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Purpose: This paper aims to develop a coffee supply chain traceability system based on Blockchain (BC) and Machine Learning (ML) with the aim of ensuring the quality of coffee beans production. BC functions to ensure supply chain performance, while the ML model ensures product quality. Design/methodology/approach: Smart Contracts will be built on the Ethereum Virtual Machine BC network based on Ethereum. The ML model to identify good and bad green coffee beans will be built using different YOLO algorithms, which will go through training and validation stages, namely using the k-fold cross validation method. The ML model algorithm is based on Convolutional Neural Network (CNN) using YOLOv5m, YOLOv6m and YOLOv7. The best model will be chosen based on the results of cross-validation with test data in the form of coffee image data that the model has never seen (unseen data). The whole process of building the ML model is done on the Google Collab Pro+ Virtual Machine. Findings: YOLOv5m outperformed the other models in both non-augmented and augmented training datasets, highlighting the proficiency of YOLOv5m in managing compact datasets and its resilience in the face of data augmentation, positioning it as a prime selection for quality discernment tasks within the realm of green coffee beans. The smart contracts offer an all-encompassing approach for user management, monitoring product status, and presenting traceability data within the framework of coffee plantation administration. Originality/value: This research contributes to the development of blockchain network as a solution to implement traceability systems along coffee supply chains in Indonesia. Moreover, it shows that while blockchain can ensure the process of production along the coffee supply to follow certain guidelines, machine learning can verify whether the product that was produced by utilizing BC is of high quality/acceptable. [ABSTRACT FROM AUTHOR]

Several studies have focused on generating seismic vulnerability maps for earthquake-prone areas, particularly in Indonesia. Building typologies are a key factor in determining vulnerability to earthquakes. However, conducting large-scale field surveys to determine the spatial distribution of building typologies in a city is uneconomical. This paper explores the use of a convolutional neural network (CNN) to automatically detect building typologies from diverse regions in Indonesia, utilizing both conventional and automated building image acquisition processes. In this study, datasets from three distinct image acquisition methods are trained with four unique CNN architectures to identify the best-performing model to classify building typologies. The sample size effect on CNN performance is also investigated. The results showed that randomly sampled Google Street View (GSV) images are the most effective dataset for the CNN model, achieving an f1-score of 84.33%. Among the network architectures tested, MobileNet demonstrated superior performance on the majority of evaluated datasets. As the sample size increases by about 350% in the dataset, there is a positive correlation with up to 2.3% f1-score improvement. Using the best-performing CNN model, two building vulnerability models were employed to assess the spatial distribution of building damage in the urban area of Bandung, considering a hypothetical scenario of an M7 earthquake. Incorporating local construction data, one of the generated maps estimated that approximately 55% of buildings in Bandung would experience moderate to severe structural damage. This study showcases the potential of CNN models in automating regional seismic assessments and providing valuable insights for comprehensive seismic mitigation strategies. • A decision tree to label building typology objectively in Indonesia was developed. • CNN model trained with randomly sampled GSV dataset outperforms other models. • The CNN and probability model generated a city-scale building vulnerability map. [ABSTRACT FROM AUTHOR]