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Jasmine : Journal of Intelligent Systems and Machine Learning
Published by Universitas Telkom
ISSN : -     EISSN : 31634788     DOI : https://doi.org/10.25124/jasmine
Core Subject :
JASMINE: Journal of Intelligent Systems and Machine Learning welcomes submissions covering a wide range of topics, including, but not limited to: Deep Learning and Pattern Analysis: Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), deep reinforcement learning, data mining, clustering, classification, anomaly detection. Computer Vision: Image processing, object recognition, scene understanding, image captioning. Natural Language Processing (NLP) and Recommender Systems: Text analysis, speech recognition, machine translation, sentiment analysis, personalization algorithms, collaborative filtering, content-based recommendations. Biomedical Engineering and Bioinformatics: AI/ML applications in medical diagnostics, drug discovery, personalized medicine, genomic data analysis, protein structure prediction, computational biology. Optimization Algorithms: Swarm intelligence, evolutionary computation, metaheuristics for complex problem-solving.
Arjuna Subject : -
Articles 13 Documents
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Comparison of SVM, Naive Bayes, and Logistic Regression for LinkedIn Reviews Sentiment Analysis Nadhilah Hazrati; Alam Rahmatulloh
JASMINE: Journal of Intelligent Systems and Machine Learning Vol. 1 No. 1 (2026)
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jasmine.v1i1.10214

Abstract

The rapid development of digital technology has transformed the way people search for jobs, with LinkedIn emerging as the world’s largest professional social media platform. Many users express their opinions about the application through reviews on the Google Play Store, reflecting both positive and negative sentiments regarding their experiences. This study aims to conduct sentiment analysis on LinkedIn user reviews by comparing three classification algorithms: Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression. The research process involves data collection, text preprocessing, feature extraction, and model evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that all three algorithms are capable of classifying sentiments effectively, with Logistic Regression achieving the best performance, obtaining an accuracy of 88.53%, a precision of 94% for negative reviews and 83% for positive reviews, as well as a recall of 84% for negative reviews and 94% for positive reviews. In comparison, SVM achieved an accuracy of 87.79%, while Naïve Bayes reached 83.44%. These findings highlight that Logistic Regression outperforms the other models in sentiment analysis of LinkedIn reviews, making it a reliable method for understanding user perceptions and supporting application improvement.
Hybrid Approach for Extractive Text Summarization of Indonesian News Articles using Machine Learning and Heuristic Features Aqeela Nashwa Naysilla; Anjeli Tedan; Samuel Karel Augusta Koesmendro
JASMINE: Journal of Intelligent Systems and Machine Learning 2026: Articles in Press
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jasmine.vi.10253

Abstract

The rapid growth of Indonesian digital news content highlights the need for effective automated summarization methods tailored to morphologically rich, low-resource languages. This study proposes a linguistically informed hybrid approach for extractive text summarization designed specifically for Indonesian language characteristics. The framework integrates machine learning classification with carefully engineered linguistic features to improve summary relevance while maintaining computational efficiency. The methodology combines Logistic Regression and TF-IDF vectorization with additional heuristic features, including positional weighting, keyword relevance, and sentence length scoring. The system is evaluated on a dataset of 750 Indonesian news documents (10,159 sentences) annotated by three linguistic experts and covering multiple news domains to evaluate cross-domain behavior. Experimental results show that the proposed approach achieves 82.53% classification accuracy with a classification F1-score of 0.640. The system also maintains high computational efficiency, requiring only 0.18 seconds per document with a compact 124 MB model size. Summarization quality evaluation further indicates competitive content preservation with a ROUGE-1 F1-score of 0.778. Compared to traditional rule-based baselines, the hybrid system provides a more balanced trade-off between effectiveness and efficiency. Despite these advantages, performance variation across different document structures indicates limitations in handling less structured content, suggesting the need for improved structural adaptability and cross-domain robustness. Overall, this work contributes a practical and linguistically tailored summarization framework that supports scalable deployment for Indonesian digital news processing.
Transfer Learning for Medical Waste Image Classification Using EfficientNet-B0 with 5-Fold Cross-Validation: English Reni Kartika Suwandi; Asep Saeppani; Irfan Fadil
JASMINE: Journal of Intelligent Systems and Machine Learning 2026: Articles in Press
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jasmine.vi.10267

Abstract

Medical waste management requires careful handling due to its potential to cause infection and environmental hazards when not properly treated. Manual classification of medical waste is time-consuming, highly dependent on human accuracy, and prone to error. This study employs a transfer learning approach using the EfficientNet-B0 architecture to automatically classify medical waste images. The dataset consists of 23 waste categories and undergoes preprocessing steps including data cleaning, resizing, normalization, and data augmentation. The model is initialized with ImageNet-pretrained weights and refined through fine-tuning. Experimental results demonstrate stable classification performance, achieving an average accuracy of 92.2%, precision of 94.1%, recall of 92.2%, and an F1-score of 92.1%. The results indicate that EfficientNet-B0 provides a competitive balance between classification performance and computational efficiency for medical waste image classification, particularly under limited data conditions. However, this study is limited by the size and scope of the dataset and the absence of real-world deployment evaluation, which may affect the generalizability of the results. Despite these limitations, the proposed approach offers a feasible basis for further research toward automated medical waste sorting systems.

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