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Unveiling perceptions: aspect-based sentiment analysis of Malaysia’s e-hailing reviews

10.11591/ijeecs.v37.i3.pp2077-2086
Khyrina Airin Fariza Abu Samah , Mohamad Azan Amit , Siti Salwa Salleh , Wannoraini Abdul Latif , Nurul ‘Azwa Kamarudin , Shahadan Saad , Mohd Nor Hajar Hasrol Jono
The growing demand for e-hailing services in Malaysia leads to increased competition among more than 20 licensed e-hailing service providers. Consumer satisfaction is a crucial factor influencing variables in the business organization, and understanding consumers’ perceptions is vital for service improvement. Reviews on e-hailing services are unstructured data and massive, making comparisons difficult. Thus, this study aims to classify Malaysia’s e-hailing service reviews from Google Play Store and X using latent Dirichlet allocation (LDA) and support vector machine (SVM). Aspect-based sentiment analysis (ABSA) was performed using a two-staged method, applying LDA for aspect category detection and SVM for aspect sentiment classification separately. Fare, availability, comfort, time, and convenience are five predetermined aspect categories in this study. The LDA for English and Malay achieved a perplexity of -7.31 and -7.49, respectively. Besides, the accuracy scores of SVM for English and Malay are 86.32% and 62.97%, respectively.
Volume: 37
Issue: 3
Page: 2077-2086
Publish at: 2025-03-01

Machine learning model approach in cyber attack threat detection in security operation center

10.11591/csit.v6i1.p80-90
Muhammad Ajran Saputra , Deris Stiawan , Rahmat Budiarto
The evolution of technology roles attracted cyber security threats not only compromise stable technology but also cause significant financial loss for organizations and individuals. As a result, organizations must create and implement a comprehensive cybersecurity strategy to minimize further loss. The founding of a cybersecurity surveillance center is one of the optimal adopted strategies, known as security operation center (SOC). The strategy has become the forefront of digital systems protection. We propose strategy optimization to prevent or mitigate cyberattacks by analyzing and detecting log anomalies using machine learning models. This study employs two machine learning models: the naïve Bayes model with multinomial, Gaussian, and Bernoulli variants, and the support vector machine (SVM) model with radial basis function (RBF), linear, polynomial, and sigmoid kernel variants. The hyperparameters in both models are then optimized. The models with optimized hyperparameters are subsequently trained and tested. The experimental results indicate that the best performance is achieved by the RBF kernel SVM model, with an accuracy of 79.75%, precision of 80.8%, recall of 79.75%, and F1-score of 80.01%; and the Gaussian naïve Bayes model, with an accuracy of 70.0%, precision of 80.27%, recall of 70.0%, and F1-score of 70.66%. Overall, both models perform relatively well and are classified in the very good category (75%‒89%).
Volume: 6
Issue: 1
Page: 80-90
Publish at: 2025-03-01

A novel hybrid model for sentiment analysis in MOOC forums with hybrid word and character-level neural networks

10.11591/ijeecs.v37.i3.pp1758-1771
Mohammed Jebbari , Mohamed Amine Ouassil , Mouaad Errami , Rabia Rachidi , Soufiane Hamida , Bouchaib Cherradi , Abdelhadi Raihani
Sentiment analysis is crucial, in the field of natural language processing (NLP). Has applications in different areas. This study focuses on analyzing sentiments in massive open online course (MOOC) forums highlighting its importance in understanding how users interact and shaping educational strategies. The study presents a novel hybrid neural network model specifically tailored for sentiment analysis in MOOC forums. This innovative model combines word level and character level embeddings to handle the linguistic expressions commonly found in this context. The model architecture integrates bidirectional long short-term memory (BiLSTM) layers for word level embeddings and convolutional neural networks (CNNs) for character level embeddings aiming to harness the strengths of both types of embeddings for a view of the linguistic used in MOOC forum posts. Notably this model achieves an accuracy rate of 93.11% showcasing its effectiveness, in sentiment analysis within MOOC forums. This research contributes to sentiment analysis within the context of online education.
Volume: 37
Issue: 3
Page: 1758-1771
Publish at: 2025-03-01

