Publication Search

Ship berthing and departure planning is one of the crucial aspects in supporting the operational effectiveness of shipping companies. This study aims to analyze the important role of planners in ship berthing and departure activities in supporting marketing activities at PT. Nusantara Pasifik Sentosa Belawan. The method used in this research is a descriptive qualitative approach through observation and documentation study. The findings indicate that well-planned and coordinated ship berthing and departure activities provide a significant contribution to customer satisfaction, delivery punctuality, and the company’s image in the eyes of business partners. Effective planning also helps the company maintain service reliability and improve operational efficiency in port activities. However, several obstacles were identified, including lack of coordination between departments, limited information flow, and schedule disruptions caused by external factors such as weather conditions and port congestion. These challenges can affect the smoothness of operational activities and indirectly influence the company’s marketing performance. Therefore, strategies are required to improve human resource competencies, enhance digital communication systems, and strengthen cross-sector collaboration. With a well-structured planning system, PT.Nusantara Pasifik Sentosa is expected to improve its service quality and increase its competitiveness in the national shipping industry.

Digital transformation in healthcare services is a strategic approach to improve access, efficiency, and service quality, particularly within Indonesia’s National Health Insurance (JKN) system. BPJS Kesehatan has introduced an online queue feature through the Mobile JKN application to minimize manual queuing and reduce waiting times in outpatient services. However, despite the widespread ownership of the application, its actual utilization for online queuing remains relatively low, including in a regional public hospital (RSUD) in West Bandung. This condition reflects a gap between the availability of digital health technology and patients’ actual usage behavior, highlighting the need to examine factors influencing adoption.This study aims to analyze the effects of perceived ease of use, social influence, and facilitating conditions on the actual use of the Mobile JKN online queue, with behavioral intention as an intervening variable among outpatients. A quantitative cross-sectional design was applied, involving 255 JKN outpatient participants selected through purposive sampling. Data were collected using structured questionnaires and analyzed using Structural Equation Modeling (SEM) with AMOS version 24, based on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Data analysis included descriptive statistics, validity and reliability testing, normality assessment, goodness-of-fit evaluation, Three Box Method, and hypothesis testing.

This study was conducted with the aim of identifying and analyzing the influence of price, innovation, and service quality variables on consumer decisions in using the Maxim online transportation service. The background of this research is the increasing competition in the online transportation industry in Indonesia, which encourages companies to continuously improve their marketing strategies to attract and retain customers. As one of the rapidly growing online transportation service providers, Maxim needs to understand the factors that influence consumer decisions in order to compete effectively. The method used in this research is a quantitative approach, with data collected through questionnaires distributed to 134 respondents who are active users of Maxim's transportation services. The data collected were analyzed using multiple linear regression to examine the effect of each independent variable (price, innovation, and service quality) on the dependent variable (consumer decision). Validity and reliability tests were also conducted to ensure the research instrument was appropriate for use.The results of the study show that all three independent variables—price, innovation, and service quality—have a positive and significant effect on consumer decisions. This means that the more competitive the price offered, the higher the level of innovation provided, and the better the perceived service quality, the more likely consumers are to choose and continue using the Maxim online transportation service.These findings provide practical implications for Maxim’s management in designing more targeted marketing strategies and service improvements, as well as strengthening the company’s position in facing market competition.

