This study aims to determine how servicescape and service quality affect customer satisfaction and word of mouth. This research is based on customer satisfaction as a benchmark for business continuity that can have an impact on marketing, with pre-survey results based on visitor testimonials from 20 people interviewed, as many as 55% of consumers are dissatisfied with servicescape and as many as 45% of consumers are dissatisfied with quality. service and only as much as 40% whose satisfaction affects word of mouth. This research was conducted at Post Shop Coffee Toffee Bogor City, with a total of 100 respondents with two equations, namely equation I Y1 = PY1X1 + PY1X2 + ?1 and equation II: Y2 = PY2X1 + PY2X2 + PY2Y1 + ?2. The analysis used in this research begins with the classic assumption test in the form of normality test and heteroskesdasticity test, path analysis with t test; f test; and codetermination test and coefficient test between variables. The results showed that the normality test for equation I and equation II, the value of sig. > 0.05 means that the data residuals are normally distributed. Equations I and II do not occur heteroskesdasticity, that is, they are not patterned regularly and spread above and below the number 0. The T test for equation I obtained a calculated T value of 3.885 and 5,279 greater than T table 1985, the sig value of 0.000 less than 0.05 means that H0 is rejected and H1 is accepted, it means that both servicescape and service quality have an influence on customer satisfaction. The F test for the servicescape variable and service quality obtained the calculated F value of 88,319, greater than the F table of 3.090 and the sig 0,000 value smaller than 0.05, meaning that H0 is rejected and H1 is accepted. The contribution of the influence of X1 servicescape and X2 service quality on Y1 customer satisfaction amounted to 64.6%. T test for equation II, it is obtained that the value of T count is greater than T table 1.985, the sig value of 0.000 is less than 0.05, which means that service quality and customer satisfaction have an influence on word of mouth on consumers of Post Shop Coffee Tofee in Bogor City. The F test for the servicescape variable, service quality and customer satisfaction obtained a calculated F value of 129,419 greater than the F table of 2,699 and a sig 0,000 value less than 0.05, meaning that there is a joint influence between servicescape, service quality and customer satisfaction on word of mouth. The contribution or contribution of the influence of servicescape (X1), service quality (X2) and customer satisfaction (Y1) to word of mouth (Y2) is 80.2%. The correlation between customer satisfaction and word of mouth is 0.874 which means that customer satisfaction has a strong relationship. very strong against word of mouth.

Natural language processing is one of the essential activities of artificial intelligence. There is an excellent need for chatbots that require integration into artificial intelligence applications. The local language makes the process easier. Our research aims to analyze the possibilities and challenges of implementing a chatbot in the Kumaon language. We also provide a detailed survey of the Kumaon language and map it to other languages to make it easier to process it for industrial use. This chatbot can help with various needs and services in the Kumaon language. The method used in this research is a study analysis of the Kumaon language to deal with language extinction. The novelty in this research is a chatbot in the Kumaon language with end-to-end encryption so that the service user has good security.

This research aims to analyze the financial management of Indonesian hajj on yield using a dynamic system model and determine and simulate the return obtained with the expenditure so that the hajj funds remain safe. In addition, the purpose of this research is to provide input on policy strategies to the BPKH in increasing hajj financial yields. The method used in this research is dynamic systems modeling. The resulting model structure formulation is illustrated by a causal loop diagram and a stock-flow diagram. The results obtained were then simulated, and model validation was carried out using AME and AVE. Operational data used in this study uses time-series data. This study's population or several samples are annual historical data during the research period.  In modeling the dynamic system of hajj financial management on yield, it is divided into 2 (two) sub-models, namely the economic sub-model and the social sub-model. Meanwhile, to find out and simulate the yield obtained with the yield expenditure, 3 (three) scenarios were made, namely the existing, moderate, and optimistic scenarios. From the simulation results, it can be seen that making changes to portfolio policies with an optimistic scheme in the form of placements in Islamic Banks with a maximum of 20% and 80% investment and increasing the initial deposits of pilgrims from IDR 25 million to IDR 30 million in 2022 is a government policy intervention that produces optimal yield.

This study aims to identify factors affecting costumer satisfaction in PT.OSO sekuritas cabang galeri Universitas MH. Thamrin and describe it based on customer perceptions. This research uses qualitative approach with case study strategy. Data collection techniques used are interviews, observation, and document review. The participants of this study 25 customer with different background (student or employee). The result of study shows that factors affecting costumer satisfaction in PT.OSO sekuritas cabang galeri Universitas MH. Thamrin Branch are the excellent of the relationship between the employee and the costume, accurancy in satisfying customer desires or demand and adequate facilities in the investment gallery. This strategy will always improved by the employees to provide customer satisfaction in the long term.

Stocks, apart from having volatile and chaotic characteristics, also have various kinds of noise, non-linear and non-stationary movements, making them difficult to predict accurately. Therefore, the risk of investing in stocks depends on the skills of investors or traders in making judgments and decisions. This study aims to use Long Short-Term Memory (LSTM) as a decision-making technique with historical stock prices as the sole predictor, then implement it in conditions before and during the COVID-19 pandemic. The study results concluded that Long Short-Term Memory (LSTM) could be used as a decision-making technique in conditions before and during the COVID-19 pandemic with historical price inputs as the sole predictor. Based on the research that has been done, the following conclusions can be drawn: The LSTM model can predict stock prices well using historical stock prices as the sole predictor. The LSTM model can be used as a trading decision-making technique for day traders. The risk of stock prediction using the LSTM method in 2019 before the COVID pandemic was proven to be lower than in 2020 during the COVID pandemic. For further research, researchers can conduct more in-depth research on the risk criteria for making trading decisions as an essential reference that can be used to select the LSTM model.