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This study discusses the effect of store atmosphere, service quality, and layout on purchasing decisions at Sambi Swalayan. This study uses quantitative methods with the aim of knowing the extent to which these three variables influence consumer decisions in making purchases. The research sample was obtained using the Accidental Sampling technique, which is a sampling technique based on chance, where the researcher chooses respondents who are accidentally encountered at the research location and deemed suitable for sampling. The number of respondents obtained in this study was 99 people. In data processing, researchers use several statistical analysis techniques, including validity and reliability tests to test the accuracy and consistency of research instruments. In addition, a classical assumption test was carried out to ensure that the data met the requirements for regression analysis, then continued with multiple linear regression analysis to determine the relationship and influence between variables. The coefficient of determination (R²) is used to see how much the independent variable contributes to the dependent variable, while the t test and f test are used to test the effect partially and simultaneously.The results showed that the store atmosphere had a significant effect on purchasing decisions with a sig value. 0,000 < 0,05. Conversely, service quality has no significant effect because the sig value. 0,383 > 0,05. However, store layout has a significant influence on purchasing decisions with a sig value. 0,000 < 0,05. Simultaneously, the three variables have a significant effect on purchasing decisions at Sambi Swalayan with a sig value. 0,000 < 0,05.

This study aims to determine the effect of the role of work motivation as a mediating variable between workload on employee performance at Perumda Air Minum Tirta Sakti, Kerinci Regency. The method used is quantitative analysis with data collection techniques by distributing questionnaires to 86 respondents. Data were collected using a Likert-scale-based questionnaire, and hypothesis testing used descriptive analysis techniques using SmartPLS 4 software. This study uses Outer Model testing, namely convergent Validity, Discriminant Validity, composite reliability and Inner Model testing, namely R-Square and Bootstrapping. The results of the study indicate that (1) workload has a positive and significant effect on employee performance. So the higher the workload, the higher the employee performance will be. (2) Workload has a positive and significant effect on work motivation. The higher the workload, the greater the motivation that arises in an employee. (3) Work motivation has a positive and significant effect on employee performance. If employees are motivated to work, their performance will be higher and improve. Conversely, if employees are less motivated, they will not be able to do their work well, so their performance will be less than optimal. (4) Work motivation can mediate between workload and employee performance. Employees who have high motivation can complete high workloads because this motivation encourages them to do their work with more enthusiasm and can determine efforts to achieve predetermined goals. e and Bootstrapping. The results of the study indicate that (1) workload has a positive and significant effect on employee performance. So the higher the workload, the higher the employee performance will be. (2) Workload has a positive and significant effect on work motivation. The higher the workload, the greater the motivation that arises in an employee. (3) Work motivation has a positive and significant effect on employee performance. If employees are motivated to work, their performance will be higher and improve. Conversely, if employees are less motivated, they will not be able to do their work well, so their performance will be less than optimal. (4) Work motivation can mediate between workload and employee performance. Employees who have high motivation can complete high workloads because this motivation encourages them to do their work with more enthusiasm and can determine efforts to achieve predetermined goals.

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder that presents significant diagnostic challenges due to its heterogeneous clinical manifestations and symptom overlap with other neurological conditions. Early and accurate diagnosis is critical for initiating timely interventions and improving patient outcomes. Traditional diagnostic approaches rely heavily on clinical expertise and manual interpretation of neuroimaging data, such as structural MRI, Diffusion Tensor Imaging (DTI), and functional MRI (fMRI), which are inherently time-consuming and prone to interobserver variability. Recent advances in Artificial Intelligence (AI) and Deep Learning (DL) have demonstrated potential for automating neuroimaging analysis, yet existing models often suffer from limited generalizability across modalities and datasets. To address these limitations, we propose a Transformer-augmented deep learning ensemble framework for automated ALS diagnosis using multi-modal neuroimaging data. The proposed architecture integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Vision Transformers (ViTs) to leverage the complementary strengths of spatial, temporal, and global contextual feature representations. An adaptive weighting-based fusion mechanism dynamically integrates modality-specific outputs, enhancing the robustness and reliability of the final diagnosis. Comprehensive preprocessing steps, including intensity normalization, motion correction, and modality-specific data augmentation, are employed to ensure cross-modality consistency. Evaluation using 5-fold cross-validation on a curated multi-modal ALS neuroimaging dataset demon-strates the superior performance of the proposed model, achieving a mean classification accuracy of 94.5% ± 0.7%, precision of 93.9% ± 0.8%, recall of 92.9% ± 0.9%, F1-score of 93.4% ± 0.7%, spec-ificity of 97.4% ± 0.6%, and AUC-ROC of 0.968 ± 0.004. These results significantly outperform baseline CNN models and highlight the potential of transformer-augmented ensembles in complex neurodiagnostic applications. This framework offers a promising tool for clinicians, supporting early and precise ALS detection and enabling more personalized and effective patient management strategies.

