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Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Published by Universitas Bumigora
ISSN : -     EISSN : 28285611     DOI : https://doi.org/10.30812/ijecsa.v1i2
Core Subject : Science,
Computer Science & IT
Description of Journal : The International Journal of Engineering and Computer Science Applications (IJECSA) is a scientific journal that was born as a forum to facilitate scientists, especially in the field of computer science, to publish their research papers. The 12th of the 12th month of 2021 is the historic day of the establishment of the IJECSA International Journal. The initial idea of ​​forming the IJECSA Journal was based on the thoughts and suggestions of Experts and Lecturers of Computer Science at Bumigora University Mataram-Lombok. This journal covers all areas of computer science research, and studies literature including hardware, software, computer systems organization, computational theory, information systems, computational mathematics, data mining and data science, computational methodology, computer applications, machine learning, and learning technologies. computer. The initial publication of the IJECSA journal is 2 editions in one year, and this will continue to be reviewed based on the number of submitted papers and will increase the number of editions based on the number of submitted papers. Incoming papers will be reviewed by experts in the field of computer science from various countries. We, on behalf of the Editors, ask researchers from all fields of computer science to contribute to the publication of the IJECSA Journal. Topics covered include Computational Mathematics Data Science Computer Applications Information Systems Learning Science And Technology Network Architectures And Protocols Computer Network Education Computer Distance Learning Cloud Computing Cluster Computing Distributed Computing E-Commerce Protocols Automata Theory Game Theory. E-Health Biometric Security And Artificial Intelligence Cryptography And Security Protocols Authentication And Identification Modulation/Coding/Signal Processing Network Measurement And Management Bayesian Networks, Fuzzy And Rough Set Biometric Security And Artificial Intelligence Cryptography And Security Protocols Image Processing And Computer Vision Authentication And Identification Bayesian Networks Fuzzy And Rough Set Mobile System Security Ubiquitous Computing Security Sensor And Mobile Ad Hoc Network Security Security In Social Networks Security For Web Services Security In Wireless Network Security For Grid Computing Security For Web Services Security For Personal Data And Databases Management Of Computing Security Intelligent Multimedia Security Service Computer Applications In Engineering And Technology Computer Control System Design Cad/Cam, Cae, Cim And Robotics Computer Applications In Knowledge-Based And Expert Systems Computer Applications In Information Technology And Communication Computer-Integrated Material Processing (Cimp) Computer-Aided Learning (Cal) Computer Modelling And Simulation Man-Machine Interface Software Engineering And Management Management Techniques And Methods Human Computer InteractionTopics covered include Computational Mathematics Data Science Computer Applications Information Systems Learning Science And Technology Network Architectures And Protocols Computer Network Education Computer Distance Learning Cloud Computing Cluster Computing Distributed Computing E-Commerce Protocols Automata Theory Game Theory. E-Health Biometric Security And Artificial Intelligence Cryptography And Security Protocols Authentication And Identification Modulation/Coding/Signal Processing Network Measurement And Management Bayesian Networks, Fuzzy And Rough Set Biometric Security And Artificial Intelligence Cryptography And Security Protocols Image Processing And Computer Vision Authentication And Identification Bayesian Networks Fuzzy And Rough Set Mobile System Security Ubiquitous Computing Security Sensor And Mobile Ad Hoc Network Security Security In Social Networks Security For Web Services Security In Wireless Network Security For Grid Computing Security For Web Services Security For Personal Data And Databases Management Of Computing Security Intelligent Multimedia Security Service Computer Applications In Engineering And Technology Computer Control System Design Cad/Cam, Cae, Cim And Robotics Computer Applications In Knowledge-Based And Expert Systems Computer Applications In Information Technology And Communication Computer-Integrated Material Processing (Cimp) Computer-Aided Learning (Cal) Computer Modelling And Simulation Man-Machine Interface Software Engineering And Management Management Techniques And Methods Human Computer Interaction
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 86 Documents
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Sentiment Classification of Football Supporters Using NusaBERT Embeddings, BiLSTM, and BiGRU Methods Guntara, Muhammad; Gusti Ayu Diah Gita Kartika Santi, I; Hairani, Hairani; Lalu Zazuli Azhar Mardedi
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 1 (2026): March 2026
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i1.6151

