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This study aims to examine in depth the influence of price, promotion, and switching barriers on customer loyalty of Telkomsel prepaid cards, with customer satisfaction as an intervening variable in Generation Z in West Palu District. This study is motivated by the intense competition in the telecommunications industry and the importance of maintaining customer loyalty amidst the increasing number of service options. Data were collected from 100 respondents who are active Telkomsel prepaid card users through questionnaires. Data analysis was conducted using the Structural Equation Modeling-Partial Least Squares (SEM-PLS) approach using SmartPLS software version 4.0. The results show that the price variable has a significant direct effect on customer loyalty, but does not show a significant effect on customer satisfaction. Conversely, promotion has a significant effect on satisfaction, but does not directly affect customer loyalty significantly. Switching barriers are shown to have a significant effect on both satisfaction and loyalty, indicating that the higher the switching barriers, the more likely customers will remain loyal. In addition, customer satisfaction also significantly affects loyalty. Another important finding is that of the several indirect influences tested, only the path from promotion through satisfaction to loyalty was statistically significant. Therefore, this study suggests the importance of targeted promotional strategies and strengthening switching barriers as effective measures in retaining young customers who are prone to switching services. Furthermore, this study emphasizes that customer loyalty is not solely determined by competitive pricing factors, but is more complex and influenced by customers' emotional and psychological experiences, such as satisfaction with the service and perceptions of the ease or difficulty of switching to another service provider.

This study aims to analyze the influence of Online Advertising on purchasing decisions for Skintific products by considering the mediating role of Trust and Social Media Engagement. Skintific, as a digital-native skincare brand, actively utilizes digital marketing strategies through various social media platforms such as Instagram, Tiktok, or e-commerce like Shopee to increase consumer appeal, especially in Malang City. This study uses a quantitative approach with the Partial Least Squares Structual Equation Modeling (PLS-SEM) method to test the relationship between variables. The sample was obtained through a purposive sampling technique with 114 respondents who are active consumers of Skintific. The results of this study indicate that Online Advertising has a significant effect on purchasing decisions, both directly and through the mediation of Trust and Social Media Engagement. Theoretically, this study extends the application of the Theory of Reasoned Action (TRA) in explaining the formation of attitudes and subjective norms of consumers towards digital advertising, as well as the Technology Acceptance Model (TAM) which shows how perceived ease and usefulness of digital advertising content affect user engagement. Customer Trust Theory in E-Commerce is used to explain how trust is formed in online transactions, while Relationship Marketing Theory emphasizes the importance of long-term interactions between brands and consumers. The Theory of Planned Behavior (TPB) expands this framework by adding the construct of behavioral control, which explains how consumers' perceptions of ease and barriers contribute to purchasing decisions. Social Influence Theory is used to understand the influence of social norms and online community opinions on purchase intentions, and Uses and Gratifications Theory (UGT) to examine consumer motivations in accessing and interacting with digital content based on information needs, affection, and self-identity. This research provides theoretical contributions in strengthening the relevance of integrating several consumer behavior theories in a digital context.

This research investigates the impact of tourist attractions, service quality, and electronic word of mouth (e-WOM) on visitors’ intention to revisit Sendang Tourism Village, located in Tulungagung, East Java. Additionally, the study explores the function of visitor satisfaction as an intermediary variable and digital promotion as a potential moderator. The study emerged from the concern over fluctuating visitor numbers despite Sendang’s rich appeal in terms of natural landscapes, cultural heritage, and traditional charm. A quantitative method was applied, making use of a structured data collection process structured survey distributed to 138 individuals who had previously visited the site. The data were examined through Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate and interpret the relationships between the research variables both the direct and mediated pathways between variables. This technique was chosen for its ability to explain complex relationships within behavioral research. The results reveal that tourist attraction, service quality, and e-WOM have a significant positive influence on revisit intention. Furthermore, tourist satisfaction was shown to act as a bridging variable between these predictors and return visits. On the other hand, digital promotion was not found to significantly moderate these effects. This indicates that digital marketing, while useful, may not independently boost visitor return rates unless paired with strong destination quality and positive visitor experiences. From a conceptual standpoint, the study contributes to tourism marketing discourse by incorporating both mediation and moderation into the revisit intention framework. In practical terms, the findings highlight the need for tourism managers to prioritize visitor satisfaction and enhance the core travel experience. Digital tools should complement rather than replace genuine service improvements and compelling destination features to effectively encourage repeat tourism.

