commands directly from front-facing camera images, while explainability mechanisms are applied to reveal input regions influencing each control decision. Model performance is evaluated using both prediction accuracy and safety-oriented behavioral metrics. Experimental results show that the proposed explainable model achieves lower control prediction errors compared to a baseline end-to-end CNN, reducing steering mean squared error from 0.034 to 0.031, throttle error from 0.021 to 0.019, and brake error from 0.018 to 0.016. Moreover, safety-oriented analysis indicates improved driving stability, with steering variance reduced from 0.087 to 0.072 and abrupt control changes decreased from 14.6 to 10.3 events. Visual explanations consistently highlight road surfaces and lane-related structures during complex maneuvers, indicating reliance on semantically meaningful cues. In conclusion, the results demonstrate that integrating explainability into end-to-end autonomous driving not only preserves predictive performance but also correlates with smoother and more stable driving behavior. This framework contributes to the development of transparent and trustworthy autonomous driving systems suitable for safety-critical applications

This study aims to analyze the influence of Innovative Work Behaviour, Upskilling, and Work Ethic on the Health Workers Performance with Intention to Stay as an intervening variable at RSUD Sumberrejo. The background of this research stems from challenges in improving service quality, high workload, and the need to strengthen competency and retention among health workers. The study employs a quantitative approach using a survey method through the distribution of questionnaires, with data analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) through SmartPLS version 4.0. The population consists of 216 employees, and the sampling technique used is non-probability sampling, resulting in 140 respondents. The findings reveal that Innovative Work Behaviour, Work Ethic, and Intention to Stay have a significant positive effect on the performance of health workers. In addition, Innovative Work Behaviour and Work Ethic significantly influence Intention to Stay. However, Upskilling shows a positive but non-significant effect on both Intention to Stay and Performance, indicating that skill enhancement requires managerial support and motivation to contribute effectively to employee performance. Intention to Stay serves as a mediating variable in several relationships among the constructs. Strengthening innovative behaviour, work ethic, and competency development, accompanied by appropriate retention strategies, is essential for improving the performance of health workers in regional hospitals. Future studies are recommended to develop the research model by incorporating additional variables that may have stronger effects on Intention to Stay and Performance.

This study aims to examine the effect of employee competence, behavioral control, and trust on employee performance at the Bojonegoro District Health Office with Organizational Citizenship Behavior (OCB) as an intervening variable. The background of this study is related to the importance of employee performance in achieving public service organizational goals, particularly in the health sector. This study uses an explanatory quantitative method with a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The research sample consisted of 100 employees at the Bojonegoro District Health Office. The results of the analysis show that the influence of employee competence on employee performance is not significant, but employee competence has a significant influence on OCB. Behavioral control has a significant effect on employee performance, but its effect on OCB is not significant. Trust has a significant effect on OCB, but its direct effect on employee performance does not. OCB is proven to have a significant effect on employee performance. Therefore, OCB plays an important role as a mediator in improving employee performance. This study provides a theoretical contribution by clarifying the relationship between variables in the context of public service-based government organizations. In practical terms, the results of this study are expected to form the basis for recommendations to improve the quality of human resources and develop managerial strategies at the Bojonegoro District Health Office in order to support the effectiveness and efficiency of public services.

