Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization International Journal of Advances in Artificial Intelligence and Machine Learning
Jaber, Mustafa Musa
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A Deep Learning Approach to Sentiment Analysis of Hotel Reviews: Comparing BERT and LSTM Models Wang, Gunawan; Jaber, Mustafa Musa
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 2 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i2.403

Abstract

Background of study: Online reviews are crucial in shaping consumer decisions, particularly in the hospitality industry, but accurately extracting sentiment remains challenging due to language subjectivity, varied expression styles, and significant class imbalance where positive reviews outweigh negative and neutral ones. Traditional machine learning methods often fail to address these issues effectively, favoring the majority class.Aims and scope of paper: This study employs BERT and LSTM deep learning models to classify hotel reviews into positive, neutral, and negative sentiment categories. The primary aim is to compare their performance in sentiment analysis and managing imbalanced data, evaluating both with and without under-sampling.Methods: A 20,000-review dataset from TripAdvisor was pre-processed, including stop word/special character removal, tokenization, stemming, and lemmatization. Star ratings were categorized: 4-5 as positive, 3 as neutral, and 1-2 as negative. Random under-sampling was applied to the majority (positive) class to balance the dataset. BERT (bert-base-uncased) and LSTM models were trained with an 80:20 training-validation split, and evaluated using accuracy, precision, recall, and F1-score, with 5-fold cross-validation.Result: BERT without under-sampling achieved the highest overall accuracy of 0.86, with strong F1-scores for positive (0.93) and negative (0.79) sentiments. However, all models struggled with neutral sentiments (BERT F1-score: 0.43, LSTM: 0.25). Under-sampling improved neutral class recall (BERT: 0.79) but decreased overall accuracy (BERT: 0.73; LSTM: 0.67) and positive class precision.Conclusion: BERT generally outperforms LSTM for hotel review sentiment analysis, particularly with imbalanced data. While under-sampling helps address class imbalance by improving neutral recall, it incurs significant performance trade-offs, reducing overall accuracy and precision in majority classes due to information loss. Future work should explore advanced resampling (SMOTE, ADASYN) or transfer learning (RoBERTa, XLNet) for better balance and neutral sentiment classification.
Software Agent Simulation Design on the Efficiency of Food Delivery Ismail, Shahrinaz; Mostafa, Salama A; Baharum, Zirawani; Erianda, Aldo; Jaber, Mustafa Musa; Jubair, Mohammed Ahmed; Adiya, M. Hasmil
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2648

Abstract

Food delivery services have gained popularity since the emergence of online food delivery. Since the recent pandemic, the demand for service has increased tremendously. Due to several factors that affect how much time additional riders spend on the road; food delivery companies have no control over the location or timing of the delivery riders. There is a need to study and understand the food delivery riders' efficiency to estimate the service system's capacity. The study can ensure that the capacity is sufficient based on the number of orders, which usually depends on the number of potential customers within a territory and the time each rider takes to deliver the orders successfully. This study is an opportunity to focus on the efficiency of the riders since there is not much work at the operational level of the food delivery structure. This study takes up the opportunity to design a software agent simulation on the efficiency of riders' operations in food service due to the lack of simulation to predict this perspective, which could be extended to efficiency prediction. The results presented in this paper are based on the system design phase using the Tropos methodology. At movement in the simulation, the graph of the efficiency is calculated. Upon crossing the threshold, it is considered that the rider agents have achieved the efficiency rate required for decision-making. The simulation's primary operations depend on frontline remotely mobile workers like food delivery riders. It can benefit relevant organizations in decision-making during strategic capacity planning.
Co-Authors Adiya, M. Hasmil Aldo Erianda, Aldo Baharum, Zirawani Gunawan Wang, Gunawan Ismail, Shahrinaz Jubair, Mohammed Ahmed Mostafa, Salama A