Publication Search

Public Wi-Fi services (Wifi_STAKat) at the State Catholic College of Pontianak continue to face technical issues, such as network speed and connection stability, as well as non-technical challenges, including the responsiveness of administrators to user complaints. This study aims to analyze user satisfaction based on the five Servqual dimensions and to map improvement priorities using the Importance–Performance Analysis (IPA) method. The relationship between Servqual and IPA is explained by mapping GAP values (perception–expectation) into the four IPA quadrants to determine the urgency level of service improvements. A 4-point Likert scale was used to avoid neutral responses and strengthen the clarity of respondents’ perceptions. The results show that all dimensions have negative GAP scores, particularly responsiveness and reliability, which are directly related to technical indicators (speed, stability, coverage) and non-technical factors (responsiveness to complaints, ease of access. The study recommends integrating an IT-based monitoring system and increasing network capacity to improve service quality.

The completeness of nursing care documentation serves as a primary indicator of hospital service quality and remains a critical component of patient safety, clinical communication, and legal accountability. Despite its importance, empirical evidence from various healthcare facilities indicates that nursing records are frequently suboptimal, incomplete, or inconsistent. This study aims to analyze the complex interrelationships between burnout and coaching on the completeness of nursing care documentation, specifically examining the role of nursing competence as a mediating (intervening) variable at the Dr. Chasbullah Abdulmadjid General Hospital in Bekasi City. Utilizing a quantitative research framework with a cross-sectional design, this study sampled practicing nurses stationed across inpatient and intensive care units. Primary data were gathered through a validated, structured questionnaire designed to measure psychological burnout, the frequency of clinical coaching, perceived professional competence, and the objective completeness of documentation. The data were subjected to rigorous analysis using Structural Equation Modeling–Partial Least Squares (SEM-PLS) to test the hypothesized pathways within the conceptual model. The results demonstrated that burnout exerts a significant negative influence on both nursing competence and the quality of documentation, suggesting that emotional exhaustion hinders professional performance. Conversely, systematic coaching was found to have a significant positive impact, directly improving both competence levels and documentation adherence. Critically, the analysis confirmed that nursing competence acts as a vital intervening variable; it effectively mediates and strengthens the influence of both burnout reduction and coaching interventions on the overall completeness of nursing care records. This study concludes that proactive burnout management and the institutionalization of structured coaching programs are essential strategic priorities. By addressing these factors, hospital management can enhance individual nurse competence, thereby ensuring high-quality, comprehensive nursing documentation that supports patient safety and institutional integrity.

Recent Internet of Things (IoT) intrusion detection studies have reported near-perfect benchmark performance for Distributed Denial of Service (DDoS) detection, yet limited attention has been given to understanding how different traffic representations contribute to the detection process under highly imbalanced traffic conditions. This study presents an ablation-driven analysis to investigate the contribution of statistical and temporal representations for large-scale IoT DDoS detection using the CICIoT2023 dataset. Three experimental scenarios are evaluated, including statistical representation, temporal sequence representation, and hybrid statistical–temporal representation. Temporal representations are learned using a one-dimensional Convolutional Neural Network (1D-CNN) with lag-based traffic sequences, while ensemble tree-based classifiers are employed for final classification and representation analysis. In addition, multiple ablation configurations are designed to evaluate the impact of temporal dependency modeling and feature engineering strategies on detection performance. Experimental results show that statistical traffic representations remain highly effective for DDoS detection on CICIoT2023, achieving 99.36% accuracy and 99.31% weighted F1-score in the statistical representation scenario. Feature importance analysis further indicates that engineered statistical features contribute substantially more to the classification process than CNN-based temporal representations. Although temporal modeling captures sequential traffic behavior, its contribution is relatively limited and mainly acts as a complementary representation. Furthermore, the hybrid configuration produces only marginal improvements over the statistical representation alone. These findings highlight the importance of representation-level analysis for understanding the actual contribution of statistical and temporal modeling in modern IoT intrusion detection systems beyond relying solely on benchmark accuracy.

