Banjongkan, Anupong

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Banjongkan, Anupong
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anupong.ba@kmitl.ac.th

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    A Comparative Study of NLP-Based Models: Popularity Trends in Pop Mart Comments
    (2026-05-04)
    Charoenporn, Pattama
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    Poonsawat, Kititinun
    ;
    Banjongkan, Anupong
    In today's digital era, where consumer sentiment significantly influences brand perception, understanding customer emotions has become a strategic priority for companies like POP MART. Among its iconic collectibles, Labubu stands out as a symbol of the brand's global appeal, gaining tremendous popularity through the viral influence of Lisa from BLACKPINK, who showcased her Labubu collection on social media. This moment sparked a surge in interest, especially in Thailand, where Labubu achieved over 365,000 mentions in a single month, driven by its appeal to young, trend-conscious consumers. This study, Sentiment Analysis of LABUBU Popularity Trends: A Study of POPMART Comments Using NLP Techniques, investigates customer perceptions and emotions toward Labubu by employing advanced transformer-based NLP models - WangchanBERTa, RoBERTa, and XLM-R. By analyzing Thai-specific and multilingual datasets collected from social media platforms, this research evaluates the models' performance in sentiment classification. The analysis highlights WangchanBERTa's superior performance on Thai-specific datasets, achieving the highest F1-score of 87.51%. XLM-R demonstrated robust multilingual capabilities with an F1-score of 84.65%, while RoBERTa excelled in English sentiment analysis with an F1-score of 82.83%. These findings emphasize the importance of leveraging both language-specific and multilingual models to address cultural and linguistic nuances effectively. Sentiment patterns reveal positive comments highlighting emotional connections to Labubu, neutral sentiments about logistical inquiries, and negative feedback on technical and availability issues. These insights guide strategies to enhance marketing, boost customer engagement, and address operational challenges. This study advances NLP applications in sentiment analysis, offering POP MART data-driven insights to refine global and regional strategies while underscoring the value of multilingual transformer models in analyzing diverse consumer feedback.
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    Hybrid Deep Learning Framework for Accurate Surface Defect Detection Using Autoencoder and CNNs
    (2026-01-01)
    Craypo, Niphat
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    Banjongkan, Anupong
    ;
    Hanskunatai, Anantaporn
    Surface defect detection is paramount in industrial quality control. Conventional methods, which often rely on human inspection or manually engineered statistical models, frequently fail to accurately detect and classify defects, particularly on complex surfaces or those with intricate features. Human inspection is inherently inconsistent and prone to errors due to fatigue, while traditional machine vision systems often lack the sensitivity to clearly identify small or low-contrast defects. This paper proposes a hybrid deep learning framework, termed Autoencoders-Convolutional Neural Networks (AE-CNNs), DefectNet, for surface defect classification, AE, and CNNs to enhance both accuracy and efficiency. The AE is employed to extract and compress reliable features from surface images into latent representations, which are subsequently classified by a CNN enhanced through transfer learning using InceptionV3. The CNN is fine-tuned from a pretrained model with customized fully connected layers to adapt to specific defect characteristics, while the AE is trained exclusively on non-defective images. The encoded features produced by the AE serve as the input to the CNN. The proposed model is evaluated on standard benchmark datasets comprising diverse surface defect types and compared against Anomaly Detection with Autoencoder (ADA), Visual Geometry Group (VGG16), Inception-based Convolutional Neural Network Long Short-Term Memory (In-CNNLSTM), and DTL_Inception_v3. Experimental results demonstrate the superior performance of the proposed method, achieving classification accuracies ranging from 85.60% to 100% across five datasets, including a perfect 100% accuracy on the glass bottle neck dataset.