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Abarda, Abdallah
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The impact of feature extraction techniques on the performance of text data classification models Maiti, Abdallah; Abarda, Abdallah; Hanini, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1041-1052

Abstract

Sentiment analysis is a crucial discipline that focuses on the interpretation of feelings and points of view in textual data. Our study aims to assess the impact of different feature extraction methods on the accuracy of opinion research models. Techniques such as bag-of-words (BoW), term frequency-inverse document frequency (TF-IDF), Word2Vec, global vectors (GloVe) and bidirectional encoder representations from transformers (BERT) were used with three machine learning algorithms and three deep learning networks as classifiers. The IMDB movie review dataset was used for evaluation. The results showed that combining BERT with LSTM, CNN and RNN improved performance, achieving an accuracy rate of 94%, precision of 94.14%, recall of 93.27% and an F1 score of 89.33%. These results highlight the significant contribution of ERTB to model performance, outperforming other feature extraction techniques in text classification. The study concludes that the fusion of BERT and LSTM significantly improves model accuracy for opinion retrieval, recommending BERT as the main feature extraction method for optimizing performance in NLP tasks.
An approach-based ensemble methods to predict school performance for Moroccan students Maiti, Abdallah; Abarda, Abdallah; Hanini, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1211-1220

Abstract

Education is a key factor in Morocco's development, with school performance serving as a critical measure of the education system’s quality. However, disparities in student outcomes remain, influenced by socioeconomic, demographic, and infrastructural factors. Our study aims to develop a predictive model to assess and improve school performance in Morocco using ensemble machine learning techniques, focusing on the stacking approach. Data from the Massar platform includes variables such as gender, age, type of school, parental occupation, academic results, and residential area. After rigorous data cleaning and preprocessing, a stacking model was created by combining predictions from five base models: random forest, gradient boosting, k-nearest neighbors (KNN), support vector machine (SVM), and multi-layer perceptron (MLP). A random forest metamodel was used to integrate these results. The experimental results of the paper demonstrate the effectiveness of our approach. The stacking model achieved an accuracy of 78.70%, surpassing the individual base models. The meta-model demonstrated strong reliability, achieving an F1 score of 78.62% while reducing false negatives and ensuring balanced predictions. Among the base models, neural networks showed the best performance, achieving the highest predictive accuracy. This research highlights the potential of stacking methods for predicting school performance. Incorporating additional variables, such as parental education and teacher attributes, could further refine the model and enhance Morocco’s educational outcomes.
Co-Authors Hanini, Mohamed Maiti, Abdallah