This study aims to analyze the influence of work motivation and work ability on employee job satisfaction at the Department of Marine and Fisheries of Bima Regency. The research employed a quantitative approach with an associative type of study. The population consisted of 67 employees, and a sample of 38 civil servants was selected using a purposive sampling technique. Data were collected through a closed-ended questionnaire using a five-point Likert scale and analyzed using multiple linear regression with SPSS version 26. The validity and reliability tests confirmed that all items were valid and reliable. The results indicate that work motivation has a positive and significant effect on job satisfaction, and work ability also shows a positive and significant impact. Simultaneously, both variables significantly influence job satisfaction with a coefficient of determination (R²) of 0.967, meaning that 96.7% of job satisfaction variation is explained by motivation and work ability. These findings confirm that enhancing employee motivation and competence is a strategic factor in improving job satisfaction and performance in public sector organizations.

This research aims to analyze the influence of brand image, product quality, and brand ambassador Lee Min Ho on consumer buying interest in Azarine sunscreen products in Kediri city district. This study uses a quantitative method with an associative approach. The sampling method employed is Non-Probability Sampling with purposive sampling technique. In this study, data collection was conducted using a questionnaire distributed to 130 respondents. The analytical techniques used in this research include validity test, reliability test, normality test, multicollinearity test, heteroscedasticity test, multiple linear regression test, t-test (partial), F-test (simultaneous), and coefficient of determination test (R2) analyzed using SPSS 26 software. The results show that partially, the brand image variable does not have a significant effect on buying interest, indicated by a significance value of 0.555 > 0.05. The product quality variable partially has a significant effect on purchase interest with a significance value of 0.041 < 0.05, and the brand ambassador variable partially has a significant effect on purchase interest with a significance value of 0.000 < 0.05. Simultaneously, brand image, product quality, and brand ambassador have a positive and significant effect on purchase interest for Azarine sunscreen products in Kediri city district, with a significance value of 0.000 < 0.05. The coefficient of determination value of 0.581 indicates that 58.1% of the variation in consumer purchase interest can be explained by the three variables: brand image, product quality, and brand ambassador, while the remaining 41.9% is influenced by other external factors outside these variables.

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.

This research presents the design, development, and implementation of a mini smart car prototype that operates using Internet of Things (IoT) technology. The system is built around the ESP8266 microcontroller (Amica version), which functions as the core processing unit responsible for handling Wi-Fi communication and data processing. The motion of the car is controlled by an L298 motor driver module that regulates the operation of DC motors. The entire system is powered by a 3.7-volt rechargeable battery, ensuring portability and energy efficiency. The study discusses in detail the hardware configuration, software programming, and integration of IoT-based control through a web or mobile interface. Functional testing of the prototype, named MINIOT, focuses on evaluating the responsiveness, stability, and reliability of remote control operations. The results are expected to show that the system can effectively receive and execute user commands while transmitting real-time telemetry data, such as motor status and connection indicators. This project demonstrates the feasibility of low-cost IoT-based automation for small-scale robotic applications.

