Nurvita Trianasari

This study aimed to analyze the impact of digital transformation technology on hospital performance in Bandung City. In the current digital era, the healthcare sector is undergoing significant changes through the integration of information and communication technology (ICT). Hospitals are increasingly adopting digital technologies to improve operational efficiency and enhance healthcare service quality. This research employed a quantitative approach, surveying 50 hospitals in Bandung. Data were collected through questionnaires designed to measure technology adoption levels, operational efficiency, and patient satisfaction. The analysis results revealed a significant positive relationship between technology adoption levels and improvements in hospital performance. Specifically, higher levels of technology adoption were associated with enhanced service efficiency, reduced patient waiting times, and increased patient satisfaction. Hospitals that effectively implemented digital solutions streamlined operations, improved care quality, and met growing patient expectations for convenience and satisfaction. These findings highlight the importance of digital transformation for hospital management in Bandung and offer valuable insights into how healthcare institutions can leverage technology to enhance operations. Hospital managers can use these insights to plan and implement effective digital strategies, ultimately improving the overall quality and efficiency of healthcare services provided to the community.

Human factors significantly contribute to breaches in healthcare organizations, such as a lack of awareness regarding cybersecurity threats. This study aims to evaluate the information security behaviors of healthcare workers and identify critical demographic factors that influence them. The demographic factors examined in this study include age, gender, education level, type of profession, and frequently used devices to access health information systems (HIS). Information security behavior evaluated in this study consists of four dimensions: Device Protection, Password Management, Proactive Awareness, and Information Handling. This study employs quantitative methods. Data are collected through an online survey of 209 health workers in Indonesia. The data analysis process consists of two stages. The first stage is comparison group analysis using the Chi-square test to define significant factors in studied security behavior, and the second stage is multivariate analysis, which employs binary logistic regression to determine the most impactful variables. The findings show that, among the five characteristics examined, education level had the most significant effect on the information security behavior of healthcare workers in Indonesia. It underlines the necessity of education in sculpting security practices and awareness in an ever-changing digital world. This study recommends that while developing a security training and awareness program, the education level of healthcare workers should be considered to improve healthcare facilities' security posture and decrease data breach risks. As a result, governments and healthcare managers are encouraged to emphasize training programs as a strategic method of improving information security standards in the business.

Tenjolaya Village, situated in the Ciwidey Region, Rancabali, Bandung Regency, possesses significant potential as a tourism village due to its natural beauty and distinctive products such as orange kalua. However, the management and marketing of this tourism village still face challenges, including the ineffective utilization of digital technology and social media. This community service activity aims to enhance the village's marketing strategy through training and assistance in digital technology and social media utilization. Involving local communities, the training covered materials on digital content management, social media-based marketing, and activity outcome evaluation. The training results demonstrated a significant improvement in participants' understanding and capabilities, as evidenced by an increase in the average score from 83.18 before training to 93.64 after training (p<0.05). This activity provides hope that Tenjolaya Village can gain more recognition and attract more visitors, thereby impacting the village's economic growth.

Current technological developments occur in various sectors, one of which is marketing with increasing marketing activities carried out on social media such as Instagram. This is influenced by various factors such as the increase in internet users and the time each person uses the internet. In response to this development, as one of the transportation service providers, PT BST with one of its integrated products, namely Bhisa, uses Instagram social media to market its products and services. The use of Instagram with the username Bhisa.id is PT BST's effort to continue to compete with its competitors. This study aims to determine the influence of Social Media Marketing Activities carried out by PT BST on brand awareness, brand image, EWOM, and commitment both directly and indirectly. This study uses a quantitative causal research method by testing survey data from 180 respondents of Bhisa.id Instagram followers. Testing in the study will use the SEM PLS model which will be tested using the Smart PLS 3 application. The results of the study show that the SMMA variable has a positive influence on brand equity consisting of brand awareness and brand image and has a positive and significant effect on customer responses, namely e-WOM and commitment. Furthermore, strong brand equity contributes positively to customer responses, such as E-WOM and commitment to the Bhisa brand. This study concludes that effective marketing strategies on social media, especially Instagram, can increase brand equity and, in turn, generate better customer responses. The recommendation for marketing practitioners is to continue to utilize Instagram as a primary marketing tool and focus on