Smart home advisory system based on IoT-enabled sensor network

10.11591/ijeecs.v37.i3.pp1487-1496
Mohamed Feroz Mohd Ashrap Khan , Gregory Soon How Thien , Boon Kar Yap , Kah-Yoong Chan
A consistent drive to make daily living more streamlined and less complex was critical throughout the industrial revolution. Although many applications are available, the general adoption of these applications is hindered by several factors (security concerns, expensive costs, and perceived usefulness). Hence, a cost-efficient smart home system can provide significant advantages without placing unnecessary financial or operational costs on consumers. This study proposed an inexpensive internet of things (IoT) enabled smart home advisory system for promoting healthier living conditions. The system contained numerous sensors for detecting surrounding brightness, temperature, humidity, and dust levels. Consequently, the system demonstrated effective real-time updates regarding the simulated temperature (22–32 °C), humidity (11–53 %RH), light (3–100 lx), and dust (0–278 mg/m3) environments. Blynk software was also embedded as an efficient user interface for the application. Overall, the lowcost IoT-enabled smart home advisory system offered concrete benefits by allowing users to improve their living surroundings while decreasing energy usage.
Volume: 37
Issue: 3
Page: 1487-1496
Publish at: 2025-03-01

Detection of android malware with deep learning method using convolutional neural network model

10.11591/csit.v6i1.p68-79
Reza Maulana , Deris Stiawan , Rahmat Budiarto
Android malware is an application that targets Android devices to steal crucial data, including money or confidential information from Android users. Recent years have seen a surge in research on Android malware, as its types continue to evolve, and cybersecurity requires periodic improvements. This research focuses on detecting Android malware attack patterns using deep learning and convolutional neural network (CNN) models, which classify and detect malware attack patterns on Android devices into two categories: malware and non-malware. This research contributes to understanding how effective the CNN models are by comparing the ratio of data used with several epochs. We effectively use CNN models to detect malware attack patterns. The results show that the deep learning method with the CNN model can manage unstructured data. The research results indicate that the CNN model demonstrates a minimal error rate during evaluation. The comparison of accuracy, precision, recall, F1 Score, and area under the curve (AUC) values demonstrates the recognition of malware attack patterns, reaching an average of 92% accuracy in data testing. This provides a holistic understanding of the model's performance and its practical utility in detecting Android malware.
Volume: 6
Issue: 1
Page: 68-79
Publish at: 2025-03-01

Advancements in seismic data collection and analysis through machine learning

10.11591/ijeecs.v37.i3.pp2058-2068
Sujata Kulkarni , Malay Phadke , Ashwini Sawant , Neel Patel , Om Patil
The evolution of seismic data collection has been driven by the need for stations to capture large volumes of high-frequency signals continuously. These signals typically contain both seismic and non-seismic information. Previous research converted SEED data into CSV format and used principal component analysis (PCA) for feature extraction from the seismic dataset. Machine learning models were then employed, showing an improvement in identifying seismic and non-seismic events. This paper focuses on applying deep learning methods, specifically deep neural networks (DNN) and a hybrid model combining long short-term memory (LSTM) networks with DNN (LSTM+DNN). The proposed deep learning models demonstrate a notable improvement over traditional machine learning technique. Experimental results show a test accuracy of 99.24% using deep learning, compared to an average of 97.80% achieved with machine learning models, indicating a 1.46% enhancement in detection accuracy. This underscores the potential of deep learning in accurately detecting seismic events in real-time monitoring systems.
Volume: 37
Issue: 3
Page: 2058-2068
Publish at: 2025-03-01