 The quality of digital system services in the ship clearance process is closely related to the work effectiveness of shipping agency companies. The advancement of port operational services is influenced by the system’s ability to provide convenience, speed, accuracy, and service integration for users. The system used in the ship clearance process must be able to meet user needs and support inter-agency coordination so that ship services can operate more effectively and efficiently. This study aims to analyze the performance level and the level of user importance regarding the implementation of the Single Submission Pengangkut System in the ship clearance process at PT Trans Cakrawala Perkasa. The research employed a descriptive quantitative approach with a sample of 30 respondents consisting of ship agents involved in the clearance process. Data were collected through questionnaires, observations, and literature studies. The data were analyzed using validity tests, reliability tests, conformity level analysis, and the Importance Performance Analysis (IPA) method. The results indicate that the SSM Pengangkut System has made a positive contribution to accelerating document processing, facilitating system usage, increasing work productivity, and supporting coordination among related agencies. Based on the IPA Cartesian diagram, one indicator was identified as a priority for improvement, while other indicators were categorized as maintain performance, low priority, and high performance with lower importance levels. Overall, the implementation of the Single Submission Pengangkut System has been running quite well; however, continuous evaluation and improvement are still needed to optimize the ship clearance process further.

This study aims to analyze the effect of wages and work environment on employee performance at PT. Rezeki Surya Gasindo. The background of this research is based on the importance of decent wages and a conducive work environment as factors that can enhance employee productivity and work quality. This study employs a quantitative method with a causal associative approach. Data were obtained through the distribution of questionnaires to all employees of PT. Rezeki Surya Gasindo and measured using a Likert scale. The data analysis techniques used in this study include validity and reliability tests, R-square test, and hypothesis testing through bootstrapping with SmartPLS 3 software. The results indicate that wages have a positive and significant effect on employee performance, and similarly, the work environment also has a positive and significant effect on employee performance. Simultaneously, wages and work environment are proven to have a significant effect on employee performance. These findings suggest that the company should pay attention to providing appropriate wages and creating a comfortable work environment in order to optimize employee performance.

The purpose of this study is to examine the effect of Good Corporate Governance (GCG), financial performance, and Earning Per Share (EPS) on firm value. The object of this research consists of state-owned enterprises (SOEs) listed on the Indonesia Stock Exchange during the period of 2021–2024. This study employs a quantitative approach using secondary data in the form of annual financial statements as the primary source. The sample was selected using purposive sampling based on predetermined criteria, ensuring that only companies with complete data and consistent reporting were included in the analysis. The independent variables analyzed include the audit committee, independent commissioners, institutional ownership, Return on Assets (ROA), and Earning Per Share (EPS). Multiple linear regression analysis was used to process the data in this study, allowing the researchers to examine the simultaneous and partial effects of the variables on firm value. The findings indicate that firm value is significantly influenced by financial performance, particularly ROA, highlighting the importance of operational efficiency and profitability in enhancing shareholder wealth. While certain GCG variables such as institutional ownership showed positive influence, other elements like audit committees and independent commissioners produced mixed results, suggesting that governance mechanisms may have varying effects depending on organizational context. Meanwhile, EPS demonstrated inconsistent results in relation to firm value, implying that market perceptions of earnings may not fully capture the impact on overall firm valuation. This study provides insights for policymakers, investors, and corporate managers on the relative importance of governance and financial indicators in value creation for state-owned enterprises.

This study examines the influence of work discipline, employee motivation, and work environment on employee performance at the Office of Women Empowerment, Child Protection, Population Control, and Family Planning (DP3AP2KB) of Batam City. The research applies a quantitative approach with an associative design to determine the relationship and influence among variables. Data were collected from all employees using a structured questionnaire that had been tested for validity and reliability to ensure the accuracy of the research instrument. The analysis technique used is multiple linear regression to evaluate both partial and simultaneous effects of the independent variables on employee performance. The findings indicate that work discipline, motivation, and work environment collectively contribute significantly to improving employee performance. Individually, each variable also shows a meaningful and positive effect, reflecting the importance of both internal and external factors in shaping employee outcomes and productivity. These results imply that strengthening discipline, enhancing employee motivation, and creating a supportive, comfortable, and conducive work environment are essential strategies for improving organizational performance and achieving institutional goals effectively. Therefore, organizations are encouraged to implement policies and programs that support employee development, maintain a positive work atmosphere, and improve overall work quality in a sustainable manner.

