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All Journal International Journal of Advances in Artificial Intelligence and Machine Learning
Misinem Misinem
Universitas Bina Darma, Palembang
Computer Science & IT

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A Comparative Evaluation of Predictive Models for Lung Cancer: Insights from Logistic Regression, Naive Bayes, and Random Forest Muhammad Hafiz Kurniawan; Misinem Misinem
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 1 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i1.378

Abstract

This study aims to evaluate the performance of three machine learning models-Logistic Regression, Naive Bayes, and Random Forest-in predicting lung cancer using a publicly available dataset from Kaggle. The data used included demographic information, risk factors, and diagnostic imaging features, with significant class imbalance between benign and malignant cases. To address this imbalance, the Synthetic Minority Sampling Technique (SMOTE) was applied. In addition, Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) were used for dimensionality reduction and feature selection to improve model performance. The results showed that Random Forest, especially when combined with PCA, outperformed the other models with the highest accuracy of 96.77% and a balanced F1 score of 0.50 for the minority class. Although Logistic Regression achieved high accuracy, it was less effective in predicting minority classes, especially when combined with RFE. Meanwhile, Naive Bayes showed moderate performance but was limited by the assumption of feature independence. The application of SMOTE significantly improved the model's ability to handle class imbalance, while PCA proved more effective than RFE in improving model performance. This study highlights the importance of selecting appropriate machine learning models and preprocessing techniques for lung cancer prediction. Random Forest, with its ability to model complex relationships and handle imbalanced data, emerged as the most effective model for this task. These findings underscore the potential of machine learning in medical diagnostics and provide valuable insights for future research.
Chatbot for Traditional Malay Museum Misinem Misinem; Hafiz Muhammad Kurniawan; Fadly Fadly; Muhammad Raihan Hanif
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 3 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i3.567

Abstract

Background of study: Museums play a key role in preserving cultural heritage and educating the public. In Malaysia and Indonesia, over 150 traditional Malay museums attract millions of visitors annually. Between 2015 and 2017, the Department of Museum Malaysia recorded 9.1 million visitors, while Indonesia reported over 12 million. Managing visitor inquiries manually poses challenges for museum staff, highlighting the need for an automated information system.Aims: This study aims to develop a chatbot for Muzium Adat to provide instant, accurate, and accessible information to visitors, thereby reducing dependence on staff and enhancing the visitor experience.Methods: A museum information chatbot was developed to answer frequently asked questions related to Muzium Adat. The system was tested through questionnaires distributed to 15 respondents to assess usability, accuracy, and satisfaction.Result: Findings showed that the chatbot achieved an 83.6% satisfaction and accuracy rate, indicating effective real-time interaction and reliable responses.Conclusion: The Muzium Adat chatbot improves communication efficiency, saves time, and enhances visitor engagement. It demonstrates the potential of chatbot technology to support museum operations and promote cultural heritage in Malaysia and Indonesia.
Co-Authors Fadly Fadly Hafiz Muhammad Kurniawan Muhammad Hafiz Kurniawan Muhammad Raihan Hanif