Publication Search

The rapid development of Artificial Intelligence (AI) has brought significant changes to the criminal justice system, particularly in criminal investigations and evidentiary processes, while simultaneously raising complex legal and ethical challenges. Objective: This study aims to analyze the legal implications of the use of AI in criminal investigations, focusing on its benefits, risks, and challenges related to the admissibility of AI-based evidence, as well as the need for regulatory frameworks that ensure fairness, transparency, and accountability. Methods: This research employs a normative qualitative approach through the analysis of legal regulations, a review of legal and technological literature, and a comparative approach across jurisdictions, complemented by case studies of AI applications in law enforcement practices. Results: The findings indicate that AI enhances investigative efficiency through data analysis, crime prediction, and digital forensics; however, it also poses risks such as algorithmic bias, human rights violations, and issues concerning the reliability and transparency of evidence. Furthermore, differences across legal systems result in the absence of uniform standards for the admissibility of AI-based evidence. Therefore, adaptive regulatory frameworks grounded in the principles of fairness, transparency, and accountability are required, along with strengthened human oversight to ensure that the use of AI aligns with the principles of justice and human rights protection.

This study aims to analyze the effect of Brand Image, Product Quality, and Promotion on Purchase Decisions of Crocs sandals in Batam City. The research employs a quantitative approach using a survey method. Data were collected through questionnaires distributed to 119 respondents who are consumers of Crocs products in Batam City. The data analysis techniques include validity and reliability tests, classical assumption tests, multiple linear regression analysis, t-test, F-test, and coefficient of determination analysis, using SPSS version 26. The results indicate that Brand Image and Product Quality have a positive and significant effect on Purchase Decisions. Meanwhile, Promotion shows a positive but insignificant effect on Purchase Decisions. Simultaneously, Brand Image, Product Quality, and Promotion have a positive and significant effect on Purchase Decisions. The Adjusted R Square value of 0.905 indicates that 90.5% of the variation in Purchase Decisions can be explained by the three independent variables, while the remaining 9.5% is influenced by other factors not examined in this study. This research is expected to provide insights for companies in formulating more effective marketing strategies, particularly in strengthening brand image and improving product quality.

This study aims to examine the factors that influence consumer purchasing decisions for Nacific products in Batam City, with an emphasis on the role of digital marketing, E-WOM, and brand image. The research approach used is descriptive quantitative involving 204 respondents who have purchased Nacific products, selected through purposive sampling techniques based on Jacob Cohen's calculations to ensure the adequacy and representativeness of the sample. Data collection was carried out using a structured questionnaire, then analyzed through a series of statistical procedures including validity and reliability tests, classical assumption testing, multiple linear regression analysis, and hypothesis testing using t-tests and F-tests. The findings of the regression analysis revealed that the influence of digital marketing on purchasing decisions was recorded at 21.9%, E-WOM at 27.0%, and brand image at 34.0%. The coefficient of determination value of 0.722 confirms that digital marketing, E-WOM, and brand image simultaneously contribute 72.2% in explaining purchasing decisions. The results of the t-test and F-test indicate that digital marketing, e-WOM, and brand image each have a positive and significant influence on purchasing decisions, both partially and simultaneously.

This study is motivated by the growing public demand for reliable and high-quality public transportation services such as the Trans Solo Bus on corridor 4. The purpose of this research is to analyze the influence of Facilities, Innovation, and Service Quality on User Interest, both partially and simultaneously. This study employs a quantitative method with a sample of 100 respondents who are users of the Trans Solo Bus on corridor 4. Data were collected through questionnaires and analyzed using IBM SPSS Statistics through several tests, including validity, reliability, normality, multicollinearity, heteroscedasticity, multiple linear regression, t-test, F-test, and coefficient of determination (R²). The results revealed that Facilities, Innovation, and Service Quality significantly influence User Interest, either individually or simultaneously. These findings imply that improving facilities, implementing continuous innovation, and ensuring high service quality can increase public interest in using Trans Solo Bus services, thus supporting the development of sustainable urban transportation.