This article examines the complex interactions between the military and civil society in regions affected by conflict, approached from a sociological-legal perspective. The study seeks to understand how military power influences civil society, the ways in which legal and social legitimacy are established, and how local communities respond to military presence and control. Employing a qualitative case-study methodology, the research utilized in-depth interviews with community members and military personnel, extensive field observations, and detailed analysis of relevant legal documents. The findings reveal that military power structures significantly impact civil rights, security, and social dynamics, with interactions ranging from cooperative engagement to open conflict. Furthermore, the legitimacy of military actions is closely linked to factors such as operational performance, transparency, accountability, and social acceptance by local populations. Based on these insights, the article argues for the importance of improving communication channels between military forces and communities, integrating formal legal frameworks with local norms and practices, and actively involving community members in security and governance programs. These measures are proposed as strategies to enhance the legitimacy of military operations, reduce conflict, and promote long-term social stability. The study contributes to the broader understanding of civil-military relations by highlighting the sociological and legal dimensions of military influence in conflict-affected areas.

This study is motivated by the importance of economic growth as a key indicator of regional development performance and public welfare improvement. Economic growth reflects the ability of local governments to manage resources and implement effective fiscal policies. This research aims to analyze the development and the influence of Local Own-Source Revenue, General Allocation Fund, and Special Allocation Fund on the economic growth of regencies/cities in Jambi Province during the period 2020–2024. The study employs a quantitative approach combined with descriptive analysis. Secondary data are obtained from official publications of relevant institutions and analyzed using panel data regression with the Common Effect Model approach. The results indicate that Local Own-Source Revenue and the General Allocation Fund have a significant effect on economic growth, showing that the increase in regional revenue and fiscal transfers contributes to economic performance. Meanwhile, the Special Allocation Fund does not have a significant effect, indicating that its allocation may not be optimally utilized in stimulating regional economic activities. These findings imply that strengthening regional fiscal capacity and improving the effectiveness of fund allocation are essential to promote sustainable economic growth. The study also highlights the need for better policy coordination and efficient financial management at the regional level to reduce disparities and enhance development outcomes.  

This study aims to analyze the influence of workforce agility, job satisfaction, and job engagement on employee adaptive performance at PT. MMT Buana Logistik. The dynamic logistics industry requires employees who have high adaptability to technological changes and operational demands. This type of research is quantitative causality with an explanatory approach. The population in this study were all employees of PT. MMT Buana Logistik, totaling 49 people, with the sampling technique used saturated sampling (census). Data were collected through questionnaires with a 5-point Likert scale. Data analysis techniques used included instrument testing (validity and reliability), testing of classical assumptions (normality, multicollinearity, and heteroscedasticity), and multiple linear regression analysis. Hypothesis testing was carried out through t-tests (partial), F-tests (simultaneous) and coefficient of determination (R2). The results showed that workforce agility, job satisfaction, and job engagement partially and simultaneously have a positive and significant influence on employee adaptive performance. These findings imply the importance of managing work flexibility, fulfilling employee satisfaction, and increasing work engagement to build adaptive human resources to support company competitiveness in the logistics industry.  

This study examines customer churn prediction in subscription-based telecommunications from a digital marketing perspective using machine learning. The analysis utilizes a secondary dataset of 7,043 customer records that simulate behavioral, contractual, and financial attributes commonly found in telecom services. Three classification algorithms Logistic Regression, Random Forest, and Gradient Boosting are applied to model churn behavior. Data preprocessing includes handling missing values, encoding categorical variables, and splitting data into training and testing sets. Model performance is evaluated using accuracy, recall, and ROC-AUC, with emphasis on recall due to its importance in identifying at-risk customers. The results show that Gradient Boosting achieves the highest overall performance with an ROC-AUC of 0.84, while Logistic Regression provides relatively higher recall. Key drivers of churn include short-term contracts, higher monthly charges, and lower service engagement. However, recall remains moderate, indicating limitations in capturing complex behavioral factors. These findings suggest the need to combine predictive models with behavioral insights and highlight the importance of early customer engagement and long-term retention strategies.