This study aims to analyze the effect of price, green knowledge, and electronic word of mouth (eWOM) on purchase intention toward electric bicycles in Kediri City. A quantitative approach with a causal-comparative design was employed. Data were collected through an online questionnaire involving 112 respondents familiar with electric vehicle concepts, using purposive sampling. The validity and reliability tests confirmed that all research instruments were valid and reliable. Data analysis was performed using multiple linear regression with SPSS version 26. The results reveal that all independent variables have a positive and significant effect on purchase intention, with an R² value of 0.893. Among them, eWOM has the most dominant influence compared to price and green knowledge. These findings indicate that reasonable price perception, adequate environmental knowledge, and positive online reviews simultaneously enhance consumers’ purchase intention for electric bicycles. This study supports the Theory of Planned Behavior in explaining eco-friendly purchasing behavior and provides strategic implications for companies to apply value-based pricing, environmental education campaigns, and effective digital promotion strategies.

This study aims to analyze the influence of work discipline, work motivation, and incentive provision on employee performance in the production department of PT Wonojati Wijoyo. The study employed an associative quantitative approach, with 65 respondents selected using a purposive sampling technique. Data were collected through a five-point Likert-scale questionnaire that passed validity and reliability tests. The data were analyzed using multiple linear regression with SPSS version 29, including classical assumption tests, t-test, F-test, and the coefficient of determination (R²). The results show that work discipline has a positive and significant effect on employee performance, while work motivation and incentive provision have no significant effect. However, simultaneously, the three variables significantly affect employee performance, with a coefficient of determination of 77.9%, indicating that the remaining 22.1% is influenced by other factors not included in the model. These findings highlight the importance of implementing human resource management policies that balance discipline, reward systems, and motivation programs to improve employee performance sustainably.

The era of digital transformation requires office administration employees to master digital competencies and maintain high work motivation to achieve optimal productivity. This study analyzes the influence of digital competence and work motivation on the productivity of office administration employees in the Greater Bandung area. The main problem studied is the gap in understanding how digital competence and work motivation interact in influencing productivity, considering that the majority of previous studies examined the two variables separately. This study aims to analyze the partial and simultaneous influence of digital competence and work motivation on employee productivity. The research method uses a quantitative approach with an explanatory research design involving 420 respondents of office administration employees selected through proportionate stratified random sampling. The data collection instrument is a structured questionnaire with a 5-point Likert scale that has been validated using Confirmatory Factor Analysis and tested for reliability using Cronbach's Alpha. The data analysis technique uses multiple linear regression analysis by first conducting a classical assumption test to ensure the feasibility of the model. The results showed that digital competence had a significant positive effect on productivity with a regression coefficient of 0.398, work motivation had a significant positive effect with a coefficient of 0.425, and simultaneously the two variables explained 52.8% of the variation in employee productivity. These findings confirm that work motivation has a slightly more dominant influence than digital competence, indicating the importance of psychological factors in maintaining long-term productivity consistency. This research contributes to the development of human resource management theory by integrating Resource-Based View Theory and Self-Determination Theory, as well as providing practical implications for organizations to adopt a dual-track approach in employee development that combines digital competency training with a holistic motivation system.

Good Corporate Governance (GCG) is a form of decision by placing the company to be more organized and structured, according on the principles of transparency, independence, responsibility, accountability, also fairness. This study goal to determine the effect of professionalism, internal control systems, also internal audits on the implementation of GCG. This study uses a quantitative research type. The population comprised of 156 individuals is the personnel of the Administration & Commercial Division, Engineering Division, and Operational Division at PT Angkasa Pura Indonesia, Supadio Airport Branch, which are involved in the implementation of GCG. Utilizing the Slovin formula and data collection techniques, the sampling technique is purposive and non-probability, with a total of 115 respondents receiving questionnaires. Validity, reliability, classical assumption tests, multiple linear regression analysis, t-test, also coefficient of determination were used in this study to facilitate data processing, assisted by IBM SPSS version 25. The study findings indicate that professionalism, internal control systems, also internal audits on the implementation of GCG. Partially, the professionalism has a significant positive impact on the implementation of GCG, can be seen from the sig value 0.007 < 0.05, the internal control system has a significant positive impact on the implementation of GCG, can be seen from the sig value 0.000 < 0.05, and internal audit has a significant positive impact on the implementation of GCG, can be seen from the sig value 0.000 < 0.05.

