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All Journal International Journal of Advances in Artificial Intelligence and Machine Learning
Fadly Fadly
Politeknik Kesehatan Kemenkes Palembang
Computer Science & IT

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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.
Out of Distribution Detection for Reliable Deployment of Machine Learning Models William Yeoh; Fadly Fadly; Maria Ulfa
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 2 (2026): 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.v3i2.865

Abstract

Background: Machine learning models can give confident predictions when they receive inputs that are different from the data used during training. These predictions may not always be reliable when the model encounters unfamiliar inputs. OOD detection can help identify such inputs and reduce the risk of incorrect predictions in real-world applications.Aims: This study evaluates several OOD detection methods and examines their ability to reduce unreliable automatic predictions.Methods: Several post hoc detection methods were evaluated using ResNet classifiers trained on CIFAR datasets. The methods included Maximum Softmax Probability, ODIN, Energy-Based Detection, Mahalanobis Distance, Deep k Nearest Neighbors, and ReAct. The evaluation covered near-OOD, far-OOD, and corrupted-in-distribution conditions. Detection performance was also examined together with prediction errors, review needs, processing time, and memory use.Results: The methods showed different performance across the tested conditions. Deep k-Nearest Neighbors performed well for near OOD inputs, while ReAct showed strong performance for far OOD detection. The deployment simulation showed that ReAct-based gating reduced OOD leakage and improved the accuracy of predictions that were processed automatically. It also reduced incorrect predictions made without additional review. Energy-based detection provided competitive results with relatively low computational requirements.Conclusion: OOD detection can improve the reliability of machine learning systems. When combined with abstention and fallback mechanisms, unfamiliar inputs can be identified and sent for further review. This approach reduces unreliable predictions while allowing familiar inputs to continue through the automated process.
Co-Authors Hafiz Muhammad Kurniawan Maria Ulfa Misinem Misinem Muhammad Raihan Hanif William Yeoh