Digital supply chains play an important role in improving the performance of small and medium enterprises (SMEs) in this digital era. There has been no research that analyzes the relationship between digital leadership, leadership, and partnerships. The aim of this research is to analyze the defect of digital supply chain implementation on logistics leadership and the impact of digital supply chain implementation on partnerships and logistics leadership partnerships. The method of this research is quantitative and data analysis uses structural equation modeling (SEM) partial least squares (PLS) using tools. SmartPLS 3.0 software data is used for processing the data. Research data is obtained by distributing online questionnaires to 589 SME owners in Indonesia determined using a simple random sampling method. The online questionnaire is designed using a Likert scale from 1 to 7 and distributed via social media. The stages of data analysis are validity testing, reliability testing and hypothetical testing. Based on the results of data analysis, it is concluded that digital supply chain implementation has a positive and significant effect on logistics leadership, digital supply chain implementation has a positive and significant effect on partnerships and logistics leadership had a positive and significant effect on partnerships. The novelty of this research is the creation of a correlation model for variable partnerships, logistics leadership and digital supply chain implementation. The managerial implication of this research is to encourage increased partnerships and logistics leadership and we conclude that SMES managers must implement digital supply chain implementation. The theoretical implication of this research is that a new correlation model of partnerships, logistics leadership and digital supply chain implementation in SMEs is created.

Skin conditions affect millions of people globally, with symptoms appearing in different body areas. Technological advancements have brought diverse data types, including situations where an image depicting a skin condition can be assigned multiple labels. The Classification Transformer (C-Tran) method, which utilizes transfer learning and transformers, was developed for multi-label classification. Recently, Google introduced a new dataset called SCIN (Skin Condition Image Network), which aims to provide diverse data on skin conditions. This research aimed to use the C-Tran method for the multi-label classification of skin conditions with the SCIN dataset while incorporating additional metadata inputs to improve the metric results. The results show that the multi-label classification process using metadata is far superior to the model without metadata. For example, In the mAP metric, models that utilized metadata scored 82.37, whereas models without metadata only scored 47.02. Similarly, models with metadata achieved 70.83% in the accuracy metric, while models without metadata achieved only 34.72%. Out of the 10,379 data points available with metadata in the SCIN dataset, only 718 were actually utilized for the classification task. It is thought that the inaccurate prediction outcomes are due to unreliable data, even with a confidence level of 4. In this analysis, two metadata categories stood out the most in terms of different measurements: the body part and symptoms metadata categories from the SCIN dataset. With just the body part and symptoms metadata groups, the mAP results achieved a 74.23%, accuracy at 63.89%, CF1 at 68.79%, and OF1 at 73.13%.