This research is motivated by the importance of strengthening a professional public service culture through the integration of public administration values, norms, and ethics. Although various regulations and codes of ethics have been established in public organizations, gaps remain between formal norms and service practices in the field, such as low levels of responsiveness and professionalism among civil servants. This study aims to analyze the relationship between values, norms, and ethics in shaping public service culture, and to examine the role of organizational ethics and codes of ethics in strengthening public administration professionalism. The research employed a qualitative approach with a descriptive-analytical design through a literature review of relevant journals, books, and policy documents. Content analysis techniques were used to identify patterns of relationships between concepts. The results indicate that the internalization of ethical values ​​such as integrity, accountability, and transparency significantly influences the quality of public services. An organizational code of ethics serves as a formal instrument that clarifies standards of conduct for civil servants and strengthens a professional work culture. Consequently, public organizations need to strengthen ethics training, oversight mechanisms, and the integration of ethical values ​​into performance evaluation systems to build responsive and public-interest-oriented services.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

The issue of uninhabitable houses still requires an accurate identification mechanism because the manual data collection process has the potential to be time-consuming, costly, and subject to subjectivity in determining aid priorities. This study aims to develop a classification model to identify habitable and uninhabitable houses based on family socioeconomic data using the Random Forest algorithm. The research method includes data preprocessing, data division using stratified split in three scenarios, baseline model development, and optimization through hyperparameter tuning using GridSearchCV with 3-fold cross-validation and balanced class_weight parameters. The data used includes variables such as education type, employment status, occupation type, number of family members, and family insurance type. The test results show that the 70:30 data division scenario after tuning provides the best performance with a recall value of 0.5797 for uninhabitable houses and an F1-score of 0.4746. Feature importance analysis shows that education type and employment status are the most influential variables in the classification. The results of this study show that the model built is capable of increasing sensitivity in detecting uninhabitable houses to support more objective field survey prioritization.

This study aims to evaluate the quality of public services at the Department of Population and Civil Registration of West Sumba Regency following the implementation of digitalization through the use of electronic signatures. A quantitative approach was employed, with data collected through questionnaires distributed to 100 respondents. Data analysis was conducted using the Importance Performance Analysis (IPA) method to identify gaps between the level of importance and service performance. The results indicate that, overall, service quality can be categorized as fairly good, as reflected by performance levels that are relatively close to public expectations. However, several service attributes remain in the priority quadrant, particularly in terms of system usability, service stability, and accessibility. Meanwhile, attributes such as service speed, data security, and staff responsiveness demonstrate satisfactory performance and should be maintained. These findings suggest that the implementation of electronic signatures has improved service efficiency and effectiveness, although further improvements are still required, particularly in technical aspects and system usability. Therefore, continuous improvement efforts are necessary to enhance public service quality, making it more responsive and oriented toward user satisfaction.

This study aimed to analyze the relationship between nurses’ interpersonal conflict and job satisfaction at Hospital X Depok in 2025. Interpersonal conflict in the workplace may negatively affect nurses’ psychological well-being, performance, and quality of healthcare services, thereby reducing job satisfaction. This research employed a correlational analytic design with a cross-sectional approach. The study population consisted of all nurses working at Hospital X Depok, totaling 151 individuals, with a final sample of 121 respondents selected using purposive sampling. Data were collected using three instruments: a demographic questionnaire, an interpersonal conflict questionnaire based on Wexley’s theory, and the Minnesota Satisfaction Questionnaire (MSQ) short form to measure job satisfaction. Data analysis was conducted using univariate statistics and the Chi-Square test with a significance level of 0.05. The results showed that 55.4% of respondents experienced low interpersonal conflict, while 44.6% experienced high interpersonal conflict. Job satisfaction levels were relatively balanced, with 50.4% of nurses reporting high satisfaction and 49.6% reporting low satisfaction. The Chi-Square test indicated a significant relationship between interpersonal conflict and job satisfaction (p = 0.000). Nurses experiencing low interpersonal conflict tended to have higher job satisfaction, whereas those experiencing high interpersonal conflict predominantly reported low satisfaction. These findings highlight the importance of effective conflict management strategies, supportive leadership, and strengthened interpersonal communication to enhance nurses’ job satisfaction and improve the quality of healthcare services.

