E-learning nowadays has become a requirement for institutions to support their learning activities. To adopt e-learning, an institution requires a large strategy and resources for optimal application. Unfortunately, not all institutions that have used e-learning got the desired results or expectations. This study aims to identify the extent of the level of readiness of e-learning implementation in institution X. The degree of institutional readiness will determine the success of future e-learning utilization. In addition, institutional readiness measurement are needed to evaluate the effectiveness of strategies in e-learning development. The research method used is survey with questionnaire designed based on integration of 8 best practice ELR (e-learning readiness) model. The results showed that from 13 factors of integrated ELR model being measured, there are 3 readiness factors included in the category of not ready and needs a lot of work. They are human resource (2.57), technology skill (2.38) and content factors (2.41). In general, e-learning implementation in institutions is in the category of not ready but needs some of work (3.27). Therefore, the institution should consider which factors or areas of ELR factors are considered still not ready and needs improvement in the future.

K-Means Clustering (KMC) is a technique used in performing data groupings. The data classification procedure is based on the degree of membership of each member. The purpose of this study is to group the existing Provinces in Indonesia based on Population Density, School Participation Rate, Human Development Index, and Open Unemployment Rate using K-Means Clustering. The result reveals 5 large clusters in each center in South Sumatra, Lampung, DKI Jakarta, Central Java, and West Kalimantan.

Around the world, the adoption of digital health applications is growing very fast. The use of e-health laboratory systems is increasing while research on the factors that impact users to use e-health laboratory systems in Indonesia has not been done much. The objective of this study is to analyze the behavioral factors of e-health laboratory users. This study includes a survey conducted on Indonesian users, and data analysis was carried out thoroughly. Based on the Technology Acceptance Model, this research framework explores a combination of variables consisting of task-driven, technology-driven, human-driven, and adoption variables to form the model proposed in this study. This model was verified using the Structural Equation Modeling (SEM) method for factor analysis, path analysis, and regression. A total of 163 respondents were collected to evaluate this research model empirically and the level of this study were individuals. These three problems are all essential in affecting usage intentions in adopting an e-health laboratory system. Specifically, task technology fit, information quality, and accessibility show a direct effect on both perceived usefulness and perceived ease of use factors perceived by the user, and have an indirect influence on the adoption of an e-health laboratory system through these two factors. The design of an online laboratory system affects perceived ease of use and personal innovativeness factors affect the perceived usefulness that users feel when adopting a laboratory system, while task technology fit and personal innovativeness factors do not affect the perceived ease of use. However, overall technology characteristic and perceived usefulness followed by design are the main predictors of adopting an e-health laboratory system on e-health systems in Indonesia.