Abstract

Class imbalance and the use of non-standard language in football supporters’ opinions on social media constitute major obstacles to producing accurate sentiment classification for evaluating federation performance. This study aims to identify the most effective bidirectional recurrent architecture for capturing public opinion after applying data balancing techniques. Using a primary dataset of 1,039 instances (604 positive and 435 negative samples), the proposed method integrates a pre-trained NusaBERT model with hybrid Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) layers. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to the training data, with dataset partitioning using a stratified split ratio of 70:30. The results indicate that the NusaBERT-BiLSTM model achieves the best performance, with a testing accuracy of 70.83% and an F1-score of 0.6990, outperforming the BiGRU variant, which attains an accuracy of only 64.74%. Furthermore, NusaBERT-BiLSTM demonstrates greater reliability in detecting negative sentiment, achieving a recall value of 0.6336 compared to 0.4504 for BiGRU. In conclusion, combining NusaBERT's semantic strength with SMOTE-based balancing and BiLSTM layers significantly enhances the model’s sensitivity to minority opinions without causing data leakage. This study contributes a more objective classification model for national team management to accurately map public criticism and aspirations on social media.
Classification of Customer Opinions on the Quality of Cooperative Minimarket Services Using the Lexicon Approach Sholeh, Muhammad; Uniing Lestari; Dina Andayati
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 1 (2026): March 2026
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i1.6172

Abstract

Almost every strategic location today has a store that sells  daily necessities. These stores compete with each other by offering prices and services that they hope will satisfy their customers. This competition must be anticipated in the management of minimarkets run by cooperatives. Minimarkets run by cooperatives need to maintain the loyalty of their members and general customers by improving service quality. Customer reviews or suggestions and criticism from members or customers are valuable sources of data for evaluating service performance. These customer reviews are unstructured data that are difficult to process manually. This study aims to classify customer opinions on the service quality of cooperative minimarkets into positive, negative, and neutral sentiments using a Lexicon-Based approach. The research methods used are text data preprocessing, sentiment weighting using a lexicon dictionary, classification into positive, negative, or neutral classes, and system performance testing using a confusion matrix. The data labeling stage is carried out automatically using the Lexicon InSet dictionary to determine the sentiment class (positive or negative). The labeled data was then processed using TF-IDF feature extraction and used to train the logistic regression model. Model performance evaluation was carried out using a Confusion Matrix with a training data and test data ratio of 80:20. The results of this study show that the logistic regression algorithm is capable of classifying cooperative service sentiment with an accuracy rate of 81%, precision of 83%, recall of 81%, and an F1 score of 79%. These results indicate that the method used is quite effective in identifying customer opinions and can be used as a decision support system for cooperative managers in continuously improving service quality based on customer sentiment data analysis.  
Evaluation of Classification Methods for Predicting Junior High School Accreditation Ranks in Indonesia Jannah, Miftahul; Izati, Prajna Pramita
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 1 (2026): March 2026
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i1.6032

Abstract

In Indonesia, school accreditation is a crucial process for assessing the eligibility of educational institutions to meet national education standards. However, this process is resource-intensive and requires significant time, manpower, and financial resources. This study aimed to explore the application of machine learning classification methods: Random Forest, Boosting, and Support Vector Machine (SVM) to predict the accreditation ranks of Junior High Schools in Indonesia. The goal was to create an efficient, automated model to predict school accreditation status, improve the efficiency of the accreditation process, and facilitate better resource allocation. Data preparation included handling missing values, reducing the data dimensions, and addressing data imbalances. The dataset consisted of 23,954 Junior Schools from 34 provinces, with 37 variables, including 36 predictors and one target variable (accreditation status). The study found that Random Forest outperformed Boosting and SVM, with the highest Area Under Curve (AUC) of 0.8133. Random Forest also demonstrated the lowest average classification error of 19.32%, indicating its superior performance in predicting junior high school accreditation ranks. The results suggest that machine learning models, particularly Random Forest, can provide a more efficient and reliable alternative to manual accreditation evaluations. This approach can optimize educational assessments, improve resource allocation, and offer valuable insights for policymakers to enhance school performance, particularly in under-served regions.
A Comparison of Logistic Regression, Random Forest, and XGBoost Based on Feature Importance in Heart Failure Prediction M. Thoriq Panca Mukti; Hairani Hairani; Djoko Rahardjo; M. Rizki
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 2 (2026): September 2026 (In Press)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i2.6583