The rapid development of AI-assisted programming has encouraged the emergence of vibe coding, an approach to software development in which developers focus more on formulating intentions, contexts, and constraints through prompts rather than writing code manually. However, existing evaluations of AI-generated code still tend to emphasize functional correctness and productivity, and therefore have not fully addressed the relationship between user intent, technical code reliability, and developers’ understanding of the generated artifacts. This study aims to propose a new evaluation method called TD-VCEM (Three-Dimensional Vibe Coding Evaluation Method) to assess vibe coding practices in a more comprehensive and auditable manner. The proposed method consists of three primary dimensions: Intent Alignment to evaluate the conformity of code with prompt requirements, Code Reliability to assess the technical quality of the generated code, and Developer Cognition to measure developers’ understanding of AI-generated code. TD-VCEM is designed through several stages, including prompt decomposition, prompt-to-code traceability matrix, code reliability assessment, and developer cognition evaluation. Each dimension employs indicator-based scoring rubrics normalized on a scale of 0–100, enabling the construction of a Vibe Coding Evaluation Score (VCES). This study does not present empirical experimental results; instead, it offers a methodological framework that can serve as a foundation for evaluating AI-generated code in modern software engineering environments. The proposed TD-VCEM is expected to improve review process transparency, reduce security risks, strengthen software maintainability, and ensure that developers maintain control and understanding of AI-generated code artifacts.

This systematic literature study conducts a comparative analysis of Reliability-Centered Maintenance (RCM) and Total Productive Maintenance (TPM) in manufacturing, and proposes an integrated hybrid model. The research concludes that RCM and TPM are fundamentally distinct methodologies: RCM excels in risk-based, analytical management of complex, high-criticality assets, while TPM is superior for achieving broad-based productivity gains and cultural empowerment through total employee involvement. The effectiveness of each approach is contingent upon factors such as asset characteristics, organizational culture, data availability, and level of automation. To leverage their complementary strengths, the study proposes the Synergistic RCM-TPM Hybrid Model (SRTHM). This framework advocates for a sequential implementation where TPM establishes the foundational operational stability, discipline, and data culture, upon which selective RCM analysis is applied to critical assets for precision risk management. The model incorporates explicit feedback mechanisms to create a continuous organizational learning cycle. The study provides a contingency framework for methodology selection and a practical roadmap for integration, offering theoretical and practical guidance for building more resilient and context-appropriate maintenance systems in modern manufacturing.

This study aims to analyze the influence of influencer marketing, brand image, and e-WOM on purchasing decisions on the Shopee marketplace. A quantitative descriptive approach was used, involving 204 Shopee consumers who had previously purchased specific products. They were selected using purposive sampling based on Jacob Cohen's calculation guidelines to ensure the sample accurately represented the population. Data were collected through a questionnaire, which was then analyzed using validity and reliability tests, classical assumption evaluation, multiple linear regression, and hypothesis testing using t-tests and F-tests. The regression analysis showed that influencer marketing had a 34.8% influence on purchasing decisions, brand image contributed 32.7%, and e-WOM had an influence of 20.0%. Furthermore, the coefficient of determination (R²) showed that these three variables together explained 86.9% of the variation in consumer purchasing decisions. The t- and F-tests confirmed that each variable had a significant and positive influence, both partially and simultaneously, on purchasing decisions on the Shopee marketplace.

This study was conducted to systematically explore the influence of the level of ease of transactions and the quality of e-commerce services on the process of determining consumer purchasing decisions in Batam City. The research framework was built with a descriptive-oriented quantitative approach through the involvement of 100 respondents who actively utilize digital transaction services, which were determined using a purposive sampling technique based on the Lemeshow formulation to ensure the adequacy and representativeness of the sample. Empirical data were collected through a structured questionnaire and then processed using statistical analysis stages that included testing the validity and reliability of the instrument, evaluation of classical assumptions, multiple linear regression modeling, and verification of hypotheses through t-tests and F-tests. The results of data processing revealed that the variable of ease of transactions contributed 18.8% to purchasing decisions, while service quality contributed an influence of 63.5%. The coefficient of determination (R²) reaching 73.3% indicates that both independent variables simultaneously have substantial explanatory power towards variations in consumer purchasing behavior. Both partial and simultaneous testing confirmed a significant influence, thus confirming that simplifying transaction mechanisms and strengthening digital service quality are strategic determinants in driving e-commerce consumer purchasing decisions in Batam City.

