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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Hendrata, Ferial
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Computer Science & IT

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Uncovering Blockchain's Potential for Supply Chain Transparency: Qualitative Study on the Fashion Industry Hindarto, Djarot; Alim, Syariful; Hendrata, Ferial
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13590

Abstract

With the capacity to increase security and transparency, blockchain technology is being used as an interesting subject of investigation in the fashion industry. This underscores the importance of this current research endeavour. In terms of supply chain transparency, the fashion industry faces considerable barriers, thus requiring new approaches such as blockchain that can address issues such as child labour, unethical payment practices, and environmental impact. Main objective of this research is to identify how blockchain technology can improve transparency, accountability, and compliance with ethical standards. However, knowledge of the specific ways in which blockchain technology can improve transparency in the fashion supply chain, including the drivers and barriers, needs to be improved. The research method is described through a qualitative approach that includes in-depth interviews, participatory observation, and document analysis to collect data from various stakeholders in the industry, including manufacturers, distributors, and consumers. Explanation provides an overview of how the researcher collected and analysed data to achieve the research objectives. Blockchain increases transparency through the provision of verifiable and durable product records and fosters consumer-brand trust. Blockchain facilitates accountability and compliance with environmental and ethical standards, according to key findings. Research detected significant barriers, including exorbitant costs for implementation, limited knowledge of technology, and difficulties in fostering collaboration among relevant parties. Results of this study have far-reaching consequences, providing valuable insights to fashion industry stakeholders on how to overcome barriers to blockchain adoption. Long-term benefits of enhanced supply chain transparency and strategic recommendations ensure a smooth implementation process.
Development of Machine Learning Model for Breast Cancer Prediction from Ultrasound Images Hindarto, Djarot; Hendrata, Ferial
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13593

Abstract

In the past decade, the revolution in information and computing technology has transformed approaches to breast cancer detection and treatment, with Machine Learning technologies offering significant potential in health data analysis. However, the development of accurate and reliable predictive models is faced with the challenges of data heterogeneity and complexity. This research proposes the development and validation of Machine Learning-based classification models using Support Vector Machine and Principal Component Analysis to address these issues, targeting improved accuracy in the early detection of breast cancer. The methodology applied involved the use of a breast cancer dataset from Kaggle, with data analysis conducted through inductive methods to identify relevant patterns. The combination of Support Vector Machine and Principal component Analysis achieved 89% accuracy in medical image classification, proving its efficacy in breast cancer diagnostics and providing a more reliable model for early detection. The implications of these findings are significant, both theoretically and practically, for the fields of Machine Learning and Breast Cancer, expanding the understanding of the applications of advanced data processing techniques. Although this study faces limitations in the variability of the dataset's patient characteristics, the results offer a basis for further development in diagnostic technology while recommending the integration of Deep Learning and Big Data analysis as a direction for future research.
Co-Authors Alim, Syariful Hindarto, Djarot