This study aims to optimize the implementation of the K-Nearest Neighbors (K-NN) algorithm for medical image classification by focusing on selecting the optimal KKK parameter and applying dimensionality reduction techniques to improve accuracy and efficiency. The data used was sourced from public medical image repositories such as The Cancer Imaging Archive (TCIA) and Medical Image Analysis datasets, covering various diseases, including brain tumors, lung cancer, and kidney lesions. The research process involves data collection, data preprocessing, dimensionality reduction using Principal Component Analysis (PCA), applying the K-NN algorithm with Euclidean, Minkowski, and Cosine distance metrics, and performance evaluation using accuracy, precision, recall, and F1-score. Experimental results demonstrate that K=5with the Euclidean distance metric provides the best performance, achieving an accuracy of 90%. Additionally, PCA effectively reduces computational time by 30% without significantly compromising accuracy. This study proves that K-NN is an effective method for medical image classification. However, further research is needed to integrate K-NN with deep learning models to enhance performance and feature extraction capabilities.

Sinar Bhakti Village, located in Ogan Komering Ulu Regency, holds significant potential in rubber plantation management. However, suboptimal rubber tapping techniques often pose a major challenge for beginner farmers, resulting in low production yields and damage to rubber trees. This community service program aims to empower beginner farmers through training on optimal rubber tapping techniques. The method used was a participatory approach with hands-on field training. The training was conducted on Saturday, January 19, 2025, and included the introduction of tapping tools, proper tapping techniques, and post-tapping tree care. The activity involved beginner farmers in Sinar Bhakti Village. The results indicate that the training successfully enhanced participants' understanding of proper rubber tapping techniques. All participants effectively applied the techniques during field simulations. Additionally, participants demonstrated increased confidence in managing rubber plantations independently. This program positively impacted not only rubber productivity but also the economic sustainability of the village community.