This study aims to examine the effect of Islamic Service Quality comprising compliance, assurance, reliability, tangibles, empathy, and responsiveness on repurchase intention through consumer satisfaction at the Musa’adah Kopontren Retail, Sukorejo Situbondo. This research employs an explanatory research design. The sampling technique used is non-probability sampling with a purposive sampling method. Data were collected a questionnaire distributed both directly and via Google Forms, involving a total of 240 respondents. The data analysis method applied is Partial Least Squares (PLS) using SmartPLS software. The results indicate that: (1) compliance has a significant effect on consumer satisfaction; (2) assurance has a significant effect on consumer satisfaction; (3) reliability has no significant effect on consumer satisfaction; (4) tangibles have a significant effect on consumer satisfaction; (5) empathy has a significant effect on consumer satisfaction; (6) responsiveness has a significant effect on consumer satisfaction; (7) compliance has no significant effect on repurchase intention; (8) assurance has no significant effect on repurchase intention; (9) reliability has no significant effect on repurchase intention; (10) tangibles have a significant effect on repurchase intention; (11) empathy has no significant effect on repurchase intention; (12) responsiveness has no significant effect on repurchase intention; and (13) consumer satisfaction has a significant effect on repurchase intention.

This article analyzes the role of Work-Life Balance, Motivation, and Work Environment on Employee Performance in the manufacturing industry. Work-Life Balance (WLB) can be measured on time management, mental health, job satisfaction, and overall performance. Work Motivation is seen from intrinsic and extrinsic factors, while the Work Environment is seen from a conducive, safe, and comfortable atmosphere. This study uses a quantitative approach using SmartPLS. This method was chosen because of its effectiveness in testing complex causal models with many variables and its flexibility assuming data distribution. Primary data was collected from 98 employees via Google from Likert scale, using the seneus technique. Analysis includes evaluating measurement models for validity and reliability and testing hypotheses. The SEM-PLS analysis confirms that Work-Life Balance, Motivation, and Work Environment all have a positive and significant impact on employee performance. WLB showed the most dominant influence among the three factors. This research is an important factor that significantly affects employee performance. Maintaining and improving these factors, especially WLB, is essential for fostering employee motivation, a sense of security, and maximizing work output, thereby contributing to the overall success of the organization.

Background: Trigeminal neuralgia (TN) is often associated with neurovascular compression in the trigeminal nerve root entry zone, necessitating the simultaneous visualisation of nerves and blood vessels. Fusion of 3D SPACE and 3D TOF MRA images provides an integrated neurovascular view; however, not all hospitals have fusion software. This study developed a MATLAB-based image fusion method as an alternative and evaluated its equivalence to hospital-based fusion software.Methods: This study employed a descriptive quantitative research design, conducted in November 2025 at Diponegoro National Hospital and Dr Kariadi General Hospital in Semarang. A total of 16 brain MRI datasets (3D SPACE and 3D TOF MRA) were fused using hospital software and the MATLAB fusion application (MATLAB R2025b GUI). The fusion results were assessed by specialist radiologists. Diagnostic performance metrics (sensitivity, specificity, NDP, NDN, accuracy) were calculated, and paired differences were tested using the McNemar test. Intra-observer reliability was assessed using percentage agreement and Cohen’s Kappa.Results: MATLAB fusion yielded a sensitivity of 90.91%, specificity of 80.00%, NDP of 90.91%, NDN of 80.00%, and accuracy of 87.50%; the McNemar test (p=1.000) indicated no significant difference. Intra-observer reliability was very good (percent agreement 94%; Kappa 0.875). These findings indicate that MATLAB-based fusion is equivalent to hospital software fusion on the study data and has the potential to serve as an alternative in facilities without fusion software, provided that registration standardisation and user training are in place.

This study aims to design and develop a functionally efficient and operationally effective Internet of Things (IoT)-based air quality monitoring system for radiology departments. The system utilises a DHT22 sensor integrated with an ESP32 microcontroller to monitor the temperature and humidity of diagnostic rooms in real time, and to display the data via the UdaraKu mobile application. The research method employed a quantitative experimental approach focused on measuring system performance, specifically the accuracy of the temperature and humidity sensors. The research model used was the Research and Development (R&D) method, aimed at transforming conventional air quality monitoring in radiology into a real-time digital system based on IoT. The research results indicate that the IoT-based monitoring system is capable of maintaining room temperature and humidity stability within the ideal range, namely 22–24°C and 50–60% RH, in accordance with international standards. This improvement in environmental stability has a direct impact on reducing noise in digital radiography images, as evidenced by an increase in the Signal-to-Noise Ratio (SNR). Instrument validation demonstrated a high level of reliability with a Cronbach’s Alpha value of 0.848, reinforcing the reliability of the data and the system. Overall, the IoT-based air quality monitoring system has proven effective in controlling noise in digital radiography images, improving the quality of diagnostic services, and supporting patient safety principles and operational efficiency within radiology departments.