Sweet corn is a horticultural commodity that is widely consumed in Indonesia. This study evaluated the effectiveness of NPK 18-18-18 fertilizer on the vegetative growth, yield components, and relative agronomic effectiveness (RAE) of sweet corn (Zea mays saccharata). Field experiments were conducted using fertilizer dosages of 0,50, 0,75, 1,00, and 1,50 NPK, with a control treatment for comparison. The application of NPK 18-18-18 significantly increased plant height, stem diameter, leaf number, ear length, ear diameter, biomass weight, ear weight with husk, ear weight without husk, plot yield, and overall productivity compared with the control. Mean values across treatments ranged from 68,94–205,72 cm for plant height, 7,41–20,47 mm for stem diameter, 6,01–13,00 leaves per plant, 15,41–20,89 cm for ear length, and 36,05–49,65 mm for ear diameter. Biomass weight ranged from 0,12–0,34 kg, ear weight with husk from 0,13–0,34 kg, and ear weight without husk from 0,12–0,28 kg. Plot yield varied between 7,91–25,46 kg, corresponding to productivity levels of 5,02–16,16 t/ha. RAE analysis indicated that fertilizer application was effective at dosages of 0,75, 1,00, and 1,50 NPK, with the highest effectiveness observed at 1,50 NPK (118%). Notably, the 0,75 NPK dosage achieved an RAE value of 101%, demonstrating that lower fertilizer input can enhance yield while reducing production costs and mitigating fertilizer scarcity. These findings suggest that NPK 18-18-18 fertilizer, when applied at an optimal dosage, can be effectively utilized in sweet corn cultivation to improve growth and productivity while ensuring efficient nutrient management.

This study aims to determine the relationship between learning motivation and learning outcomes in SMAW welding engineering among eleventh-grade students at SMKN 2 Solok. This research employed a quantitative method with a correlational approach. The population consisted of all students from classes XI TPM 1 and XI TPM 2, totaling 51 students, using a total sampling technique. Learning motivation data were collected through a validated and reliable questionnaire, while learning outcome data were obtained from post-test scores in the SMAW welding subject. Data were analyzed using the Pearson Product Moment correlation test with the assistance of SPSS software. The results showed a correlation coefficient of r = 0.783 with a significance value of 0.000 < 0.01, indicating a strong, positive, and statistically significant relationship between learning motivation and students’ learning outcomes. These findings suggest that higher learning motivation leads to better learning outcomes in SMAW welding engineering. Therefore, learning motivation plays an important role in improving students’ academic performance. This study is expected to provide useful insights for teachers and schools in developing instructional strategies that enhance students’ motivation and learning outcomes.

This study aims to analyze the application of accounting according to PSAK 65 concerning consolidated financial statements to assess the company's financial performance at PT Warung Begok Indonesia. The object of this study is a company in the field of processed livestock manufacturing for the period 2023-2024. The data in this study are primary data sourced from the annual financial reports of the head office and branches. The total sample in this study was 3 company financial reports. Data collection techniques used interviews and documentation. The hypothesis in this study was tested using descriptive analysis techniques. Based on the data analysis carried out in this study, it shows that the financial statements before and after consolidation have an effect on the assessment of the company's financial performance. This study contributes to increasing knowledge and understanding of the head office and branch consolidated reports according to PSAK 65, and can assess the company's financial performance. Although the consolidated report has been carried out, it is hoped that the company will continue to apply controls and policies in its implementation, because this can affect the assessment of the company's financial performance so that it will be useful in decision making.

This article examines the limitation of transparency within the Indonesian House of Representatives (DPR) during the formulation of the Job Creation Act (Law No. 11 of 2020) and its implications for legislative performance and public participation. Transparency represents a fundamental requirement in a democratic legal system as it ensures accountability, public oversight, and the legitimacy of legal products. However, the legislative process of the Job Creation Act demonstrated significant procedural issues, including inconsistent draft versions, restricted access to essential documents, accelerated deliberation, and the marginalization of meaningful public participation. This study highlights how these limitations hinder the public’s constitutional rights, weaken legislative oversight, and create asymmetrical power relations that enable elite dominance in policymaking. The lack of transparency also led to procedural defects acknowledged by the Constitutional Court, reflecting a systemic decline in democratic legislative practices. Using a normative juridical method supported by legislative analysis and doctrinal studies, this paper argues that the absence of transparency not only reduces the quality of participation but also erodes the legitimacy and accountability of the DPR. The findings emphasize the urgent need for open access to legislative documents, inclusive public consultation, and strengthened accountability mechanisms to ensure democratic and lawful policy making.