A company's productivity is greatly influenced by the quality of employee performance, which makes employees an important asset in achieving organizational goals. This study aims to analyze the influence of job skills, work experience, and work commitment on employee performance on CV Medali Mas. The research method used is a quantitative approach with multiple linear regression analysis techniques. Data were collected through questionnaires that had been tested for validity and reliability, and classical assumption tests were carried out including normality, multicollinearity, heteroscedasticity, and linearity tests. The results of the analysis showed that simultaneously, the three independent variables, namely work skills, work experience, and work commitment, had a significant effect on employee performance (F count = 6.788; Sig. < 0.001). However, only work experience showed a significant effect on employee performance (t count = 2.765; Sig. = 0.009), while work skills (Sig. = 0.072) and work commitment (Sig. = 0.531) had no significant effect. These findings confirm that work experience is a dominant factor in improving employee performance at CV Medali Mas. The implications of this study suggest that companies pay more attention to the track record of work experience in the recruitment and human resource development process to increase productivity optimally.

Tax avoidance is a crucial issue in corporate governance as it can reduce state revenue and create potential legal claims against the company itself. This study aims to explore the internal elements of firms that influence tax avoidance behaviour, with an emphasis on firm size, capital intensity, sales growth, and earnings management. The methodology employed in this research is a quantitative approach, using purposive sampling to select the companies serving as samples, and multiple linear regression analysis accompanied by classical assumption testing to ensure the reliability of the model used. The results of the analysis indicate that internal firm factors have a significant overall effect on tax avoidance. However, when examined individually, only firm size demonstrates a significant impact, while capital intensity, sales growth, and earnings management do not show a meaningful influence. These findings reaffirm that companies with larger asset bases tend to be more actively engaged in tax avoidance practices, thereby requiring tax authorities to strengthen their oversight of firms with substantial asset scales.

A paradigm shift in public services has occurred rapidly due to the advent of digital technologies, which has led to the creation of more efficient, transparent and user-oriented systems. The study aims to examine how the quality of e-services, reputation, trust in e-services and electronic word of mouth affect user satisfaction with technology-enabled public services. A quantitative approach using multiple linear regression analysis is used to confirm the validity of the model. This method includes validity, reliability and classical assumptions testing. The results show that all four independent variables have a positive effect on user satisfaction both in general and individually. The quality of service, which encompasses factors such as ease of access, speed, system availability, and data security, has been demonstrated to enhance satisfaction. On the other hand, positive feedback and trust in the integrity and competence of the institution strengthen the relationship with users. Electronic word of mouth is crucial to create positive perceptions and attract consumers to services. The results show that to increase user satisfaction and loyalty in digital government services, the integration of these four elements is a key strategy.

This study aims to examine the effect of compensation and leadership style on employee performance at the Gama Karanganyar cigarette company. A sample of 71 respondents was selected using the purposive sampling method, and data were collected through a questionnaire. Data analysis was carried out using multiple linear regression using SPSS, as well as through validity, reliability, classical assumptions, F tests, t tests, and coefficients of determination. The results of the study indicate that compensation and leadership style partially and simultaneously have a positive and significant effect on employee performance.

This study aims to analyze the influence of product quality and location on housing purchase decisions at Shafira Residence Kediri. The research problem focuses on how product quality and location can affect consumers in making purchase decisions. This study employs a quantitative method with questionnaires distributed to respondents and data analysis conducted through validity tests, reliability tests, t-tests, F-tests, and multiple linear regression using SPSS 25. The results indicate that product quality has a significant partial effect on purchase decisions. This means that the better the quality of the product offered, the higher the consumer’s interest in purchasing a house at Shafira Residence. Similarly, the location variable shows a significant partial effect on purchase decisions, where accessibility and strategic positioning serve as key factors. Simultaneously, product quality and location significantly influence purchase decisions, with an F-test value of 10.727 (significance < 0.001) and a coefficient of determination (R²) of 0.671, indicating that the two independent variables explain 67.1% of the variation in purchase decisions. The conclusion of this study is that product quality and location are important factors influencing consumer purchase decisions at Shafira Residence Kediri. Therefore, developers need to improve housing product quality, ensure timely completion of construction, and enhance road access to the location in order to attract more consumers.