This study aims to comprehensively examine the role of brand image, trust, and promotional intensity in shaping purchasing decisions for Maybelline two-way cake cosmetic products in Batam City. The implemented research design adopted a descriptive quantitative approach involving 204 participants who had actual experience in purchasing the product in question. Respondents were determined through purposive sampling technique based on Jacob Cohen's calculations to ensure empirical representativeness of the research sample. Primary data were collected through the distribution of structured questionnaire instruments, then processed and analyzed using hierarchical statistical stages that included testing the validity and reliability of the instrument, verification of classical assumptions, multiple linear regression modeling, and hypothesis testing through partial (t) and simultaneous (F) tests. Empirical findings indicate that brand image contributes 23.9% to purchasing decisions, trust influences 39.1%, while promotion contributes the most dominant influence at 14.7%. The coefficient of determination (R²) value of 82.9% indicates that the three independent variables are simultaneously able to explain variations in consumer purchasing decisions in a substantial proportion. The results of both partial and simultaneous tests confirm a positive and significant influence between brand image, trust, and promotion on purchasing decisions for Maybelline cosmetics in Batam.

This study aims to elaborate on the determinants of consumer purchasing decisions by emphasizing the role of social media, trust levels, and product quality in the Blibli e-commerce ecosystem in Batam City. The applied methodological framework is based on a descriptive quantitative approach involving 204 respondents with active transaction track records, selected through a purposive sampling technique based on the Jacob Cohen formula to ensure proportionality and validity of sample representation. Empirical data were collected through a structured questionnaire, then processed using comprehensive statistical analysis stages that include instrument validity and reliability testing, classical assumption evaluation, multiple linear regression modeling, and hypothesis testing through partial (t) and simultaneous (F) tests. The analysis results revealed that social media intensity contributed 14.1% to purchasing decisions, trust influenced 36.0%, while product quality influenced 40.4%. The coefficient of determination (R²) value of 86.4% indicates that the three exogenous variables simultaneously have substantial explanatory power towards variations in purchasing decisions. The t-test and F-test findings consistently confirm the significant influence of both individual and collective factors, thus confirming that strengthening digital presence, platform credibility, and consistent product quality are strategic foundations in guiding the preferences and purchasing intentions of Blibli consumers in Batam City.

The implementation of predictive maintenance supported by SAP Plant Maintenance (SAP PM) at PT Xyz has proven to be effective in reducing machine downtime, lowering maintenance costs, and improving asset reliability. The integration of SAP PM with Industry 4.0 technologies such as IoT sensors, AI-based analytics, and real-time notification systems strengthens operational efficiency and ensures continuous performance. Empirical results show improvements in key performance indicators, including a 20-25% reduction in downtime, a 30% reduction in maintenance costs, an increase in asset availability to 97%, an MTBF extension of up to 511 hours, and an OEE rate of 92.1%. These findings highlight the strategic role of digital predictive maintenance in increasing competitiveness and supporting long-term sustainability in manufacturing operations.

This study aimed to examine the influence of promotional strategies and user-friendliness on consumer purchasing decisions on the ShopeeFood platform in Batam City. The research used a descriptive quantitative approach, involving 100 actively transacting respondents, selected through purposive sampling based on Lemeshow's calculation guidelines to ensure sample representativeness. Data were collected through a structured questionnaire and analyzed using a series of validity and reliability tests, classical assumption checks, multiple linear regression, and hypothesis testing using t- and F-tests. The research findings indicated that promotions contributed 42.4% to purchasing decisions, while user-friendliness contributed 26.0%. The coefficient of determination (R²) of 78.9% confirmed that these two variables collectively explained most of the variation in purchasing behavior. The results of the t- and F-tests confirmed that promotions and user-friendliness had a significant influence, both partially and simultaneously, emphasizing the importance of integrating effective digital marketing strategies and an intuitive application interface to drive consumer decisions on ShopeeFood in Batam City.

