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All Journal Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC)
Berlikozha, Bauyrzhan
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Development of Method to Predict Career Choice of IT Students in Kazakhstan by Applying Machine Learning Methods Berlikozha, Bauyrzhan; Serek, Azamat; Zhukabayeva, Tamara; Zhamanov, Azamat; Dias, Oliver
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.25558

Abstract

The growing intricacy of IT education requires resources to aid students in choosing specialized pathways. This study investigates the prediction of specialization preferences among IT students at SDU University in Kazakhstan through the application of machine learning techniques. The research contribution is the development of a predictive model that enhances academic advising by incorporating multiple factors, including academic performance, personality traits, qualifications, and extracurricular involvement. The research examined 692 anonymized student profiles and evaluated the efficacy of five machine learning algorithms: Random Forest, K-Nearest Neighbors, Support Vector Machine, Gradient Boosting, and Naive Bayes. Stratified 10-fold cross-validation was utilized to reduce the risk of overfitting. Gradient Boosting attained a peak accuracy of 99.10% in validation; however, its performance decreased to 92.16% on an independent test set, suggesting overfitting. Naive Bayes exhibited the lowest accuracy, recorded at 35.26%. Logistic regression analysis indicated a statistically significant correlation (p < 0.05) among academic performance, extracurricular involvement, and specialization selection. Personality traits and certifications significantly influenced the prediction process. The findings suggest that although Gradient Boosting demonstrates high effectiveness, the associated risk of overfitting requires additional refinement for practical application. The notable impact of academic performance and extracurricular activities indicates that educational institutions ought to prioritize these elements in student guidance. The incorporation of machine learning-based recommendations into advising frameworks enhances the precision of specialization predictions, thereby improving student decision-making and career alignment.
Resource-Efficient Sentiment Classification of App Reviews Using a CNN-BiLSTM Hybrid Model Baktibayev, Daulet; Serek, Azamat; Berlikozha, Bauyrzhan; Rustauletov, Babur
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13954

Abstract

This study evaluates the performance of a hybrid convolutional neural network and bidirectional long short-term memory (CNN + BiLSTM) model for sentiment classification on user reviews from the Spotify mobile application. The primary aim is to explore whether competitive results can be achieved without relying on transformer-based architectures, which often require substantial computational resources. The proposed CNN + BiLSTM model combines local feature extraction with sequential context modeling and is benchmarked against traditional machine learning and simpler deep learning models, including a Random Forest classifier enhanced with polarity features, a standalone CNN, and a fully connected DNN. Sentiment labels were binary (positive or negative) and directly provided in the dataset without being inferred from star ratings. The dataset was balanced to avoid class skew. Experimental results indicate that the CNN + BiLSTM model achieves moderate improvements over the baseline models, with an accuracy of 0.8861 and an F1-score of 0.8691. While it does not surpass the highest-performing transformer-based methods reported in the literature, it performs comparably to several of them, despite having a lower computational footprint. Analyses of ROC curves, confusion matrices, and training dynamics further contextualize the model’s performance, showing strengths in classifying negative sentiments and convergence efficiency. To address overfitting, early stopping and dropout layers were employed as regularization techniques. The study contributes to the ongoing discourse on resource-efficient sentiment analysis by showing that hybrid architectures may offer a practical balance between model complexity and performance in specific application domains.
Co-Authors Baktibayev, Daulet Dias, Oliver Rustauletov, Babur Serek, Azamat Zhamanov, Azamat Zhukabayeva, Tamara