Correlation between input and output parameters of microbial fuel cell

10.11591/ijeecs.v37.i3.pp1452-1463
Ganesan V. Murugesu , Saiful Nizam Khalid , Hussain Shareef , Saad Saleem Khan
This paper presents the correlation between open circuit voltage (OCV) and pH, temperature, and total dissolved solids (TDS) of an air cathode single chamber microbial fuel cell (MFC) using artificial neural network (ANN) and support vector machine (SVM) algorithms. Previous works used terminal voltages as output parameters to determine the correlation between MFCs' input and output parameters. However, OCV is the most important measurement that can determine the validity of the MFC. Thus, various tests were conducted to analyze the correlation between OCV and input parameters using ANN and SVM algorithms. Both techniques show a strong correlation between OCV and input parameters with the highest R2 values. The highest OCV value obtained from the experiment is 1.179 V at pH 5.26, temperature 299K, and TDS 3,124 ppm. Furthermore, an ANN model was developed to predict the OCV value based on pH, temperature, and TDS value.
Volume: 37
Issue: 3
Page: 1452-1463
Publish at: 2025-03-01

Comparative analysis of incremental conductance MPPT for enhanced algorithm performance

10.11591/ijeecs.v37.i3.pp1415-1427
Asnil Asnil , Refdinal Nazir , Krismadinata Krismadinata , Muhammad Nasir
The incremental conductance (INC) approach is limited in terms of its response speed, accuracy during steady state, and ability to handle oscillations. As a result, this algorithm is ineffective in situations with variations in solar radiation and temperature, particularly abrupt fluctuations. An enhanced variable step size INC approach is suggested to improve system efficiency and performance. The proposed algorithm is subjected to rigorous testing and analysis with other INC methods for better results. The research findings indicate that the proposed algorithm's tracking efficiency can be enhanced to 99.83% under varying radiation conditions and constant temperature. Additionally, the method utilizing the second model achieves a 99.83% tracking efficiency, while the method employing the first model achieves a tracking efficiency of 89.99%. In comparison, the conventional method achieves a tracking efficiency of 96.36%. Regarding radiation and temperature circumstances, the tracking efficiency value varies for different methods. Specifically, the tracking efficiency is 95.21% for the approach using the proposed algorithm’s, 92.94% for the method using the second model, 83,39% for the method using the first model, and 91.19% for the conventional way. Therefore, it can be inferred that the suggested Maximum power point tracking (MPPT) algorithm performs better than other algorithms under both test situations.
Volume: 37
Issue: 3
Page: 1415-1427
Publish at: 2025-03-01

Power of analytic tools in Oxygen Forensic® Detective based on NIST cybersecurity framework

10.11591/csit.v6i1.p8-19
Tole Sutikno , Iqbal Busthomi
The National Institute of Standards and Technology (NIST) cybersecurity framework is a systematic approach for assessing and improving cybersecurity procedures in digital investigations. Oxygen Forensic® Detective is a digital forensic software that integrates multiple analytic tools to assist investigators in extracting valuable insights from digital evidence. The analytic tools, including timeline, social graph, image categorization, facial categorization, maps, data search, key evidence, optical character recognition, statistics, and translation, assist investigators in thoroughly analyzing digital artifacts, establishing connections, and accurately classifying images with precision and effectiveness. By incorporating these analytical resources into Oxygen Forensic® Detective, a comprehensive strategy is established to effectively combat cyber threats. The NIST cybersecurity framework is incorporated into the tool, offering a methodical approach to identifying and reducing cybersecurity risks. Law enforcement agencies can enhance the productivity and effectiveness of their forensic methodologies by implementing these advanced technologies. This can result in successful prosecutions and improved cybersecurity practices.  Overall, the utilization of analytical tools in criminological inquiries has experienced a substantial rise in the contemporary digital era.
Volume: 6
Issue: 1
Page: 8-19
Publish at: 2025-03-01

Machine learning based optimized sea vessel location detection to identify illegal fishing