This study compares the performance of two deep learning models, namely Convolutional Long Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional Network (LRCN), in the task of recognizing human activity from videos. Human activity recognition is an important field in computer vision with many applications, such as security monitoring, human-computer interaction, and social media-based video analysis. ConvLSTM is a model that combines convolution operations with long-term memory LSTM, thus capable of capturing spatial and temporal information simultaneously. This approach is ideal for processing video data sequences that have spatial and temporal dimensions. On the other hand, LRCN combines the power of spatial feature extraction from Convolutional Neural Network (CNN) and temporal sequence modeling through Recurrent Neural Network (RNN), specifically LSTM, to understand movement patterns in videos. The study used the UCF50 dataset consisting of 50 activity classes, but was limited to five classes for the focus of the experiment. The dataset was divided into 80% for training and 20% for testing, and the model was drilled for 50 epochs using early stopping to prevent overfitting. The results show that both models have high training performance. ConvLSTM achieved a training accuracy of around 98% and a validation accuracy of 90%, while LRCN achieved a training accuracy of 99.5% and a validation accuracy of 88%. Although ConvLSTM demonstrated good stability on the validation data, further testing using TikTok videos as real-world data showed that LRCN had a higher confidence level in recognizing activities, with most predictions achieving confidence scores above 80%. This difference in performance indicates that while ConvLSTM excels in generalizing on training data, LRCN is more robust to real-world data variations.

This study aims to analyze the impact of experiential marketing on repurchase intention, mediated by guest satisfaction at Aston Batam Hotel & Residence. The background phenomenon underlying this research is the discrepancy between the high number of guests staying and the low number of returning guests during the period from January to June 2024. Despite receiving many positive reviews, the hotel has yet to establish strong customer loyalty. This indicates a need to evaluate the hotel's marketing strategies and the guest experience it provides. A quantitative approach was adopted, employing the Structural Equation Modeling–Partial Least Squares (SEM-PLS) method. A total of 130 respondents were obtained through the distribution of questionnaires using a five-point Likert scale, measuring guest perceptions of experiential marketing, guest satisfaction, and repurchase intention. The data were analyzed using SmartPLS software to test the validity, reliability, and relationships among the structural model variables. The results of the analysis show that experiential marketing does not have a direct significant effect on repurchase intention, but it has a positive and significant effect on guest satisfaction. Furthermore, guest satisfaction is proven to have a significant impact on repurchase intention. The findings also confirm that guest satisfaction acts as a mediating variable in the relationship between experiential marketing and repurchase intention. The conclusion of this study emphasizes that guest satisfaction is a crucial factor in building customer loyalty and encouraging the intention to repurchase. Therefore, the hotel needs to enhance the quality of the customer experience holistically—not only focusing on service delivery but also creating meaningful and memorable impressions. In the highly competitive hospitality industry, delivering impactful and satisfying experiences is key to retaining customers and increasing guest retention. This study provides both theoretical and practical contributions to the development of experience-based marketing strategies and offers a deeper understanding of the role of guest satisfaction as a key driver of customer loyalty.

In the rapidly changing digital and global era, organizations are required to be adaptive and innovative to survive and compete sustainably. Employee performance is one of the strategic factors in supporting organizational success. This research is motivated by the urgency of improving performance through innovative work behavior (IWB) and work engagement, which are considered two key elements in addressing the challenges of modern organizations. The main objective of this study is to analyze the effect of IWB on employee performance and evaluate the mediating role of work engagement in this relationship. This study uses a quantitative approach with the Structural Equation Modeling Partial Least Square (SEM-PLS) method, and was conducted on employees of the Batu City Regional Disaster Management Agency (BPBD). The results of the analysis indicate that IWB has a positive and significant influence on work engagement and employee performance. In addition, work engagement is also proven to act as a partial mediator that strengthens the relationship between IWB and performance. This means that the higher the innovative behavior of employees, the higher their engagement in work, which ultimately has a positive impact on performance improvement. These findings enrich the theoretical literature, particularly supporting theoretical frameworks such as Social Exchange Theory, Self-Determination Theory, Job Demands-Resources Model, and Conservation of Resources Theory. Practical implications of this research suggest that organizations need to create a work environment that supports innovative behavior and enhances employee work engagement by providing autonomy, recognition, and opportunities for self-development. This research suggests further studies to explore other contextual factors, such as organizational culture, transformational leadership, and supervisor support, which have the potential to strengthen the relationship between IWB, work engagement, and employee performance in various organizational sectors.  

