Purpose: The purpose of this study is to understand how using TikTok as a digital marketing platform can help Micro, Small, and Medium Enterprises (MSMEs) in Cicarimanah Village, Situraja District, Sumedang Regency to improve their product marketing. The study aims to see whether TikTok can increase brand awareness, customer interaction, and product sales. Methodology/approach: This research uses a quantitative approach. Data were collected through surveys, interviews, and direct observations of MSME owners who actively use TikTok for marketing their products. The study was conducted specifically in Cicarimanah Village, Situraja District, Sumedang Regency, and focused on local MSMEs that utilize TikTok as part of their marketing efforts. No specific software or brand tools were mentioned, but the TikTok platform was central in the data collection and analysis process. Results/findings: The findings show that using TikTok for digital marketing has a positive effect on MSMEs. It helps in increasing brand awareness, improving customer engagement, and boosting product sales. TikTok allows MSMEs to promote their products in a creative, interactive, and effective way. Conclusion: TikTok has strong potential as a marketing tool for UMKM in Cicarimanah. Its effective use requires digital skills, creative content, platform integration, and data-driven strategies to support brand growth and business development. Limitations: The study is limited to MSMEs in one village only, so the results may not represent MSMEs in other regions or sectors. Also, the study does not explore long-term impacts or compare TikTok with other digital marketing platforms Contribution: This research contributes to the field of digital marketing and entrepreneurship development, especially for rural MSMEs. It provides practical insights for small business owners, marketing practitioners, and policymakers on how social media particularly TikTok can be used as a low-cost and high-impact marketing tool to help MSMEs grow and reach wider audiences.

Purpose: Purpose: Aristolochia acuminata is a rare plant species of significant conservation value. However, the accurate classification of its parts, such as leaves, stems, and twigs, remains a challenge. This study aimed to develop a reliable classification model to support conservation efforts using Convolutional Neural Network (CNN) technology. Methodology/approach: A digital dataset was systematically collected from various parts of Aristolochia acuminata, forming the foundation for training a CNN-based classification model. To evaluate the model performance and determine the optimal training parameters, three experimental scenarios were conducted using 10, 100, and 200 training epochs. The impact of each training duration on the classification accuracy was analyzed. Results: The model trained with 200 epochs achieved the highest accuracy, outperforming those trained with 10 epochs (68.89%) and 100 epochs (86.67%). This suggests that a longer training period enables the model to learn the visual features of each plant part better, leading to improved classification performance. Conclusion: The results confirm the effectiveness of CNN in classifying the components of Aristolochia acuminata. Using 200 training epochs allowed for deeper feature learning without overfitting, proving optimal in this context. Limitations: This study was limited by the dataset size and the number of classes involved. Further expansion of the dataset and class categories could improve the generalizability of the model. Contribution: This study contributes to plant conservation technology by demonstrating how CNN and structured dataset collection can be applied to classify rare plant species, providing a valuable tool for biodiversity preservation.