10.11591/ijeecs.v37.i3.pp1626-1636
Ajay Kumar , Kakoli Banerjee , Pradeep Kumar , Harsha K. G. , Vinooth P. , Pankaj Kumar , Saumya Saumya , Nishant Nishant , Satyam Verma , Vidushi Bhardwaj
Illegal fishing is a pervasive and destructive global issue that poses a significant threat to maritime ecosystems and the resilience of fisheries. Illegal, unregulated, and unreported (IUU) fishing leads to the extinction of the fishing population. Many researchers have presented various approaches to detect illegal fishing, for example, using sensors, image recognition, and convolutional neural networks (CNNs) but each one has some limitations. Our research aims to compare different vessel gear types to select the best vessel container that can be easily monitored and less prone to illegal activities. To achieve this, our research proposed an optimization method that involves hyperparameter selection using a genetic algorithm instead of a grid search. Using the crossover method of the genetic algorithm our model is compatible with larger datasets and unknown search space which is not possible in the baseline algorithm i.e. grid search. Moreover, after applying the genetic hyperparameter optimization technique, the overall accuracy, recall, and F1 score is increased for all vessel types significantly. While comparing our optimized model with the existing model with different evaluation metrics, our model’s performance is outstanding.
Volume: 37
Issue: 3
Page: 1626-1636
Publish at: 2025-03-01

A hybrid approach for hotspot problem using load balancing and advanced ant colony algorithm

10.11591/ijeecs.v37.i3.pp1865-1873
Padmini Mysuru Srikantha , Sampath Kuzhalvaimozhi , Susheela Shimoga Balasubramanya , Sklita Buthello , Sadana Hulugundi Jagadish , Spandana Mallegowdanakoppalu Thammanna
Wireless sensor networks (WSNs) are crucial in various applications such as environmental surveillance, military operations, transportation monitoring, and healthcare. However, due to a finite set of sensor nodes' resources concerning energy, memory, disk, and CPU processing, nodes in WSNs often face hotspot issues. The sensor nodes that are located near the base station, are responsible for relaying data not only from themselves but also from neighboring nodes. This leads to hotspot issues, where nodes near the base station experience higher traffic loads and faster energy depletion. This paper mainly focuses on mitigating hotspot issues in heterogeneous WSNs using unequal clustering, load balancing, and an advanced ant colony algorithm. This approach involves devising strategies for selecting cluster heads, determining clusters optimal number and formation, and optimizing data transmission processes. Central to the methodology is utilizing load balancing mechanisms and an advanced ant colony algorithm to distribute the workload among sensor nodes more evenly and find the optimum routing path. The proposed algorithm shows promise in alleviating traffic congestion and energy depletion and provides an innovative approach to enhance network performance and prolong the lifespan of sensor nodes.
Volume: 37
Issue: 3
Page: 1865-1873
Publish at: 2025-03-01

Empower BreastNet: breast cancer detection with transfer learning VGG Net-19

10.11591/ijeecs.v37.i3.pp1927-1935
Vaishali M. Joshi , Prajkta P. Dandavate , Rashmi Ramamurthy , Riddhi Mirajkar , Neeta N. Thune , Gitanjali R. Shinde
Breast cancer is a major cause of death among women globally, making early detection crucial for effective treatment. This study introduces a new deep learning (DL) method using transfer learning (TL) to automatically detect and diagnose breast cancer. TL improves performance on new tasks by using knowledge from previous tasks. In this study, we use pre-trained convolutional neural networks (CNNs) like AlexNet, ResNet50, visual geometry group (VGG)-16, and VGG-19 to extract features from the breast cancer wisconsin (BCW) diagnostic dataset. We measure the model's success with accuracy, sensitivity, specificity, precision, and F-score. The results show that the VGG-19 model, when applied with TL, performs best for diagnosing breast cancer, achieving an overall accuracy of 98.75%, sensitivity of 97.38%, specificity of 98.35%, precision of 97.35%, and an F-score of 97.66%.
Volume: 37
Issue: 3
Page: 1927-1935
Publish at: 2025-03-01

