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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

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 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.

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 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 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.

Local governments in Indonesia rely significantly on local tax revenues to strengthen fiscal independence and sustain regional development. Among these revenue sources, the Motor Vehicle Tax (PKB) and the Motor Vehicle Transfer Fee (BBNKB) play a vital role in shaping regional fiscal capacity. This study explores how both taxes contribute to the Regional Own-Source Revenue (PAD) of Central Java Province during the 2020–2024 period. Drawing on quantitative analysis and secondary data from the Central Java Regional Revenue Agency (Bapenda), the research assesses the effectiveness and contribution of PKB and BBNKB in supporting local fiscal performance. The results show that PKB consistently exerts a positive, meaningful influence on PAD, underscoring its central role in sustaining the province’s fiscal strength. In contrast, BBNKB demonstrates a weaker, less stable impact, primarily driven by changes in vehicle ownership trends and administrative challenges. When considered together, both tax instruments contribute to enhancing local fiscal capacity, although the reliance on PKB remains predominant. These findings underscore the importance of modernizing regional tax administration, particularly through digital innovation, transparent reporting systems, and community-based compliance strategies. Strengthening these mechanisms is essential to building a more resilient and autonomous fiscal framework, especially in the context of post-pandemic economic recovery and long-term regional development planning

The development of social media platforms like TikTok has created new spaces for digital economic activities, including the practive of thrifting, which has now become a trend among the public. However, government policies that block these activities have sparked various public reactions. This study aims to analyze public sentiment regarding the issue of thrifting bans on the TikTok platform using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. This method was chosen because it can understand text context from both directions, allowing it to capture deeper semantic meaning. The dataset consist of 4,000 TikTok user comments collected through a crawling process. The research stages include data preprocessing, sentiment labeling, splitting training and test data, training the Bi-LSTM model, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The research results show that the Bi-LSTM model achieved an accuracy of 86.15%, with stable classification performance and minimal error rate. These findings indicate that Bi-LSTM is effective for sentiment analysis of public opinions on Indonesian language social media, particularly on context specific policy issues. Further development can be carried out by adding pre-trained embeddings or attention mechanisms to improve the model’s performance.

Manual quality assessment of Platelet Concentrate (TC) is highly subjective and inconsistent, necessitating an objective, automated classification system. This study aims to develop a computationally efficient, low-cost model for TC quality classification using Histogram Features extracted from grayscale images combined with the K-Nearest Neighbor (KNN) algorithm. The methodology employed critical preprocessing steps, including StandardScaler for normalization and SMOTE for balancing the training data, followed by optimization across K=1 to K=30. The optimal model achieved a maximum accuracy of 69.23% at K=6, with an F1-Score of 71.43%, confirming robust performance on the imbalanced testing set. The results validate the effectiveness of the Histogram-KNN approach as a consistent and reliable decision support system for rapid TC quality screening in resource-limited settings.

The development of renewable energy, particularly Solar Power Plants (PV), requires a reliable, real-time, and easily accessible electrical energy monitoring system to ensure optimal system performance. This study aims to design and implement an Internet of Things (IoT)-based electrical energy monitoring system for PV using the NodeMCU ESP32 microcontroller, the PZEM-004T sensor for measuring electrical parameters, and the Node-RED platform as the data visualization interface. The developed system is designed to monitor voltage, current, power, energy, frequency, and power loss in real time, and then display the data in the form of numerical values, graphs, and indicators on a dashboard accessible through a local network. The research method includes hardware design, software development (sensor reading, data processing, and communication), integration with Node-RED, and system testing on a small-scale PV installation. The test results show that the system is capable of monitoring electrical parameters in a stable and responsive manner. Variations in sunlight intensity were found to affect the current and power produced by the solar panels, whereas the inverter output voltage tended to remain within normal operating ranges. The Node-RED dashboard display was considered informative and helpful for users in monitoring and analyzing PV performance. Based on these results, it can be concluded that the IoT-based electrical energy monitoring system designed in this study functions well and is feasible for application in residential or educational-scale PV installations. The system still has the potential for further development through cloud service integration, the addition of environmental sensors, and enhancements to data analysis features and user interface design.