This study aims to examine the mediating role of Organizational Citizenship Behavior (OCB) in the relationship between Perceived Organizational Support (POS), Perceived Organizational Commitment (POC), and employee performance in a state-owned enterprise in Indonesia. In the context of growing public expectations for improved service quality, organizations are challenged to enhance employee performance not only in formal roles but also in discretionary behaviors. Based on Social Exchange Theory (SET), this research proposes that POS and POC can affect performance both directly and indirectly through OCB. Data were collected through a census of 138 permanent employees at PT Jasa Raharja, East Java Branch. The analysis employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypothesized relationships. The findings show that POS has a significant positive influence on both OCB and employee performance. On the other hand, POC significantly influences OCB but does not have a direct impact on performance. The results also reveal that OCB plays a partial mediating role in the relationship between POS and performance, while it acts as a full mediator in the relationship between POC and performance. These findings emphasize the importance of fostering OCB within the organization to optimize the impact of support and commitment on employee performance. Encouraging voluntary, extra-role behaviors can bridge the gap between how employees perceive organizational treatment and how they perform. The study suggests practical implications for human resource management in public sector institutions. Policies should focus on building a supportive and engaging work climate that enhances affective commitment and acknowledges employee contributions beyond formal duties. Doing so will not only improve individual performance but also contribute to broader organizational effectiveness in a competitive and service-oriented environment.

The purpose of this paper is to examine the role of Green Intellectual Capital (GIC) in the development of waste-to-energy (WtE) technologies, which is of special relevance for the incineration plants in the Gulf Cooperation Council (GCC) countries. Although previous studies have focused primarily on technological and infrastructural aspects, this paper investigates the contribution of intangible assets such as green human, structural, and relational capital towards the performance and sustainability of WtE systems. Adopting a mixed-method approach, the results show that Green Structural Capital has the first place in supporting operational efficiency, followed by Green Human Capital and Green Relational Capital. The statistical analysis based on Structural Equation Modeling (SEM) shows that all GIC dimensions are positively related to plant performance, with institutionalized knowledge and systems as the most significant enablers of innovation. Qualitative findings also highlight constraints such as lack of training, unhandy knowledge systems, and poor public-private interaction. The findings recommend policy suggestions for promoting GIC assimilation within environmental infrastructure and provide a region-based theoretical model connecting knowledge-based resources and sustainable waste management practices. The study emphasizes the importance of integrating intellectual capital into the strategic decision-making process of waste-to-energy plants to enhance their operational effectiveness and long-term sustainability. Additionally, the research underlines that fostering a knowledge-sharing culture and enhancing collaboration between various stakeholders, including governmental bodies, private firms, and research institutions, is critical for the success of green initiatives. Training programs aimed at developing green human capital and improving public-private partnerships are essential to overcoming the current barriers to innovation in the WtE sector. This study has practical and scholarly implications in unifying the focus from just technology to the facilitating role of human and intellectual capital in green transformation, further bridging the gap between environmental sustainability and technological advancements in waste management.

Delivery delays pose a major challenge in the e-commerce industry, often leading to decreased customer satisfaction and negatively impacting business operations. In this study, the XGBoost (Extreme Gradient Boosting) algorithm is applied to predict delivery delays based on a dataset containing 96,476 records. These records include various features relevant to the delivery process, such as shipping distance, carrier performance, and order characteristics. The model achieves a high overall accuracy of 93.24%, indicating strong general performance. In particular, XGBoost demonstrates excellent results in predicting on-time deliveries, achieving a precision of 93% and a recall of 100%. However, the model struggles to correctly identify delayed deliveries. The recall for delayed deliveries is 0%, and the F1-score is extremely low at 0.01. This significant discrepancy reveals a critical limitation in the model's performance — the inability to detect minority class cases (delayed deliveries) due to class imbalance within the dataset. The results highlight the importance of addressing data imbalance in predictive modeling for delivery outcomes. When the dataset is dominated by on-time delivery records, the model tends to be biased toward that class, failing to learn the patterns associated with delays. To improve performance, the study recommends integrating class balancing techniques such as SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic samples of the minority class. Additionally, the use of alternative evaluation metrics beyond accuracy — such as precision, recall, and F1-score for each class — is suggested to provide a more comprehensive understanding of model effectiveness. Overall, the study provides valuable insights into the complexities of predicting delivery delays and outlines practical strategies for enhancing future models in e-commerce logistics analytics.