This study aims to explore the extent to which brand image, product quality, and trust influence purchasing decisions for Pixy two-way cake through the Blibli platform in Batam City. The approach used is quantitative descriptive, involving 100 consumers who previously purchased Pixy two-way cake on Blibli, selected through purposive sampling with Lemeshow calculation guidelines to ensure sample representativeness. Data collection was conducted using a questionnaire, then analyzed through validity and reliability tests, classical assumption evaluation, multiple linear regression, and hypothesis testing using t-tests and F-tests. The results of the regression analysis indicate that brand image has a 24.7% influence on purchasing decisions, product quality contributes 34.0%, and trust plays a role of 29.2%. Furthermore, the coefficient of determination (R²) shows that these three variables collectively explain 74.0% of the variation in purchasing decisions. The t-test and F-test confirm that the influence of each variable is significant and positive, both partially and simultaneously, on consumer purchasing decisions at Blibli in Batam City.

This study aimed to uncover the extent to which eWOM, perceived brand image, and platform ease of use influence consumer purchasing decisions for Skintific moisturizer on Tokopedia. The method employed was descriptive quantitative, selecting a sample of 96 consumers who had purchased Skintific products, using a purposive sampling technique based on the Lemeshow formula to ensure representativeness. Data were collected through a questionnaire and then analyzed using a series of tests including validity, reliability, classical assumption checking, multiple linear regression, and hypothesis testing using t- and F-tests. The analysis showed that eWOM contributed 13.9% to purchasing decisions, brand image 23.4%, and ease of use 46.1%. The coefficient of determination (R²) of 75.4% indicates that these variables collectively explain most of the variation in purchasing decisions. The t- and F-tests confirmed a positive and significant influence, both individually and simultaneously, on consumer purchasing behavior on Tokopedia.

This study aims to analyze the influence of digital leadership, digital transformation, and circular economy practices on the economic performance of weaving SMEs and their contribution to the achievement of the Sustainable Development Goals (SDGs). This study employed a quantitative approach with an explanatory research design. Data were collected from 98 weaving SME owners or managers through questionnaires and analyzed using validity testing, reliability testing, classical assumption tests, and linear regression analysis. The findings show that digital leadership has a positive and significant effect on the economic performance of weaving SMEs. Digital transformation also has a positive and significant effect by expanding market access, improving operational efficiency, and strengthening customer relationships. In addition, circular economy practices have a positive and significant effect through resource efficiency, production waste reduction, and the creation of added value from residual materials. These findings indicate that the integration of digital leadership, digital transformation, and circular economy practices is a strategic approach to enhancing the competitiveness of weaving SMEs while supporting the achievement of the SDGs. This study contributes to the development of cultural-based SME studies by positioning digitalization and sustainability as key foundations for strengthening local economic performance.

This study aimed to examine the extent to which trust, perceived security, and ease of use of the GrabFood platform influence consumer transaction decisions in Batam City. The method used was descriptive quantitative, involving 204 respondents who had made purchases through GrabFood, selected through purposive sampling based on Jacob Cohen's formula to ensure representative results. Data were collected using a questionnaire and analyzed through a series of procedures, including validity and reliability testing, classical assumption checking, multiple linear regression, and hypothesis testing using t-tests and F-tests. The study findings revealed that trust contributed 22.0%, security 38.9%, and ease of use 23.2% to purchasing decisions. The coefficient of determination (R²) of 65.6% confirmed that these three factors simultaneously explained a significant portion of the variation in consumer purchasing behavior. Furthermore, t-tests and F-tests confirmed a positive and significant influence, both individually and collectively, on consumer decisions to use GrabFood.