The development of value-added corn-based beverages representation an emerging opportunity within the agribusiness sector, particularly for student-led entrepreneurial initiatives seeking to utilize local agricultural resources. This study examines consumer perceptions of Zeagrain corn milk, a sweet-corn-derived drink positioned as a healthy, practical, and affordable alternative within the ready-to-drink market segment. The research aims to identify key attributes influencing consumer interest, evaluate perceived product performance, and assess the potential competitiveness of Zeagrain as a new agribusiness product. Data were collected using an online quantitative survey targeting respondents aged 17–35 years who represent potential urban consumers. The questionnaire measured demographic characteristics, consumption patterns of similar beverages, perceived importance and performance of product attributes, and purchase intention. The findings are expected to provide insights into consumer expectations regarding taste, packaging, nutritional value, and price. The results further serve as empirical input for improving product development strategies and enhancing the market readiness of Zeagrain. Ultimately, this study contributes to strengthening the commercialization prospects of corn-based beverages and supports innovation efforts among young agripreneurs seeking to optimize local commodities.  

Laskar Buah is a modern retail chain specializing in the sale of fresh fruit. At present, the company operates one hundred branches across ten regencies. One of its outlets, Laskar Buah Ngumpakdalem, ranks among the top three branches in terms of transaction volume; however, it has received a considerable number of customer complaints regarding the quality of service provided. This situation has prompted management to conduct a thorough evaluation of the store’s service quality.This study was conducted with the aim of analyzing and evaluating service quality using the Importance–Performance Analysis (IPA) method based on the Retail Service Quality Scale (RSQS). The results of the IPA analysis were subsequently used as a foundation for determining priority areas for service improvement.The findings reveal that four service attributes fall within Quadrant B, indicating that they should be prioritized for immediate improvement. These attributes include the cleanliness of the shopping area, store layout, employee product knowledge, and product quality. Additionally, twelve attributes fall under Quadrant C, where performance should be maintained due to their already strong results. On the other hand, eleven attributes fall into Quadrant A, meaning they are considered lower  priority, while one attribute is located in Quadrant D, suggesting that Laskar Buah Ngumpakdalem is providing excessive performance in that particular aspect.

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

The problem with the coconut grater machine is that the machine has decreased performance after being used for several years, especially in the mechanical components (electric motor, V belt, Pulley, grater knife, hopper) and its electricity. The purpose of maintenance and repair planning is to obtain costs, maintenance and repair schedules for the period 2026, and the ratio of maintenance costs to profit. The maintenance and repair planning method includes collecting previous maintenance data, applying the inspection-replace-repair-overhaul (IRRO) method, evaluating component conditions, predicting component lifespan, predicting labor costs, predicting supporting equipment that will be used in maintenance, predicting the time to replace spare parts or reinstall repaired components, estimating maintenance and repair costs for the 2026 period, and calculating the maintenance cost to profit ratio. The results of maintenance and repair planning obtained maintenance costs for the 2026 period were IDR 957,000 with an estimated coconut grater machine rental rate of IDR 10,000/hour which has the potential to be rented for 900 hours/year, and the maintenance cost to profit ratio was 10.6% which implies that the coconut grater machine with a capacity of 15 kg/hour is still prospective and feasible to use for the next few years.

In the investment world, stock returns are the leading indicator of a company’s performance and the basis for investor decision-making in the capital market. Fluctuations in stock returns reflect market expectations of the company’s prospects. The retail sector in Indonesia is facing significant pressure from post-pandemic shifts in consumer behavior and increased competition. This study aims to analyze the effect of financial distress, company size, liquidity, operating cash flow, and accounting profit on stock returns in retail sub-sector companies listed on the Indonesia Stock Exchange (IDX) during the period 2021 to 2023. This type of research is causally associated with a quantitative approach. The data used is secondary, in the form of financial statements from retail companies. The sampling technique used was purposive, yielding a total of 39 data points from 13 retail companies. Data testing was carried out using SPSS version 24. The results showed that partially, the variables of financial distress, company size, liquidity, and accounting profit had no significant effect on stock returns. Meanwhile, operating cash flow positively impacts stock returns. These findings indicate that fundamental indicators are not always the main determinants of stock returns. Therefore, investors are advised also to consider external factors such as market sentiment, macroeconomic conditions, and government policies that may have a greater influence on stock performance in the capital market.

This Community Service Program (PkM) was carried out at PT Golden Visalux with the aim of enhancing the effectiveness of the company’s operational cost budget control. Findings from field observations and interviews revealed that the company did not yet have formal written guidelines such as a Standard Operating Procedure (SOP) and lacked a well-structured evaluation system for its budgeting process. As a result, discrepancies occurred between planned and actual expenditures, along with delays in monitoring operational costs. Through this internship project, an SOP and budget measurement standards were created and applied as reference tools for all relevant divisions involved in the budgeting cycle. The SOP implementation led to several improvements, including more accurate cost recording, better monitoring of budget variances, and increased efficiency in operational spending. In addition, the company now possesses an official document that can be utilized continuously for evaluating and refining its cost control practices. Overall, this PkM initiative offers meaningful contributions to strengthening budget management effectiveness and supporting improved financial performance at PT Golden Visalux.