Abstract

Heart failure is a cardiovascular disease with a high mortality rate, requiring a prediction system capable of assisting in faster and more accurate early detection. This study aims to compare the performance of Logistic Regression, Random Forest, and XGBoost in predicting heart failure, with a focus on feature importance. The dataset is a public Kaggle dataset, consisting of 918 patient records with 11 features and 1 target attribute. The research stages include exploratory data analysis (EDA), data preprocessing, anomaly handling, label encoding, data standardization, model training, model evaluation, and feature importance analysis. Model evaluation was conducted using accuracy, precision, recall, and F1-score. The results indicate that Random Forest achieved the best performance, with an accuracy of 86.96%, a precision and recall of 88.24%, and an F1-score of 88.24%. Meanwhile, XGBoost achieved an accuracy of 85.87%, and Logistic Regression achieved 84.78%. The feature importance analysis revealed that the ST_Slope attribute was the most dominant feature across all three models in predicting heart failure. This study demonstrates that the Random Forest method provides superior classification performance compared to the other models, and feature importance analysis can aid in interpreting the clinical attributes that influence heart failure prediction.
A Comparative Analysis of Machine Learning Models Using PCA and Variance Threshold Approaches for Classifying Student Academic Success Djoko Rahardjo; Hairani Hairani; M. Thoriq Panca Mukti; M. Rizki
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 2 (2026): September 2026 (In Press)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i2.6585

Abstract

Predicting student academic success is an important problem in higher education due to the high risk of delayed graduation or dropout. However, the prediction process often faces challenges, such as an imbalanced class distribution, which can reduce the classification model’s performance. This study aims to evaluate and compare the performance of Logistic Regression, Random Forest, and Gradient Boosting algorithms in classifying student academic status into Graduate, Enrolled, and Dropout categories. The research method begins with data balancing using the Synthetic Minority Oversampling Technique (SMOTE) applied to the training data, followed by a comparison of two dimensionality reduction techniques: feature selection with Variance Threshold (VT) and feature extraction with Principal Component Analysis (PCA). Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results show that Random Forest without dimensionality reduction achieves the best performance, with an accuracy of 77.40% on SMOTE-balanced data. The application of SMOTE has also been shown to improve the balance of predictive capability across classes, as evidenced by higher F1-scores than with the original data, which is biased toward the majority class. Conversely, the use of PCA degrades model performance by reducing the information available to differentiate the classes. Furthermore, the Enrolled class is the most difficult to predict due to its high similarity in characteristics to the Graduate and Dropout classes. Based on these results, the combination of Random Forest and SMOTE, without dimensionality reduction, is the most effective approach for predicting students’ academic success. This model has the potential to be implemented as a component of an Early Warning System to support the identification of at-risk students and the implementation of earlier academic interventions.  
Enhanced Convolutional Recurrent Neural Network for Vertical Japanese Text Recognition Ronny Ronny; Sandy Kosasi; David David
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 2 (2026): September 2026 (In Press)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i2.6620

Abstract

Optical Character Recognition (OCR) plays a crucial role in text digitization, yet most existing research predominantly focuses on horizontal English or Latin text, leaving vertical Japanese text largely underexplored. This research addresses that gap by proposing an enhanced Convolutional Recurrent Neural Network (CRNN) model tailored for Japanese vertical text recognition. The novelty of this work lies in integrating residual connections and attention mechanisms into the CRNN, along with a custom dataset that includes both Japanese characters and 30% Latin characters, simulating real-world scenarios such as manga. Experimental results show that the proposed model achieves a Character Error Rate (CER) of 0.363% in only 10 epochs, compared to the baseline CRNN, which reaches 2.084% CER after 100 epochs, demonstrating both faster convergence and improved accuracy. Furthermore, evaluation on manga screenshots from the Manga109 dataset highlights the model’s practical potential, while also revealing new challenges related to irregular vertical spacing and font variability. These findings indicate that the proposed method significantly advances vertical Japanese text recognition and offers a foundation for future improvements in text recognition for manga and similar media.

Page 9 of 9 | Total Record : 86

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