Journal of System and Computer Engineering

Sentiment Analysis in User Reviews of Gojek Application using Natural Language Processing

Silvia Putri Yuliani — 2025-10-30

Gojek, a leading proponent of on-demand services in Indonesia, has garnered a total of 142 million downloads. However, it has received the fewest reviews compared to other on-demand applications. The objective of this research is to identify sentiment in Gojek application user reviews on Google Playstore using Natural Language Processing (NLP) approaches and machine learning algorithms through the Orange platform. The reviews utilized in this study were collected in June 2025 and encompass a total of 3,615 data points, including 2,892 training data and 723 testing data. Sentiments are classified into two categories based on their ratings: positive (rating 4-5) and negative (rating 1-2). The research process is comprised of four primary stages: data collection and labeling, text pre-processing, feature transformation using TF-IDF, and testing five classification algorithms: neural network, naïve Bayes, random forest, decision tree, and k-nearest neighbors. The evaluation results indicate that the Neural Network model demonstrates optimal performance, exhibiting 93.20% accuracy, 93.00% F1-score, and 75.80% MCC. These findings suggest that the NLP approach can be utilized effectively to comprehend user perceptions of applications. It is anticipated that this research will assist Gojek developers in the monitoring and enhancement of service quality, with this enhancement being informed by user feedback.

A Deep Learning Approach to Respiratory Disease Classification Using Lung Sound Visualization for Telemedicine Applications

Andi Enal Wahyudi — 2025-10-30

This study presents the development of an intelligent system for the classification of respiratory diseases using lung sound visualizations and deep learning. A hybrid Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN–BiLSTM) model was designed to classify four conditions: asthma, bronchitis, tuberculosis, and normal (healthy). Lung sound recordings were converted into time-frequency representations (e.g., mel-spectrograms), enabling spatial-temporal feature extraction. The system achieved an overall classification accuracy of 99.5%, with F1-scores above 0.93 for all classes. The confusion matrix revealed minimal misclassifications, primarily between asthma and bronchitis. These results suggest that the proposed model can effectively support real-time, non-invasive respiratory screening, particularly in telemedicine environments. Future work includes clinical validation, integration of patient metadata, and adoption of transformer-based models to further enhance diagnostic performance.

Enhancing Human Activity Recognition with Attention-Based Stacked Sparse Autoencoders

Radus Batau — 2025-10-30

Comparison Analysis of Naive Bayes and K-Nearest Neighbor Algorithms in Classifying Language Styles in Indonesian Texts

Fika Tsalsabila Tinanda — 2025-10-31

In the digital era, Indonesian-language texts have rapidly proliferated across social media, online news, blogs, and digital documents, often containing various figurative language styles such as personification, metaphor, hyperbole, euphemism, and irony. Manual identification of these language styles is inefficient on a large scale, especially when class distribution is imbalanced. This study aims to compare the performance of the Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in classifying figurative language styles in Indonesian texts, and to evaluate the impact of applying the Synthetic Minority Over-sampling Technique (SMOTE) and hyperparameter tuning on model accuracy. The dataset consists of 5,155 original samples and 6,240 samples after SMOTE application, with an 80:20 train-test split. Evaluation was conducted under four scenarios: without SMOTE and without tuning, with SMOTE without tuning, without SMOTE with tuning, and with both SMOTE and tuning. The results show that Naïve Bayes demonstrated stable performance with an accuracy of up to 93.19%, while KNN achieved its highest accuracy of 93.43% after applying SMOTE and tuning. The implementation of SMOTE and hyperparameter tuning proved effective in improving accuracy, particularly for KNN. This study highlights the significant contribution of data balancing and parameter optimization in enhancing the automatic classification of figurative language styles in Indonesian texts.

Book Recommendation System Based on Collaborative Filtering: User-Based, Item-Based, and Singular Value Decomposition Analysis

Ishak Ishak — 2025-10-30

Recommender systems have become essential in the digital era to help users navigate overwhelming content. This study develops a book recommendation system using three collaborative filtering methods: user-based, item-based, and matrix factorization using singular value decomposition. We evaluate the system on a real-world dataset of 1,149,780 book ratings from 278,858 users across 271,360 books. A subset of 500 active users is used for experimental evaluation. The models are assessed using root mean square error and mean absolute error to measure rating prediction accuracy. The results show that the item-based collaborative filtering method achieves the best accuracy (root mean square error 7.362; mean absolute error 6.761), slightly outperforming the user-based approach (7.365; 6.809) and the matrix factorization method (7.643; 7.413). We analyze the results to understand the performance differences, noting the stability of item similarity as a key factor and the need for optimal tuning in the matrix factorization model. In conclusion, item-based collaborative filtering proved most effective for this context. This work provides insights into the comparative performance of foundational recommendation techniques and highlights practical considerations for improving book recommender systems.

Development of an Internet of Things (IoT) System for Real-Time Monitoring and Control of Moringa Powder Processing.

ROZALINA AMRAN — 2025-10-30

Moringa is a widely recognized food plant in Indonesia due to its numerous health benefits and availability across various regions. One of its processed forms is moringa leaf powder. However, the production process is relatively challenging, primarily due to limited human resources and the time-consuming nature of manual processing. With advancements in technology, these challenges can be addressed through the application of Internet of Things (IoT) systems in the production process. This study aims to design and implement an IoT-based monitoring and control information system using the waterfall development method, which includes the stages of requirements analysis, system design, implementation, testing, and evaluation. The resulting system integrates various sensors, devices, and a NodeMCU microcontroller to automate the production process. The system is connected to the Firebase platform and an Android application, enabling efficient monitoring and control. The primary components used include a DHT-11 temperature and humidity sensor, ultrasonic sensor, Loadcell sensor, MG996 servo motor, adapter, blender, and heating box. The results demonstrate that this system can serve as a modern, technology-based model for efficient moringa plant processing.