This study aimed to analyze public perception towards Telkomsel on social media using Latent Dirichlet Allocation (LDA) topic modelling and emotion-based sentiment analysis with the NRC lexicon. Data were collected from Twitter tweets and comments on Instagram and Facebook from October 25-31, 2023. The results showed that public perception is dominated by complaints and dissatisfaction related to difficulties in activating data packages, problems with topping up credit, poor network quality, expensive package prices, bugs in the MyTelkomsel application, and slow customer service response. Sentiment analysis revealed 53.8% negative sentiment on Twitter and Instagram, and 66.2% on Facebook. The dominant emotion was anger, with 42.75% on Twitter, 47.88% on Instagram, and 82.22% on Facebook. These findings indicate public dissatisfaction with Telkomsel, particularly regarding network quality, package prices, and customer service. Therefore, it is recommended that Telkomsel improve its service quality by focusing on enhancing network quality, strengthening signals in problematic areas, accelerating the data package activation and credit top-up processes, and improving the responsiveness and effectiveness of customer service.

The increase in the number of internet users supported by technological advances has encouraged changes in consumer lifestyles who now prefer to shop online. Nowadays, simple online shopping methods are available, especially through e-commerce which offers many choices according to customer desires and preferences. The huge opportunity from the level of internet usage and access to online shopping applications has created increasingly fierce competition in the future. This research aims to explore consumer behavior using Shopee, an e-commerce platform with a large impact in the realm of online commerce in Indonesia. In the purchasing decision making process, especially in the realm of online purchasing, there are various factors that influence consumer behavior. Therefore, this research aims to identify factors that are considered relevant in determining purchasing decisions, including cultural, social, personal, psychological and purchasing intention factors. This research uses data from 185 Shopee e-commerce users to analyze the factors that influence purchasing decisions. The results show that culture has the greatest influence on purchasing decisions, while social factors have no influence. Buying Intention is influenced by culture, personal, and psychology, and significantly influences purchasing decisions. It is hoped that the findings from this research can become a reference for stakeholders in e-commerce, especially sellers and service providers, to create effective strategies to maintain business continuity and increase competitiveness in the e-commerce market. As well as an in-depth understanding of the factors that influence consumer purchasing decisions can help e-commerce face the challenges of intense competition and take advantage of opportunities that exist from increased internet use and access to online shopping applications.

In the analysis of the data, we often faced with the problem of data where the data did not meet some assumptions. In conditions of such data is often called data messy. This problem is a consequence of the data that generates outliers that bias or error estimation. To analyze the data messy, there are three approaches, namely standard analysis, transform data and data analysis methods rather than a standard. Simulations conducted to determine the performance of a third comparative test procedure on average often the model variance is not homogeneous. Data simulation of each scenario is raised as much as 500 times. Next, we do the analysis of the average comparison test using three methods, Welch test, mixed models and Welch-r test. Data generation is done through software R version 3.1.2. Based on simulation results, these three methods can be used for both normal and abnormal case (homoscedastic). The third method works very well on data balanced or unbalanced when there is no violation in the homogenity’s assumptions variance. For balanced data, the three methods still showed an excellent performance despite the violation of the assumption of homogeneity of variance, with the requisite degree of heterogeneity is high. It can be shown from the level of power test above 90 percent, and the best to Welch method (98.4%) and the Welch-r method (97.8%). For unbalanced data, Welch method will be very good moderate at in case of heterogeneity positive pair with a 98.2% power. Mixed models method will be very good at case of highly heterogeneity was negative negative pairs with power. Welch-r method works very well in both cases. However, if the level of heterogeneity of variance is very high, the power of all method will decrease especially for mixed models methods. The method which still works well enough (power more than 50%) is Welch-r method (62.6%), and the method of Welch (58.6%) in the case of balanced data. If the data are unbalanced, Welch-r method works well enough in the case of highly heterogeneous positive positive or negative negative pairs, there power are 68.8% and 51% consequencly. Welch method perform well enough only in the case of highly heterogeneous variety of positive positive pairs with it is power of 64.8%. While mixed models method is good in the case of a very heterogeneous variety of negative partner with 54.6% power. So in general, when there is a variance is not homogeneous case, Welch method is applied to the data rank (Welch-r) has a better performance than the other methods.