Ensiklotari is a digital education startup, that provides a platform for dance performers and learners, where dance studios deliver virtual dance lessons in the form of coordinated, integrated, and directed video tutorials. Ensiklotari innovation is a blend of Design Thinking and Lean Startup strategies, or Lean Design Thinking. The strength of Design Thinking lies in the area of qualitative methods, which include details of ethnography, user research, and observation, while Lean Startup emphasizes quantitative methods that focus on metric-based analysis. Both ensure tests are conducted at each stage. The user-centered and customer-oriented implementation of the two strategies above accelerates the iterative process in the Ensiklotari software development stage. The results of the SUS measurement of the Ensiklotari application are 78 points. These results indicate that the overall usability level of the application is effective, efficient, and satisfactory for users in the promoter category or users who have the potential to give a positive response and promote the Ensiklotari website.
Background: Technology in the field of health services allows an individual to get a productive life and a longer life expectancy. The purpose of this study was to provide describe of obstacles in the implementation of the e-Hospital system, especially EMR services at the Dermatology & Venereology (DV) Polyclinic "X" Hospital from the perspective of service providers. Subjects and Method: This qualitative descriptive research was done with natural observation and using electronic questionnaire based on Modified-TAM Analyses. It was conducted since February-June 2022. The study participants were determined based on the Non-Random Purposive Sampling technique which included all of DV Polyclinic staff at "X" Hospital (3 doctors, 2 nurses, and 2 administrative staff). Results According to the results, it was found that the implementation of the e-Hospital system at the DV Polyclinic was still not running optimally. The most commonly encountered barrier factors based on the Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Behavioural Intetion (BEI) components was the perception that "e-Hospital cannot replace the conventional system" (50%); and for the PTH & SI components was "the hospitals are not ready to run e-Hospital system" (60%). Conclusion:The hospital management needed to re-evaluate which constraint factors cause these perspectives, which could be an obstacle in implementing the e-Hospital system. In addition, it was necessary to re-emphasize the e-Hospital system and its benefits through periodically training, affirm the vision and mission related to the system, and do periodic feedback evaluations from service providers so that the hospital management could comprehend which obstacles were found and were able to make changes and increase the motivation of service providers to use the e-Hospital system.
The trading of stocks is one of the activities carried out all over the world. To make the most profit, analysis is required, so the trader could determine whether to buy or sell stocks at the right moment and at the right price. Traditionally, technical analysis which is mathematically processed based on historical price data can be used. Parallel to technological development, the analysis of stock price and its forecasting can also be accomplished by using computer algorithms e.g. machine learning. In this study, Nonlinear Auto Regressive network with eXogenous inputs (NARX) neural network simulations were performed to predict the stock index prices. Experiments were implemented using various configurations of input parameters consisting of Open, High, Low, Closed prices in conjunction with several technical indicators for maximum accuracy. The simulations were carried out by using stock index data sets namely JKSE (Indonesia Jakarta index) and N225 (Japan Nikkei index). This work showed that the best input configurations can predict the future 13 days Close prices with 0.016 and 0.064 mean absolute error (MAE) for JKSE and N225 respectively.
Stock trading is one of the businesses that has been done worldwide. In order to gain the maximum profit, accurate analysis is needed, so a trader can decide to buy and sell stock at the perfect time and price. Conventionally, two analyses are employed, namely fundamental and technical. Technical analysis is obtained based on historical data that is processed mathematically. Along with technology development, stock price analysis and prediction can be performed with the help of computational algorithms, such as machine learning. In this research, Artificial Neural Network simulations to produce accurate stock price predictions were carried out. Experiments are performed by using various input parameters, such as moving average filters, in order to produce the best accuracy. Simulations are completed with stock index datasets that represent three continents, i.e. NYA (America, USA), GDAXI (Europe, Germany), and JKSE (Asia, Indonesia). This work proposes a new method, which is the utilization of input parameters combinations of C, O, L, H, MA-5 of C, MA-5 of O, and the average of O C prices. Furthermore, this proposed scheme is also compared to previous work done by Khorram et al, where this new work shows more accurate results.
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