Cine Crib is a YouTube channel that routinely reviews both international and Indonesian films, with a primary focus on Indonesian cinema. The review content, presented in a relaxed, informative, and entertaining manner, possesses significant potential to influence audience decisions to watch films in cinemas. In the digital era, social media platforms, including YouTube, have emerged as primary information sources that play a vital role in shaping consumer perceptions, preferences, and behaviors concerning movie-watching interest, particularly in theaters. This study aims to determine the influence and the magnitude of the impact of review content on the Cine Crib YouTube channel toward the interest in watching films in cinemas. The research employs a quantitative approach utilizing a survey method. Data were collected by distributing questionnaires to 100 respondents consisting of Cine Crib channel subscribers. Data analysis includes validity and reliability tests, simple linear regression, t-tests, F-tests, and the calculation of the coefficient of determination ($R^2$). Research findings indicate that film review content on YouTube has a positive and significant influence on the interest in watching films in cinemas, albeit with a weak correlation. Variations in watching interest are influenced by film review content, while the remaining factors are influenced by variables outside the scope of this study. Consequently, the Cine Crib channel represents an effective strategy for increasing interest in cinema-going.

This study investigates the impact of social support and learning motivation on students' desire to pursue higher education at SMA Negeri 4 in South Tangerang City. It employs a quantitative design through survey methods, collecting data via a Google Form questionnaire from 84 participants, with instruments validated and tested for reliability using SPSS version 27. Analysis involved simple and multiple linear regression, supplemented by t-tests for individual effects and F-tests for combined influences. Social support (X1) significantly affects interest in further education (Y), showing a regression coefficient of 1.521 (p = 0.000) and R² of 0.649. Learning motivation (X2) likewise exerts a strong positive influence on interest in higher education (Y), with a coefficient of 1.307 (p = 0.000) and R² of 0.691. Together, these factors significantly predict students' interest (F = 106.418, p = 0.000), explaining 72.4% of the variance (multiple R² = 0.724), while 27.6% stems from unexamined variables. These results highlight the critical role of social support and robust learning motivation in boosting students' aspirations for advanced studies. Schools, families, and policymakers can leverage these insights to craft targeted initiatives for enhancing postsecondary enrollment. These results highlight the critical role of social support and robust learning motivation in boosting students' aspirations for advanced studies. Schools, families, and policymakers can leverage these insights to craft targeted initiatives for enhancing postsecondary enrollment.

This study aims to describe the event management of Kartini Humas Indonesia 2024 organized by Media Humas Indonesia as a form of appreciation for outstanding female public relations practitioners and as a strategy to build the reputation of Media Humas Indonesia. This research uses a descriptive qualitative method with a case study approach. Data were collected through in-depth interviews with four informants, non-participant observation, and documentation. The results show that the Kartini Humas Indonesia 2024 event was managed using Goldblatt’s five-stage event management approach: research, design, planning, coordination, and evaluation. The research stage involved identifying the needs and expectations of participants and target audiences. In the design stage, the committee developed creative ideas and defined the event’s characteristics. Planning focused on selecting the theme, location, and timing. In the coordination stage, communication and teamwork were key to the event’s successful execution. Evaluation was conducted through internal reflection and participant feedback. Furthermore, the event had a positive impact on the reputation of Media Humas Indonesia as the organizer, particularly in terms of reliability, credibility, and social responsibility. The study concludes that structured event management plays a crucial role in enhancing the reputation of the media as an event organizer in the public eye.

