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Fajar Pitarsi Dharma

As seen in: Taylor & Francis Online
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Articles

Fabric defect classification in textile manufacturing: enhancing CNN performance through hierarchical cross-stage attention using the ScienceDB dataset

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By Fajar Pitarsi Dharma, Moses Laksono Singgih, Dedy Dwi Prastyo
| Taylor & Francis Online
ABSTRACT Fabric defect classification has a significant impact on textile manufacturing efficiency and product quality. Traditional convolutional neural networks (CNNs) rely on static methods, limiting their ability to detect subtle fabric defects. This study proposes a novel Hierarchical Cross-Stage Attention (HC_CSA) model, which features dynamic multi-scale feature integration to enhance defect classification accuracy.
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AI, Machine Learning and Deep Learning a Security Perspective

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By Moses Laksono Singgih, Fajar Pitarsi Dharma, Hamdan S. Bintang
| Taylor & Francis Online
In recent times, the utilization of machine learning (Al-Anqoudi et al. Citation2021; Majumder, Gupta, and Paul Citation2022), artificial intelligence, and deep learning (Batool and Khan Citation2022) has profoundly disrupted various industries and technological domains. This pertains not only to industrial implementations (Hidayatno, Destyanto, and Hulu Citation2019) but also to the potential setback in knowledge acquisition and the diminished productivity of both individuals and groups.
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