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Daud, Hanita
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Computer Science & IT Engineering

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Leveraging Social Media Data for Forest Fires Sentiment Classification: A Data-Driven Method Maharani, Warih; Daud, Hanita; Muhammad, Noryanti; Kadir, Evizal Abdul
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 3 (2024): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.3.392-407

Abstract

Background: The rise in forest fires over the last two years, which is due to rise in dry weather conditions and human activities, have greatly impacted an area of 1.6 million hectares, leading to significant ecological, economic, and health issues, hence the need to improve disaster response strategies. Previous research determined the lack of coverage regarding public response during forest fires with conventional methods such as satellite images and sensor data. However, social media platforms provide real-time information generated by users, along with location information of disaster events. Sentiment analysis helps in understanding the public reactions and responses to natural disasters, thereby increasing awareness about forest fires. Objective: The purpose of this research is to assess the efficiency of Long Short-Term Memory (LSTM) method in classifying sentiment for social networks in regard to forest fires. This research aims to examine the effect of TF-IDF, unigram, and the FastText features on the effectiveness of the classification of sentiment. Methods: The precision, recall, and F1 score of 2, 3, and 4 determined in the LSTM models with commonly available sentiment analysis tools, such as the Vader Sentiment Analysis and SentiWordNet was used to evaluate the performance of the model. Results: With an improvement of roughly 10%, the four layers of the LSTM model generated the best performance for the evaluation of sentiments about forest fires. The LSTM model with FastText achieved F1, recall and precision scores of 0.649, 0.641, and 0.659, which exceeds lexicon-based method including SentiWordNet and Vader. Conclusion: The experimental results showed that the LSTM model outperformed lexicon-based methods when used to analyse the tweets related to forest fire. Additional research is required to integrate rule-based models and LSTM models to develop a more robust model for dynamic data.   Keywords: Forest Fire, Disaster, Long Short-Term Memory, LSTM, Vader, SentiWordnet
A Novel Extension of the Fréchet Distribution: Statistical Properties and Application to Groundwater Pollutant Concentrations Suleiman, Ahmad Abubakar; Daud, Hanita; Othman, Mahmod; Sawaran Singh, Narinderjit Singh; Ishaq, Aliyu Ismail; Sokkalingam, Rajalingam; Husin, Abdullah
Data Science Insights Vol. 1 No. 1 (2023): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v1i1.3

Abstract

In this work, we propose and study a novel generalization of the Fréchet distribution called the odd beta prime Fréchet (OBPF) distribution. This distribution was an extension of the Fréchet distribution by applying the odd beta prime generalized family of distributions. The proposed model can be expressed as a linear mixture of Fréchet densities. The shapes of the density function possess great flexibility. It can accommodate various hazard shapes, such as increasing, decreasing, and reversed J. Some important statistical properties of the OBPF are derived, including the ordinary and incomplete moments, order statistics, and quantile function. We have used the maximum likelihood estimation method to estimate the model parameters. The application and flexibility of the new distribution are empirically proven using groundwater pollution data sets compared to other competing distributions. The new model can be used instead of existing lifetime distributions and is suitable to fit data with right-skewed and left-skewed behaviors
Forecasting the Southeast Asian Currencies against the British Pound Sterling Using Probability Distributions Suleiman, Ahmad Abubakar; Daud, Hanita; Othman, Mahmod; Husin, Abdullah; Ishaq, Aliyu Ismail; Sokkalingam, Rajalingam; Abdullah, Mohd. Lazim; Khan, Iliyas Karim
Data Science Insights Vol. 1 No. 1 (2023): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v1i1.5

Abstract

The current study aimed to identify the most suitable probability distribution function (pdf) for modeling the exchange rates of three countries. Financial data is essential to many people and to the management of a country. Volatility in financial data influences individual and the country's economic growth. This volatility in the exchange rates between the Malaysian Ringgit (MYR), Singapore Dollar (SGD), and Thailand Thai Baht (THB) against British Pound Sterling (GBP) is found to be very high which make it difficult to model and forecast. This is what has necessitated the development of an accurate and reliable approach for assessing and reducing the risks of trading in any of these currencies.
Co-Authors Abdullah Abdullah Abdullah, Mohd. Lazim Evizal Abdul Kadir Ishaq, Aliyu Ismail Khan, Iliyas Karim Muhammad, Noryanti Othman, Mahmod Sawaran Singh, Narinderjit Singh Sokkalingam, Rajalingam Suleiman, Ahmad Abubakar Warih Maharani