Purpose: This study examines the effect of service communication on usage intention of Holistic Homecare for Elderly services, with customer trust serving as a mediating variable. Design/Methodology/Approach: Employing a quantitative explanatory approach with cross-sectional survey design, this research involved 120 family respondents with elderly members (≥60 years) in urban areas of Jakarta and Bandung, selected through stratified purposive sampling. Data were collected using structured questionnaires with 5-point Likert scales that had been tested for validity (Pearson correlation > 0.30) and reliability (Cronbach's Alpha > 0.70). Data analysis utilized Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0 to examine measurement and structural models, along with bootstrapping mediation tests. Findings: Service communication demonstrated significant positive effects on customer trust (β = 0.712; t = 12.456; p < 0.001) and usage intention (β = 0.324; t = 4.213; p < 0.001). Customer trust significantly influenced usage intention (β = 0.498; t = 6.847; p < 0.001) and functioned as a partial mediator (indirect effect = 0.354; BootCI [0.248; 0.471]). Originality/Value: This research integrates Commitment-Trust Theory and Social Support Theory within the context of home-based elderly care services in a developing country, revealing the critical role of empathetic, responsive, and transparent communication as the foundation for trust-building that subsequently drives service adoption. Practical Implications: The findings emphasize the necessity of therapeutic communication training for caregivers and information transparency to establish a trustworthy homecare service ecosystem in Indonesia.

The Foot-and-Mouth Disease (FMD) outbreak has inflicted severe multidimensional disruptions on dairy farming communities, yet limited research has systematically examined how social, economic, and psychological impacts collectively shape farmer welfare in post-outbreak contexts, particularly in developing countries. This study aims to investigate the simultaneous effects of social, economic, and psychological impacts on the welfare of dairy farmers following the FMD outbreak in Indonesia, and to extend theoretical frameworks of welfare and resilience to the context of livestock disease crises. A quantitative survey was conducted with 150 dairy farmers in Pujon District, Malang Regency—one of the areas most severely affected by the 2022 FMD outbreak. Data were collected using structured questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The measurement model results was assessed for reliability and validity, while the structural model tested the hypothesized relationships. All three impact dimensions demonstrated significant positive effects on farmer welfare: social impact (β = 0.640, t = 4.431, p < 0.001), economic impact (β = 0.635, t = 4.487, p < 0.001), and psychological impact (β = 0.647, t = 4.143, p < 0.001). The model explained 95.5% of the variance in farmer welfare (R² adjusted = 0.955), indicating exceptional explanatory power. Factor loadings revealed that mutual assistance (0.990), loss of assets (0.986), and business insecurity (0.981) were the strongest indicators of social, economic, and psychological impacts, respectively. Farmer welfare in post-FMD contexts emerges from the synergistic interplay of social solidarity, economic pragmatism, and psychological resilience. The findings extend welfare utility and post-traumatic growth theories to livestock disease settings and provide evidence-based foundations for integrated post-disaster interventions targeting smallholder farmers in developing countries.

This research investigates integration models combining navigation and port information systems through blockchain technology to enhance ship operational reliability, addressing critical challenges in data integrity, information sharing, and system coordination affecting maritime operations. Current maritime information systems operate in silos with limited interoperability, creating information asymmetries, coordination inefficiencies, and data integrity concerns that compromise operational decision-making and safety management. Through qualitative analysis involving ship operators, port authorities, navigation system providers, blockchain specialists, and maritime regulators, this study examines how distributed ledger technology can enable secure, transparent, and tamper-proof information integration across navigation and port systems. Results demonstrate that blockchain-based integration can improve data integrity assurance by 70-90%, reduce coordination time by 40-60%, enhance operational transparency by 55-75%, and decrease documentation errors by 50-70% through immutable records, smart contracts, and decentralized consensus mechanisms. Key implementation challenges include technical complexity, computational requirements, regulatory uncertainty, organizational adoption resistance, and scalability limitations. Findings reveal that blockchain represents transformative enabling technology for maritime digital ecosystems requiring trusted information exchange among multiple stakeholders without centralized intermediaries. This research contributes to maritime information systems literature by providing practical frameworks for blockchain implementation supporting operational reliability enhancement.