This study aims to analyze the effect of service quality on repurchase intention with customer satisfaction as a mediating variable at Pandan View Mandeh Resto. The study was conducted with a quantitative approach and used a causal associative design to explain the relationship between variables. The population in this study were all customers who had received services and consumed food and drinks at Pandan View Mandeh Resto within the past year, with a minimum age requirement of 17 years. The sample set was 130 respondents and was obtained through questionnaire distribution. Data collection was carried out using a questionnaire instrument that had been prepared based on indicators from the research variables. Furthermore, the data obtained were analyzed using the Partial Least Square - Structural Equation Modeling (PLS-SEM) method with the help of the Smart-PLS application. The analysis carried out included outer model testing (validity and reliability), inner model testing (relationships between latent variables), hypothesis testing, and mediation testing. The results showed that service quality has a positive and significant influence on customer satisfaction. Furthermore, customer satisfaction has been shown to have a positive and significant effect on repurchase intention. Furthermore, service quality also has a significant positive effect on repurchase intention. Mediation analysis shows that customer satisfaction acts as a partial mediator in the relationship between service quality and repurchase intention. This means that some of the influence of service quality on repurchase intention occurs through customer satisfaction, while others occur directly. These findings emphasize the importance of maintaining and improving service quality to create satisfaction, which in turn impacts customer loyalty.

This study aims to empirically analyze the influence of product quality and service quality on customer satisfaction, with purchase decisions as a mediating variable, specifically among Alfamart consumers in the Singosari area, Malang Regency. The background of this research stems from the increasing competition in the modern retail sector, which requires companies to understand the key factors influencing customer satisfaction and loyalty. In this context, product quality and service quality are considered essential elements that shape customer perceptions and influence their purchasing decisions. A quantitative approach with an explanatory research design was used in this study. The sample was selected using purposive sampling, involving 97 respondents who had made purchases at Alfamart at least twice within the last three months. Data collection was conducted through the distribution of structured questionnaires, which included items measuring perceptions of product quality, service quality, purchase decisions, and customer satisfaction. The data were analyzed using the Partial Least Square-Structural Equation Modeling (PLS-SEM) method to examine the relationships among variables comprehensively and simultaneously. The results of the analysis show that both product quality and service quality have a significant and positive effect on purchase decisions and customer satisfaction. Moreover, purchase decisions are proven to significantly mediate the influence of both product and service quality on customer satisfaction. This indicates that the purchase decision acts as a crucial mechanism linking the effect of quality to overall satisfaction. These findings contribute theoretically to the consumer behavior literature in the modern retail context and offer practical implications for retail management, such as Alfamart. Retailers are encouraged to focus on improving both product offerings and service delivery as strategic efforts to enhance customer experience. By doing so, they can effectively increase customer satisfaction and strengthen customer loyalty in the long term.

This study investigates the influence of the Selena application’s service features on job satisfaction among sales personnel at PT Pegadaian Kanwil VII Denpasar, with the interest in using technology serving as a mediating variable. The research is motivated by the growing importance of digital applications in supporting operational performance and employee satisfaction, especially in the financial services sector. A quantitative causal research approach was employed to examine the relationships between the variables. Data were obtained through questionnaires distributed to respondents and supplemented with interviews to gain deeper insights. The sample consisted of 50 active sales personnel operating in the Bali region, selected using purposive sampling to ensure relevance to the research objectives. Data analysis was carried out using Structural Equation Modeling with the Partial Least Square (SEM-PLS) method through the WarpPLS 8.0 application. This analytical approach was chosen because it is effective in testing complex causal relationships and mediation effects, even with relatively small sample sizes. The findings reveal that the service features of the Selena application have a positive and significant effect on job satisfaction. Furthermore, the service features also exert a positive and significant influence on employees’ interest in using technology. The interest in using technology itself positively and significantly affects job satisfaction, confirming its role as an important factor in enhancing workplace outcomes. In addition, the mediation test results demonstrate that the interest in using technology significantly mediates the effect of service features on job satisfaction. These results highlight the importance of continuously improving application service features and fostering employees’ willingness to adopt technology to maximize job satisfaction and overall performance.

