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All Journal Data Science: Journal of Computing and Applied Informatics
Abdullah, Salwani
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Computer Science & IT

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Journal : Data Science: Journal of Computing and Applied Informatics

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A Review on Metaheuristic Approaches for Job-Shop Scheduling Problems Abdolrazzagh-Nezhad, Majid; Abdullah, Salwani
Data Science: Journal of Computing and Applied Informatics Vol. 8 No. 1 (2024): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v8.i1-17138

Abstract

Over the past several decades, interest in metaheuristic approaches to address job-shop scheduling problems (JSSPs) has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides a significant attention on reviewing state-of-the-art metaheuristic approaches that have been developed to solve JSSPs. These approaches are analysed with respect to three steps: (i) preprocessing, (ii) initialization procedures and (iii) improvement algorithms. Through this review, the paper highlights the gaps in the literature and potential avenues for further research.
Time Series Prediction of Bitcoin Cryptocurrency Price Based on Machine Learning Approach Eddie Ngai; Abdullah, Salwani; Nazri, Mohd Zakree Ahmad; Sani, Nor Samsiah; Othman, Zalinda
Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 2 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v7.i2-14356

Abstract

Over the past few years, Bitcoin has attracted the attention of numerous parties, ranging from academic researchers to institutional investors. Bitcoin is the first and most widely used cryptocurrency to date. Due to the significant volatility of the Bitcoin price and the fact that its trading method does not require a third party, it has gained great popularity since its inception in 2009 among a wide range of individuals. Given the previous difficulties in predicting the price of cryptocurrencies, this project will be developing and implementing a time series approach-based solution prediction model using machine learning algorithms which include Support Vector Machine Regression (SVR), K-Nearest Neighbor Regression (KNN), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) to determine the trend of bitcoin price movement, and assessing the effectiveness of the machine learning models. The data that will be used is the close prices of Bitcoin from the year 2018 up to the year 2023. The performance of the machine learning models is evaluated by comparing the results of R-squared, mean absolute error (MAE), mean squared error (RMSE), and also through a visualization graph of the original close price and predicted close price of Bitcoin in a dashboard. Among the models compared, LSTM emerged as the most accurate, followed by SVR, while XGBoost and KNN exhibited comparatively lower performance.
Co-Authors Abdolrazzagh-Nezhad, Majid Eddie Ngai Nazri, Mohd Zakree Ahmad Othman, Zalinda Sani, Nor Samsiah