This study aims to determine the influence of Social Media Marketing, Influencer Marketing, and Location on Generation Z Customer Loyalty at Kedai Wanus Kediri. This research is quantitative. Data were obtained through two methods: primary and secondary data. The population in this study were Generation Z consumers at Kedai Wanus Kediri. The sample in this study was 100 consumers, with accidental sampling as the sampling technique. The analysis techniques used in this study included validity testing, reliability testing, classical assumption testing, multiple linear regression analysis, and hypothesis testing. The results of the partial t-test indicate that the variables Social Media Marketing, Influencer Marketing, and Location have a positive and significant effect on Generation Z Customer Loyalty at Kedai Wanus Kediri. The results of the simultaneous F-test indicate that Social Media Marketing, Influencer Marketing, and Location have a positive and significant effect on Generation Z Customer Loyalty at Kedai Wanus Kediri.

This study aims to analyze the influence of the implementation of the Internal Quality Assurance System (SPMI) and principal leadership on the quality of educational services in South Konawe Regency, Southeast Sulawesi. The research employed a quantitative descriptive approach with a survey design to obtain an empirical overview of the relationships among the variables examined. The population consisted of all principals at the elementary, junior high, senior high, and vocational high school levels, totaling 70 respondents. The sampling technique used purposive sampling based on specific criteria relevant to the research objectives. Data were collected through questionnaires that had been tested for validity and reliability to ensure the accuracy and consistency of the research instrument. Data analysis was conducted using multiple linear regression with the assistance of SPSS version 25.0. Prior to hypothesis testing, classical assumption tests were performed, including normality, multicollinearity, and heteroscedasticity tests, to ensure the feasibility of the regression model. Furthermore, the F-test was conducted to examine the model’s overall significance, while the t-test was used to determine the partial effect of each independent variable on the dependent variable. The results indicate that the implementation of SPMI and principal leadership have a positive and significant effect on the quality of educational services. These findings highlight the importance of strengthening managerial capacity and internal commitment in implementing SPMI, as well as enhancing principal leadership quality to continuously improve educational service standards and overall education quality.

This study investigates the role of artificial intelligence (AI) in enhancing language learning, with a focus on five key applications: automatic text analysis, personalized learning, adaptive feedback, language error detection, and automatic translation. The study addresses the challenge of integrating AI effectively in educational contexts while balancing technological potential with pedagogical guidance. The objective is to provide a comprehensive understanding of how AI tools contribute to more adaptive, efficient, and engaging language learning experiences. A systematic literature review method was employed, selecting and critically analyzing studies published between 2020 and 2025 that examined AI-assisted language learning strategies. The findings indicate that automatic text analysis supports comprehension monitoring and guided learning, while personalized learning adapts content to individual learner needs, enhancing motivation and retention. Adaptive feedback delivers immediate, targeted guidance that fosters accuracy and self-regulated learning, and language error detection tools enable learners to identify and correct grammatical and lexical mistakes, promoting metalinguistic awareness. Automatic translation broadens access to authentic texts and cross-cultural materials, supporting comprehension and independent learning. Synthesizing these findings highlights the transformative potential of AI to improve learning outcomes while also revealing challenges such as tool reliability, ethical considerations, and the need for teacher oversight. The study concludes that AI, when thoughtfully integrated, complements instruction, enhances learner engagement, and supports differentiated and data-driven teaching strategies, providing valuable insights for language educators and guiding future research on AI-enabled language learning.