This study examines how company size, asset growth, tangibility, leverage, and total asset turnover affect profitability in consumer manufacturing companies listed on the Indonesia Stock Exchange from 2019 to 2023, using secondary data collected via purposive sampling. The independent variables in this study include the natural logarithm of total assets, asset growth (this year’s total assets relative to the previous year), and tangibility (the fixed asset ratio to total assets). Leverage uses the debt-to-asset ratio, and total asset turnover uses the total asset turnover ratio, while the dependent variable of profitability uses return on assets. Of the 108 companies in the population, 19 that met the research sample criteria were selected, yielding 95 observations. Data analysis was conducted using multiple linear regression, accompanied by classical assumption tests and hypothesis testing through F-tests and t-tests. The findings of this study reveal that asset growth has a significant positive effect on profitability, while leverage shows a significant negative effect. However, firm size, tangibility, and total asset turnover do not exhibit significant relationships with profitability. This study contributes both theoretically and practically to understanding the internal determinants of financial performance in the consumer sector and serves as a reference for management.

This study aims to analyze the effect of profitability, leverage, and liquidity on firm value in food and beverage sub-sector companies listed on the Indonesia Stock Exchange (IDX) for the 2020–2024 period. Profitability is measured by Return On Assets (ROA), leverage by Debt to Equity Ratio (DER), and liquidity by Current Ratio (CR), while firm value is proxied by Price to Book Value (PBV). The study employs a quantitative approach using multiple linear regression analysis. The sample consists of 25 companies selected through purposive sampling, with a total of 125 secondary data observations obtained from annual financial statements. The results indicate that, partially, profitability, financial risk, and liquidity have a positive and significant effect on firm value. Simultaneously, the three independent variables also significantly affect firm value, with an adjusted R² of 43.4%, meaning that 56.6% of the variation in firm value is explained by other factors outside the model. These findings support agency theory and signaling theory, which suggest that strong financial performance, optimal debt management, and adequate liquidity provide positive signals to investors, thereby enhancing firm value.

This qualitative study examines the Public Service Performance Gap at the Ponorogo Regency Transportation Agency (Dishub) in managing high-risk traffic assets, which stems from the failure to synergise Normative Governance with operational Public Asset Management (MAP). Utilising Edwards III's Policy Implementation Model and GG/NPS principles, the core finding indicates that synergy failure is mediated by a Reactive Bureaucratic Disposition. While Dishub adheres procedurally, asset maintenance is largely reactive—performed only after damage or public complaint—not preventive. This non-responsive attitude limits accountability to reporting outputs disconnected from physical service outcomes, leading to low service quality. The proposed substantive solution is to activate Community Involvement (NPS) as a key moderator, which is currently weak, by integrating Functional Participation into the agile MAP cycle. The research recommends an e-governance system with KPIs, where transparently integrated citizen reports automatically trigger work orders, creating external public accountability pressure that forces the reactive bureaucracy to act proactively.

This research aims to analyse the implementation of digital innovation in passport services at the Class I Immigration Office Tanjung Perak as part of bureaucratic reform and Good Governance initiatives. The study employed a qualitative case study approach, involving in-depth interviews, participatory observation, and document analysis, to assess the successes and challenges of digital systems such as SIMKIM and M-Paspor. The findings reveal that digital innovation has successfully improved operational efficiency and generally provided transparency into costs and application status, whilst reducing queuing times and increasing service speed, thereby enhancing public satisfaction and trust in immigration services. However, the innovation has not fully addressed structural and cultural bureaucratic barriers, as field officers tend to become passive when systems experience errors or delays, struggling to provide detailed explanations about issues, which results in information ambiguity and potentially undermines procedural transparency and performance accountability principles. The study concludes that bureaucratic reform in the immigration sector requires internal cultural updates and enhanced human resource competencies to support comprehensive transparency and accountability. Digital infrastructure instability and officers' discretionary authority significantly influence the effectiveness of digital innovation. Whilst digital innovation offers substantial benefits in efficiency and transparency, its success depends heavily on strengthening systems, improving human resource skills, and fostering organisational cultural change, with continuous efforts essential for achieving effective, transparent, and accountable public service.