Tourism regulations play a critical role in shaping destination competitiveness and influencing tourist travel decisions, yet empirical evidence on their effectiveness across different institutional contexts remains limited. This study investigates the impact of tourism regulations on tourist visit performance through a comparative quantitative analysis of Indonesia and Singapore. Using panel data covering the period 2010–2023, this research employs a fixed-effects regression model to examine the relationship between regulatory quality and tourist arrivals, while controlling for economic conditions and transport infrastructure. The findings reveal that tourism regulations have a positive and statistically significant effect on tourist visit performance in both countries. However, the magnitude of the effect is substantially stronger in Singapore, indicating higher regulatory efficiency and institutional effectiveness in translating policy interventions into tourism demand. These results support institutional and tourism demand theories, emphasizing that regulatory quality, rather than regulatory volume, is a key determinant of tourism performance. The study contributes to the tourism policy literature by providing cross-national empirical evidence on regulatory impacts and highlighting the importance of governance capacity in tourism development. From a policy perspective, the findings suggest that countries should prioritize regulatory coherence, administrative efficiency, and adaptive governance to enhance tourism competitiveness and resilience.

This study aims to analyze the implementation of bureaucratic reform in improve the administrative performance of the Sorong City Regional Secretariat. The research uses a mixed-methods approach, combining quantitative and qualitative methods. Quantitative data were obtained by distributing questionnaires to 30 government apparatus respondents using the Likert scale and were analyzed using descriptive statistics and correlation analysis. Meanwhile, qualitative data were obtained through in-depth interviews with key informants and analyzed using thematic analysis and data triangulation. The results of the study show that the implementation of bureaucratic reform is positively related to the organization's administrative performance. The variables of policy communication, apparatus resources, and bureaucratic structure show a fairly strong correlation with administrative performance, with bureaucratic structure exhibiting the strongest relationship. Qualitative findings also show that bureaucratic reform has been carried out through simplifying procedures, updating SOPs, digitizing administration, and strengthening the performance reporting system. Theoretically, this research strengthens policy implementation theory and public organizational performance theory, which emphasize the importance of policy communication, human resource capacity, and the effectiveness of organizational structures in improving bureaucratic performance. However, this study has limitations in scope, as it is confined to a single organizational unit and a relatively small number of respondents. Therefore, further research is suggested to expand the research object in several regional apparatus organizations in order to obtain a more comprehensive picture of the implementation of bureaucratic reform at the local government level.

This study examines the influence of earnings quality and dividend policy on firm value at PT Alkindo Naratama Tbk during the period 2014–2024. Firm value is measured using the Price to Book Value (PBV), earnings quality is proxied by the ratio of operating cash flow to net income (QOE), and dividend policy is measured using the Dividend Payout Ratio (DPR). This research adopts a quantitative approach with an associative causal design using secondary data obtained from the company’s quarterly financial reports over eleven years, resulting in 44 observations. The analysis method applied is multiple linear regression. The findings reveal that earnings quality has a positive and significant impact on firm value. Dividend policy also shows a positive and significant effect on firm value. Simultaneously, earnings quality and dividend policy significantly influence firm value. The coefficient of determination indicates that a large proportion of firm value variation can be explained by these two variables. These results support signaling theory, which suggests that high earnings quality and stable dividend distribution provide positive signals to investors and increase market confidence in the company. The study contributes to financial management literature by highlighting the importance of financial performance indicators in determining firm value.