Visual treatment with AR for children with dysphasia

10.11591/ijeecs.v37.i3.pp1702-1711
Misael Lazo Amado , Meyluz Monica Paico Campos
The language disorder known as dysphasia significantly affects the ability to communicate effectively, presenting challenges in both comprehension and expression of language. To address this issue, the development of a visual treatment using augmented reality (AR) specifically designed for children with dysphasia has been proposed. The methodology selected for this project is analysis, design, development, implementation, and evaluation (ADDIE), an innovative methodology that encompasses analysis, design, development, implementation and evaluation. This methodology is perfectly adapted to the needs of the project, allowing a systematic and complete approach at all stages of the process of creating the visual treatment. The results obtained show that the visual treatment with AR has been positively evaluated by development experts and dysphasia specialists. Its innovative capacity to assist children with this disorder in health and educational settings is highlighted. This approach provides an effective tool to improve the communication and language development of children affected by dysphasia, offering new opportunities for their learning and growth. Its implementation in healthcare and educational settings could have a significant impact on the quality of life and development of these children.
Volume: 37
Issue: 3
Page: 1702-1711
Publish at: 2025-03-01

Leveraging 3D convolutional networks for effective video feature extraction in video summarization

10.11591/ijeecs.v37.i3.pp1616-1625
Bhakti Deepak Kadam , Ashwini Mangesh Deshpande
Video feature extraction is pivotal in video processing, as it encompasses the extraction of pertinent information from video data. This process enables a more streamlined representation, analysis, and comprehension of video content. Given its advantages, feature extraction has become a crucial step in numerous video understanding tasks. This study investigates the generation of video representations utilizing three-dimensional (3D) convolutional neural networks (CNNs) for the task of video summarization. The feature vectors are extracted from the video sequences using pretrained two-dimensional (2D) networks such as GoogleNet and ResNet, along with 3D networks like 3D Convolutional Network (C3D) and Two-Stream Inflated 3D Convolutional Network (I3D). To assess the effectiveness of video representations, F1-scores are computed with the generated 2D and 3D video representations for chosen generic and query-focused video summarization techniques. The experimental results show that using feature vectors from 3D networks improves F1-scores, highlighting the effectiveness of 3D networks in video representation. It is demonstrated that 3D networks, unlike 2D ones, incorporate the time dimension to capture spatiotemporal features, providing better temporal processing and offering comprehensive video representation.
Volume: 37
Issue: 3
Page: 1616-1625
Publish at: 2025-03-01

Attention deficit and hyperactivity disorder classification in quantitative EEG signals using machine learning algorithms

10.11591/ijeecs.v37.i3.pp1580-1587
Syifani Ihfadza Aliyah , Sastra Kusuma Wijaya , Yetty Ramli
Attention deficit and hyperactivity disorder (ADHD) classification method as a quantitative observation has been continually improved to assist medical practitioners. Currently, machine learning algorithms such as k-nearest neighbors (KNN), multilayer perceptron (MLP), and support vector machine (SVM) are widely used. This study proposed a feature extraction method for quantitative electroencephalography (qEEG) data derived from the continuous wavelet transform (CWT) to classify children with ADHD versus healthy subjects. Subsequently, this study compared the performance of the classification pipeline before and after the implementation of principal component analysis (PCA) on the features prior to processing with machine learning algorithms. The results revealed that the overall performance of the classifiers consistently improved after the implementation of PCA. The results highlight the varying impact of PCA on classifier performance, with KNN showing an improvement in testing accuracy from 61.84% to 69.21% following PCA implementation, while the other classifiers showed deterioration in performance. These findings suggest that while PCA may be beneficial for some classifiers, its impact on performance varies depending on the specific characteristics of the dataset and the classifier utilized. Moreover, this study provides insight for future implementation of the classification method for ADHD patients across a more specific clinical range of the spectrum.
Volume: 37
Issue: 3
Page: 1580-1587
Publish at: 2025-03-01
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