Cryptocurrencies are digital assets that are widely used for trading and investing. One of the characteristics that traders take advantage of for profit is the high volatility of the price. Its volatile and rapidly changing prices have made cryptocurrency price predictions a challenging and highly sought-after research topic. Cryptocurrency price predictions usually only use historical prices on the dataset, while price movements are also influenced by other aspects such as sentiment contained in social media. This study proposes a new machine learning method to predict Ethereum and Solana cryptocurrency price, which integrates cryptocurrency historical price data and social media sentiment as inputs of the prediction model. FinBERT, a pre-trained sentiment analysis model is used to extract the sentiment implied in social network tweets into daily sentiment score, which are then combined with the historical market price data. The hybrid model of LSTM-GRU model is used to train the dataset and perform cryptocurrency price prediction. The experiment results show that the presented method can successfully predict the Ethereum and Solana price movement and has superior performance than all the benchmark models.

Currently, social media are commonly used by business organizations to get many advantages. Through social media, business gets feedback from their customer by giving them a room for sharing opinion or comment. This kind of customer engagement became a tool of marketing and promotion. This research proposes investigating the customer engagement by analysis the comments on social media (Facebook and Twitter) in transport online. This study investigates by mining the comments of fan page Facebook and tweet of Twitter in three transports online in Indonesia; Gojek, Grab, and Uber using the API service which is provided both of social media. The data comments are classified into some categories, positive, negative, and neutral sentiment using TF-IDF. The result shows that the category "Feedback system by driver" and "Feedback system by user" have the most comments for three means of transports online, while category "Service quality for driver" has the smallest comments. The study also reveals that the most comments are complaining. This feedback of social media can be used to evaluate the performance of these business transport online.

The permutation flow shop scheduling problem (PFSP) is a renowned problem in the scheduling research community. It is an NP-hard combinatorial optimization problem that has useful real-world applications. In this problem, finding a useful algorithm to handle the massive amounts of jobs required to retrieve an actionable permutation order in a reasonable amount of time is important. The recently developed crow search algorithm (CSA) is a novel swarm-based metaheuristic algorithm originally proposed to solve mathematical optimization problems. In this paper, a hybrid CSA (HCSA) is proposed to minimize the makespans of PFSPs. First, to make the CSA suitable for solving the PFSP, the smallest position value rule is applied to convert continuous numbers into job sequences. Then, the HCSA uses a Nawaz–Enscore–Ham (NEH) technique to create a population with the required levels of quality and diversity. We apply a local search to enhance the quality of the solutions and avoid premature convergence; simulated annealing enhances the local search of a method based on a variable neighborhood search. Computational tests are used to evaluate the algorithm using PFSP benchmarks with job sizes between 20 and 500. The tests indicate that the performance of the proposed HCSA is significantly superior to that of other algorithms.

Content based recommendation system tries to recommend items similar to those a given user has likely in the past, whereas systems designed according to the collaborative recommendation paradigm identify users whose preferences are similar to those of the given user and recommend items they have liked. The proposed recommendation system is discussing about Data finitihotel recommendations for a place across U.S. The selected process for the recommendation is calculating the rating of hotel categories on the city. By calculating hotel categories from several city by combining two features namely categories and taken from the city of the selected hotelcategory as well. The result of process will be recommended hotel with highest ratingto the user.