A tourism dataset from historical transaction for recommender systems

The tourism industry has currently grown in various aspects, including the types of attractions, their quantity, and the number of tourist visits in various regions, contributing positively to both regional and global economies. Historical transactions are essential for developing recommender systems, utilizing techniques such as Collaborative Filtering and Demographic Filtering. TripAdvisor is a reputable website providing a wide range of accessible tourism information, including attractions, user profiles, and ratings. However, this unstructured raw data requires processing to create an adequate dataset for recommender systems. This study conducted a series of data processing steps on the raw data, including data restructuring, validation, content addition, integration with Google Maps, normalization, and modeling. This study successfully produced an original dataset comprising User Transaction, Item or Attraction, Attraction Type, Continent, Region, Country, City, and Visiting Mode. It also includes an entity relational model for tourism in Indonesia, particularly in Bali, Malang, and Yogyakarta regions, based on various global user experiences. This dataset is adequate and essential for developing various models of tourism recommender systems such as using Collaborative Filtering.

A data package for abstractive opinion summarization, title generation, and rating-based sentiment prediction for airline reviews

Customer reviews are valuable resources containing customer opinions and sentiments toward the product. The reviews are informative but can be quite lengthy or may contain repetitive information calling for opinion summarization systems that retain only the significant opinion information from the review. Abstractive summarization is a form of text summarization that generates a summary mimicking a human-written summary [1]. When pretrained language models are finetuned for abstractive review summarization, there usually occurs a problem known as the 'domain shift', because the source and target domains exhibit data from varying distributions [2]. This issue results in performance degradation of the model at the target end. This paper contributes a data package comprising of an annotated abstractive summarization dataset (annotated_abs_summ) of airline reviews having 500 reviews and abstractive summary pairs, a dataset (review_titles_data) consisting of 7079 reviews and review title pairs for review title generatioon or domain adaptive training [3] to address the domain shift problem for abstractive opinion summarization and, an annotated reviews dataset (annotated_sentiment) for rating-based sentiment classification. All datasets have been collected from the Skytrax Review Portal via web scraping using Python programming language. The datasets have several potential use cases. The abstractive summarization dataset can serve as a benchmark dataset for airline review summarization. The dataset for domain adaptive training can be used as a standalone dataset for review title generation. The dataset for sentiment analysis is multipurpose having columns like user rating and recommendation value, that can be used for statistical analysis like finding correlation between these data items as well as for other Natural Language Processing (NLP) tasks like predicting rating or recommendation value from the customer reviews. The datasets can be extended using various data augmentation techniques [4,5]. Moreover, the datasets are related and can be collectively used to develop a multi-task learning model [6] for better learning efficiency and improved performance.

Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM)

BACKGROUND

Stock market process is full of uncertainty; hence stock prices forecasting very important in finance and business. For stockbrokers, understanding trends and supported by prediction software for forecasting is very important for decision making. This paper proposes a data science model for stock prices forecasting in Indonesian exchange based on the statistical computing based on R language and Long Short-Term Memory (LSTM).

FINDINGS

The first Covid-19 (Coronavirus disease-19) confirmed case in Indonesia is on 2 March 2020. After that, the composite stock price index has plunged 28% since the start of the year and the share prices of cigarette producers and banks in the midst of the corona pandemic reached their lowest value on March 24, 2020. We use the big data from Bank of Central Asia (BCA) and Bank of Mandiri from Indonesia obtained from Yahoo finance. In our experiments, we visualize the data using data science and predict and simulate the important prices called Open, High, Low and Closing (OHLC) with various parameters.

CONCLUSIONS

Based on the experiment, data science is very useful for visualization data and our proposed method using Long Short-Term Memory (LSTM) can be used as predictor in short term data with accuracy 94.57% comes from the short term (1 year) with high epoch in training phase rather than using 3 years training data.

A Framework for Obstacles Avoidance of Humanoid Robot Using Stereo Vision

In this paper, we propose a framework for multiple moving obstacles avoidance strategy using stereo vision for humanoid robot in indoor environment. We assume that this model of humanoid robot is used as a service robot to deliver a cup to customer from starting point to destination point. We have successfully developed and introduced three main modules to recognize faces, to identify multiple moving obstacles and to initiate a maneuver. A group of people who are walking will be tracked as multiple moving obstacles. Predefined maneuver to avoid obstacles is applied to robot because the limitation of view angle from stereo camera to detect multiple obstacles. The contribution of this research is a new method for multiple moving obstacles avoidance strategy with Bayesian approach using stereo vision based on the direction and speed of obstacles. Depth estimation is used to obtain distance calculation between obstacles and the robot. We present the results of the experiment of the humanoid robot called Gatotkoco II which is used our proposed method and evaluate its performance. The proposed moving obstacles avoidance strategy was tested empirically and proved effective for humanoid robot.

A Robust Obstacle Avoidance for Service Robot Using Bayesian Approach

The objective of this paper is to propose a robust obstacle avoidance method for service robot in indoor environment. The method for obstacles avoidance uses information about static obstacles on the landmark using edge detection. Speed and direction of people that walks as moving obstacle obtained by single camera using tracking and recognition system and distance measurement using 3 ultrasonic sensors. A new geometrical model and maneuvering method for moving obstacle avoidance introduced and combined with Bayesian approach for state estimation. The obstacle avoidance problem is formulated using decision theory, prior and posterior distribution and loss function to determine an optimal response based on inaccurate sensor data. Algorithms for moving obstacles avoidance method proposed and experiment results implemented to service robot also presented. Various experiments show that our proposed method very fast, robust and successfully implemented to service robot called Srikandi II that equipped with 4 DOF arm robot developed in our laboratory.