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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Information Systems Engineering and Business Intelligence
Muhammad, Noryanti
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Computer Science & IT Engineering

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Advancing machine learning for identifying cardiovascular disease via granular computing Ku Khalif, Ku Muhammad Naim; Muhammad, Noryanti; Mohd Aziz, Mohd Khairul Bazli; Irawan, Mohammad Isa; Iqbal, Mohammad; Setiawan, Muhammad Nanda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2433-2440

Abstract

Machine learning in cardiovascular disease (CVD) has broad applications in healthcare, automatically identifying hidden patterns in vast data without human intervention. Early-stage cardiovascular illness can benefit from machine learning models in drug selection. The integration of granular computing, specifically z-numbers, with machine learning algorithms, is suggested for CVD identification. Granular computing enables handling unpredictable and imprecise situations, akin to human cognitive abilities. Machine learning algorithms such as Naïve Bayes, k-nearest neighbor, random forest, and gradient boosting are commonly used in constructing these models. Experimental findings indicate that incorporating granular computing into machine learning models enhances the ability to represent uncertainty and improves accuracy in CVD detection.
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
Co-Authors Daud, Hanita Evizal Abdul Kadir Ku Khalif, Ku Muhammad Naim Mohammad Isa Irawan Mohd Aziz, Mohd Khairul Bazli Setiawan, Muhammad Nanda Warih Maharani