This study aims to analyze the impact of internal control systems and service quality on accountability and work culture at LP3I Banda Aceh. Specifically, it investigates how internal control systems influence accountability and work culture, as well as the effect of service quality on both accountability and work culture. Additionally, the research explores the role of work culture as a moderating variable in the relationship between internal control systems, service quality, and accountability. The study involves two exogenous variables, namely internal control systems and service quality, and one endogenous variable, accountability. Work culture is treated as a moderating variable. The research population includes employees and lecturers at LP3I Banda Aceh, with a total of 53 respondents in the sample. The analysis was conducted using Structural Equation Modeling (SEM) with PLS 3.0 software. The findings reveal that both internal control systems and service quality partially affect accountability and work culture. More specifically, internal control systems have a positive effect on accountability, indicating that effective internal controls contribute to higher levels of accountability within the institution. Likewise, service quality also influences accountability, suggesting that improving the quality of services leads to better accountability outcomes. Furthermore, work culture is shown to have a significant impact on accountability, reinforcing the idea that a positive work culture fosters higher levels of accountability. Importantly, the study demonstrates that work culture acts as a moderating variable, enhancing the relationship between internal control systems, service quality, and accountability. This indicates that a strong work culture can amplify the positive effects of internal control systems and service quality on accountability, contributing to the overall effectiveness of the institution.

Improving the accuracy of emission monitoring in the Continuous Emission Monitoring System (CEMS) is crucial to support compliance with environmental regulations, especially in gas and steam-based power plants (PLTGU). At PT Jawa Satu Power's PLTGU, the purging system on the CEMS sample probe is still performed manually and limited to scheduled preventive maintenance, potentially reducing emission data reliability due to particle contamination. This study aims to design and simulate an automatic purging system based on a Programmable Logic Controller (PLC), taking into account technical parameters such as timer, gas pressure, temperature, and sample flow rate. The system design was carried out through the modeling of automatic control logic using CX-Programmer software, with a protection approach based on real-time conditions and timing. The design results show that the automatic purging system can improve cleaning consistency, reduce the risk of contamination, and enhance the integrity of emission monitoring data. This study is expected to serve as a foundation for developing a more applicable and integrated automated purging system for CEMS in the future.

This study presents a machine learning approach for predicting the dimensions of microstrip antenna slots based on antenna performance parameters such as frequency, gain, directivity, return loss (S11), radiation efficiency, and VSWR. A two-phase methodology was employed. In the first phase, ten regression algorithms were evaluated, and Random Forest was identified as the most effective model based on Mean Absolute Error (MAE) and R-squared (R²) scores. In the second phase, hyperparameter tuning was conducted using Grid Search to further improve the model’s performance. The optimized Random Forest model demonstrated consistent improvements in predictive accuracy, with R² values increasing across all output variables. These results indicate that the combination of regression-based modeling and systematic hyperparameter tuning is effective for capturing complex relationships in antenna design tasks. The proposed approach offers a promising data-driven alternative for geometric prediction in microstrip antenna development, particularly when analytical models are insufficient.

This research explores the influence of customer satisfaction on the financial performance of Perumda Tirtanadi, with a particular focus on the digital service system as a moderating factor. The study is driven by the growing urgency for digital innovation in public service sectors, particularly in the wake of the COVID-19 pandemic, which significantly altered customer engagement patterns. A quantitative method is adopted, employing Partial Least Squares–Structural Equation Modeling (PLS-SEM) using the SmartPLS 4 software to process the collected data. The analysis demonstrates that both customer satisfaction and digital services have independent and significant positive effects on financial outcomes. However, the interaction between these two variables does not show a statistically significant moderating effect. These findings underline the value of digital infrastructure as a strategic internal resource that supports financial growth. Nevertheless, the minimal moderating impact suggests that a portion of customers either lack access or sufficient skills to effectively utilize the available digital platforms. This study adds to the current body of knowledge by examining the interplay between digital transformation and customer satisfaction in shaping financial performance, framed through the Resource-Based View (RBV) theory. The research suggests that improving digital literacy and promoting better adoption of digital tools among customers is essential to fully capitalize on the benefits of technological advancement. Furthermore, it highlights the need for continuous training and support to ensure that all customers can engage with digital services effectively, thereby enhancing overall satisfaction and financial performance. By addressing these gaps, organizations can foster a more inclusive digital environment that benefits both the customers and the service providers.