This study aims to analyze the role of Islamic Religious Counselors as street-level bureaucrats in implementing Islamic Community Guidance policies in Batang Regency. The study used a mixed-methods approach with a sequential explanatory design, beginning with the collection and analysis of quantitative data and then deepening with qualitative data. Quantitative data were obtained from 53 respondents using a questionnaire that was tested for validity and reliability. In contrast, qualitative data were collected through in-depth interviews, observations, and documentation with religious counselors, Ministry of Religious Affairs officials, the KUA (Office of Religious Affairs), and the community. Data analysis was conducted descriptively and analytically using Michael Lipsky's street-level bureaucracy theory as an analytical framework. The study's results indicate that the role of Islamic Religious Extension Workers as street-level bureaucrats is positively and strongly associated with the successful implementation of the Islamic Community Guidance policy (r = 0.826; p < 0.01). Qualitative findings revealed that extension workers exercise discretion in the form of community assistance, adjustments to extension methods, strategic flexibility, and responsiveness to socio-religious issues at the local level. This study confirms that the effectiveness of Islamic Community Guidance policies is not determined solely by formal policy design but is highly dependent on the capacity, flexibility, and discretion of Islamic Religious Counselors, the implementing actors at the field level. These findings provide theoretical contributions to the development of street-level bureaucracy studies in the context of religious policy, as well as practical implications for formulating more contextual and responsive policies.

This study is developed as a conceptual paper that analyzes halal marketing practices through a maqashid shariah lens across three operational domains: digital marketing, customer service and business administration. The paper is motivated by recent developments in the global halal economy, the stronger linkage between halal assurance, governance quality and digital transparency, and Indonesia's policy acceleration toward mandatory halal implementation in October 2026. Methodologically, the study uses a structured literature review and conceptual synthesis of international reputable journals and relevant Indonesian journals, complemented by policy and industry reports. The analysis maps prior findings into maqashid dimensions (protection of religion, life, intellect, wealth and dignity) and translates them into practical indicators of honesty, transparency, fairness, consumer protection and procedural reliability. The paper argues that credible halal marketing depends not only on promotional messages but also on service conduct and disciplined administrative governance. It contributes a conceptual framework and practical propositions for evaluating halal marketing practices in MSMEs and service businesses.

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.

Inconsistent terminology across cybersecurity frameworks undermines global governance and interoperability. The National Institute of Standards and Technology Cybersecurity Framework (NIST CSF 2.0) and ISO/IEC 27001:2022 share similar objectives but diverge semantically in defining risk, control, and resilience. This semantic gap causes difficulties in compliance mapping and automated policy translation. Research Objectives: This study aims to analyze the semantic similarity and divergence between NIST and ISO/IEC 27000 terminologies, identify conceptual structures influencing interoperability, and propose an AI-assisted foundation for harmonizing cybersecurity language globally. Methodology: A mixed-method semantic comparative design integrates Natural Language Processing (NLP) and ontology mapping. Using the nist_glossary.csv dataset and ISO vocabularies, terms were normalized and analyzed via cosine similarity using sentence-transformer embeddings. Ontological alignment was visualized through the Semantic Threat Graph (STG) and validated by certified experts using Cohen’s Kappa reliability tests. Results: From 672 term pairs, results show 40.9% high semantic equivalence, 38.8% partial overlap, and 20.3% semantic divergence. Strongest alignment appears in “Protect” and “Identify” domains, while divergences occur in governance and recovery-related terms. Ontology mapping revealed three conceptual clusters—Risk Governance, Technical Safeguards, and Organizational Readiness. Conclusions: Findings confirm a 79.7% total semantic alignment, indicating strong potential for harmonizing global cybersecurity standards. The study contributes an empirical model combining computational linguistics and AI-based ontology mapping to establish semantic interoperability, enabling unified cybersecurity governance and AI-driven compliance automation. Keywords: Semantic Interoperability; Ontology Mapping; Cybersecurity Frameworks; Terminology Alignment; AI Harmonization