Environmental issues are becoming increasingly urgent, particularly the worsening clean water crisis, which requires effective mass communication to raise awareness and encourage community involvement. Social media has emerged as a strategic platform for disseminating information and promoting environmental actions. PandawaraGroup, a dedicated TikTok account focusing on environmental issues, utilizes persuasive communication to encourage positive actions. This study aims to examine the influence of persuasive communication by the @PandawaraGroup TikTok account on followers’ interest in participating in social actions. The research employed a quantitative method with data collected through an online questionnaire (Google Form) distributed to 100 respondents who are followers of the account. Data analysis was conducted using simple linear regression along with descriptive tests, validity, reliability, and dimensional analysis. The findings reveal that persuasive communication from @PandawaraGroup has a moderate influence on followers’ interest in engaging in social actions. Based on these results, the study suggests that @PandawaraGroup should strengthen follower engagement through active interactions, such as responding to comments and direct messages, as well as organizing regular Q&A or discussion sessions. Furthermore, diversifying content with creative approaches is essential to maintain audience interest and relevance. These strategies are expected to enhance the effectiveness of @PandawaraGroup in promoting environmental awareness and public participation.

This study aims to determine the effect of using the Rotating Wheel (DaPur) learning media on the understanding of multiplication concepts of second-grade students at Tugu Utara 03 Elementary School, North Jakarta. The study was conducted in February 2025 with a quantitative approach and quasi-experimental design. The research sample consisted of 60 students divided into two classes, namely class II A as the control group and class II B as the experimental group, each consisting of 30 students. Data collection was carried out through the administration of pre-tests and post-tests. The instruments used have been tested for validity and reliability, with the results that all questions were declared valid and reliable (Cronbach's Alpha = 0.896). Data were analyzed using an independent sample t-test. The results of the analysis showed that there was a significant difference between the post-test scores of students in the experimental class and the control class, with a t-value of 27.757 and a significance of 0.001 (<0.05). This proves that the rotating wheel media has a positive and significant effect on the understanding of multiplication concepts. The Spinning Wheel media has been proven to help students understand multiplication material in a more enjoyable and concrete way, thereby improving their abilities in the aspects of translation, interpretation, and extrapolation of concepts. Based on these results, it can be concluded that the Spinning Wheel (DaPur) media is effective for use in mathematics learning, especially multiplication material.

This study aims to examine the influence of transformational leadership, work motivation, and organizational culture on organizational commitment at PT Kembang Jawa Permai. The research employs a quantitative approach with an associative causal design, using a survey method and data collected through a questionnaire distributed to all 46 employees as respondents. The sampling technique used is saturated sampling, considering the small population size. Data analysis was performed using multiple linear regression analysis with validity, reliability, and classical assumption tests (normality, multicollinearity, heteroscedasticity, and linearity) conducted beforehand to ensure model accuracy. The results indicate that transformational leadership, work motivation, and organizational culture each have a positive and significant effect on organizational commitment, both partially and simultaneously. Among the three variables, organizational culture shows the most dominant influence, indicating that strong organizational values and teamwork orientation play a key role in strengthening employee commitment. The coefficient of determination (R²) of 0.848 implies that 84.8% of the variation in organizational commitment can be explained by the three independent variables. This study contributes to human resource management theory and provides practical insights for organizations to enhance employee commitment through effective leadership, motivation, and cultural reinforcement.

This study aims to analyze the influence of work ethic, work spirit, and physical work environment on employee performance at the Regional Revenue, Financial, and Asset Management Agency (BPPKAD) of Kediri City. This research employed an associative quantitative method with a descriptive approach. The population consisted of 215 employees, and a purposive sampling technique was used to select 100 respondents. Data were collected using a five-point Likert scale questionnaire that had been tested for validity and reliability. Data analysis was conducted through classical assumption tests and multiple linear regression analysis using SPSS version 26. The results indicate that work ethic, work spirit, and physical work environment have a positive and significant effect on employee performance, both partially and simultaneously. The coefficient of determination (R²) value of 0.693 shows that 69.3% of employee performance variation can be explained by the three independent variables. Therefore, improving employee performance at BPPKAD Kediri City can be achieved by strengthening work ethics, increasing work spirit, and creating a comfortable and productive physical work environment.