This study explores the key determinants of field engineer efficiency in the field service industry by analyzing the impact of self-efficacy, resource management, and time management on operational performance. Employing a quantitative research approach, data were collected using saturated sampling from 102 field engineers and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The measurement model showed robust psychometric properties, satisfying the thresholds for convergent validity, discriminant validity, and internal consistency reliability. The structural model results indicate that all three independent variables significantly influence field engineer efficiency. Self-efficacy was found to have the strongest effect (β = 0.421, p < 0.001), followed by resource management (β = 0.347, p < 0.001) and time management (β = 0.289, p < 0.001). The model accounts for 68.7% of the variance in field engineer efficiency, reflecting strong explanatory power and predictive accuracy. Among these variables, self-efficacy emerged as the most dominant factor, suggesting that field engineers' belief in their ability to perform tasks is a critical driver of operational success. High self-efficacy enhances motivation, resilience, and effective problem-solving under pressure, making it essential in dynamic and unpredictable field environments. Resource and time management also play crucial roles in supporting engineers' ability to complete tasks efficiently by ensuring optimal allocation of tools, equipment, and time. The findings provide practical implications for field service organizations aiming to improve workforce performance. Investing in training programs that strengthen self-efficacy, combined with systematic improvements in resource and time management practices, can significantly enhance operational outcomes. By prioritizing these factors, organizations can boost engineer efficiency, reduce operational costs, and improve service delivery, ultimately gaining a stronger competitive advantage in the industry.

Photogrammetry is a technique for measuring and modeling three-dimensional objects by utilizing digital imagery from various perspectives. In the context of reverse engineering, this technique serves to duplicate, reconstruct, and analyze the dimensions of physical objects with a high degree of accuracy. The main advantage of photogrammetry lies in its ability to capture the details of the shape and texture of objects without the need for physical contact. However, the quality of photogrammetry scan results is greatly influenced by a number of technical factors, especially lighting and camera sensor sensitivity (ISO) settings. Variations in these two parameters can cause deviations or dimensional deviations in the resulting 3D model. This study aims to quantitatively evaluate the influence of lighting intensity and camera ISO setting on dimensional deviation in photogrammetry scan results. The research method used is experimental, where the dimensions of the scanned object are compared to the original dimensions using precision measuring instruments. The results showed that both the lighting level and the ISO setting had a significant influence on the accuracy level of the 3D model. The ideal lighting intensity range was found to be in the range of 125–150 lux, where shadows and light reflections could be minimized. Meanwhile, the use of low ISO (around 200) is able to produce cleaner image textures and minimize noise, resulting in smaller dimensional deviations. Additionally, the interaction between moderate lighting and low ISO is proven to provide the best scanning accuracy. This combination is able to maintain a balance between image quality and surface detail of the object. These findings not only provide practical recommendations regarding the regulation of scanning conditions, but can also serve as a guideline for industry practitioners and academics in improving the quality of reverse engineering results. With a proper understanding of lighting and ISO variables, the photogrammetry process can be optimized to produce more accurate and efficient 3D models.

Penelitian ini bertujuan untuk menganalisis pengaruh orientasi kewirausahaan dan orientasi pembelajaran terhadap kinerja pemasaran baik secara langsung maupun tidak langsung melalui inovasi sebagai variabel mediasi. Populasi yang digunakan adalah seluruh pelaku industri kecil makanan dan minuman di Kabupaten Kendal, dengan ukuran jumlah sampel yang digunakan adalah 100 responden. Teknik pengambilan sampel menggunakan simple random sampling. Sumber data yang digunakan data primer, dengan metode pengumpulan data kuesioner. Teknik analisis data yang digunakan adalah Structural Equation Modeling-Partial Least Square (SEM-PLS). Hasil penelitian menjelaskan jika orientasi kewirausahaan berpengaruh positif dan signifikan terhadap kinerja pemasaran, orientasi pembelajaran berpengaruh positif dan signifikan terhadap kinerja pemasaran, dan inovasi berpengaruh positif dan signifikan terhadap kinerja pemasaran. Hasil penelitian juga menjelaskan jika orientasi kewirausahaan berpengaruh positif dan signifikan terhadap inovasi, dan orientasi pembelajaran berpengaruh positif dan signifikan terhadap inovasi. Hasil uji mediasi diperoleh bahwa inovasi dapat memediasi pengaruh orientasi kewirausahaan terhadap kinerja pemasaran, dan inovasi dapat memediasi pengaruh orientasi pembelajaran terhadap kinerja pemasaran.

When producing a perfect product, design process namely 2D modeling and 3D drawing, are closely related. The purpose of this study is to design a 3D model of a gun mastering machine product as a fan drive using CAD applications. The method of this study is decision data on API production (analytical product inspection). Measurement of sterling machine degradation using CPK (deviation) received results of -0.97, -0.22, 0.86, 0.11, 0.11 between zero and what indicates this specification. On average, the results of the stirling fluid fluorescent agent measurement are red numbers indicating that the survey dimensions are absent, especially from the basic dimensions of point 4, with samples 2, 3, and 5 exceeding the specified tolerance limits.