This study aims to examine the influence of price, brand image, and product innovation on the level of customer loyalty of Honda motorcycles, especially in Purworejo Regency. The method used in this study is a quantitative approach with an explanatory design. Data collection was carried out by distributing questionnaires to 100 respondents with the following requirements: a minimum of 17 years old, domiciled in Purworejo Regency, have used or owned a Honda motorcycle for at least 1 year, and selected using a purposive sampling technique. Then the data obtained were analyzed using multiple linear regression with the help of IBM SPSS Statistics 25. Before being analyzed, validity and reliability tests were conducted to determine the instrument can be used and a classical assumption test was conducted to ensure that the regression model meets statistical requirements. The results of the research analysis simultaneously show that price, brand image, and product innovation have a significant influence on customer loyalty. However, this is different from the partial test which indicates that only brand image has a significant influence in shaping customer loyalty. Brand image was also found to be the most dominant factor in increasing the tendency to repurchase, resistance to competing brands, and positive customer recommendations.

This study analyzes the effect of pastry menu innovation and breakfast service on guest satisfaction at Hotel Indigo Seminyak Bali. The increasing competition in the hospitality industry requires hotels to continuously improve service quality and guest experience. Menu innovation and breakfast service are key factors that directly influence guest satisfaction, particularly through the overall dining experience during guests’ stay. This research used a quantitative descriptive approach with data collected through questionnaires, observation, and documentation from 120 respondents selected using purposive sampling. The data were analyzed using validity and reliability tests, classical assumption tests, multiple linear regression, t-test, and F-test with SPSS version 26. The results show that menu innovation has a positive and significant effect on guest satisfaction (p < 0.05). Breakfast service also has a positive and significant effect on guest satisfaction (p < 0.05). Simultaneously, both variables significantly influence guest satisfaction (F = 41.701). Among the two variables, breakfast service is the most dominant factor affecting guest satisfaction. These findings indicate that improving menu innovation and breakfast service can enhance guest satisfaction and strengthen competitiveness in the hospitality industry.

This study aims to analyze the effect of employer branding on the job interest of Generation Z in digital companies. The background of this research is based on the increasing competition among companies in attracting young talents, especially Generation Z, who have unique characteristics such as being highly adaptive to technology, prioritizing work-life balance, and having high expectations toward the workplace environment. Employer branding has become an essential strategy for companies to build an attractive and competitive image as an employer. This study employs a quantitative approach using a survey method. Data were collected through an online questionnaire distributed to 100 respondents from Generation Z who are seeking jobs or interested in working in digital companies. The sampling technique used was purposive sampling. Data analysis techniques include validity testing, reliability testing, simple linear regression analysis, and t-test. The results indicate that employer branding has a positive and significant effect on Generation Z’s job interest. This implies that the better the company’s image as an employer, the higher the interest of Generation Z to work in the company. This study suggests that digital companies need to strengthen their employer branding strategies to attract and retain young talents.

Heating, Ventilating, and Air Conditioning (HVAC) systems often suffer from significant energy wastage due to their inability to adapt to real-time environmental changes, leading to high operational costs. Although Proportional-Integral-Derivative (PID) controllers are widely used for their simplicity and reliability, they struggle to handle the complex dynamics of modern environments, requiring advanced optimization to enhance efficiency. This study aims to optimize PID controllers by integrating the Queen Honey Bee Migration (QHBM) algorithm to improve HVAC performance, energy efficiency, and adaptability. The research method employs an experimental approach that compares the performance of conventional PID controllers with PID controllers optimized using the QHBM algorithm under dynamic environmental conditions. The results show that the PID-QHBM system significantly outperforms the conventional PID system, achieving a rise time of 0.2649 seconds and a settling time of 1.6874 seconds with an almost negligible steady-state error of 9.4991e-08. Although it experiences a slight overshoot of 16.3810%, the system stabilizes quickly and maintains the target temperature efficiently. In contrast, the conventional PID controller exhibits slower response characteristics, with a rise time of 1.3730 seconds, a settling time of 2.5144 seconds, and a larger steady-state error of 0.0361. This study demonstrates that integrating the QHBM algorithm into PID controllers provides a more effective solution for real-time temperature control, offering substantial improvements in energy efficiency and system performance. The findings contribute to advancing intelligent HVAC control systems that can better adapt to environmental variations while minimizing operational costs.