Purpose: Breast cancer remains a significant cause of mortality among women, requiring accurate diagnostic methods. Traditional classification models often face accuracy challenges due to missing values and irrelevant features. This investigation advances the classification of breast cancer through the amalgamation of the C4.5 algorithm with K-Nearest Neighbor (KNN) imputation and Relief feature selection methodologies, thereby augmenting data integrity and enhancing classification efficacy.
Methods: The Wisconsin Breast Cancer Database (WBCD) was the core reference for evaluating the proposed methodology. KNN imputation addressed missing values, while Relief selected the most relevant features. The C4.5 algorithm executed training by utilizing data segregations in the corresponding proportions of 70:30, 80:20, and 90:10, with its efficiency gauged through a range of metrics, particularly accuracy, precision, recall, and F1-score.
Result: This innovative methodology achieved the highest classification accuracy of 98.57%, surpassing several existing models. Particularly noteworthy, the strategy being analyzed exhibited remarkable success relative to PSO-C4.5 (96.49%), EBL-RBFNN (98.40%), Gaussian Naïve Bayes (97.50%), and t-SNE (98.20%), demonstrating associated advancements of 2.08%, 0.17%, 1.07%, and 0.37%. These results confirm its effectiveness in handling missing values and selecting relevant features.
Novelty: Unlike prior studies that addressed missing values and feature selection separately, this research integrates both techniques, enhancing classification accuracy and computational efficiency. The findings suggest that this approach provides a reliable breast cancer diagnosis method. Future work could explore deep learning integration and validation on larger datasets to improve generalizability.

Many countries are transitioning from conventional energy to renewable energy sources. This transformation requires support through socialization and training activities targeting all sectors of society, including educators in schools. The Al-Hidayah Waqaf Foundation in Thailand is an educational institution with educators who have limited knowledge regarding the implementation of renewable energy as part of energy diversification efforts. Collaborative activities were conducted with the Al-Hidayah Waqaf Foundation in Thailand through socialization and training on renewable energy. The outcomes of these activities provided educators with increased knowledge, information, and perspectives on renewable energy. 

This study aims to find the relationship between prospective biology teachers' ethnicity and teaching skills to preserve indigenous knowledge in Indonesia. The research intervention integrates the Science Integrated Learning (SIL) model with NGSS dimensions in the teaching practices of prospective biology teachers. The method chosen was embedded experimental with a quasi-experimental design consisting of three stages: a qualitative stage before, a qualitative stage during, and a qualitative stage after the intervention. The research targets were 60 prospective science teachers in two different cluster universities, each with 30 prospective teachers. Interpretation of quantitative and qualitative data in the form of statistical test results. The analysis of the comparison test using the t-statistic model obtained a difference in the average score of microteaching practice of 0.3, with an average of the first practice of 2,613 with a standard deviation of 0.3024 and the second practice of 2,921 with a standard deviation of 0.3122. The second microteaching practice experienced an increase in scores from the first practice. The study concluded that prospective biology teachers' skills in preserving indigenous Indonesian traditional knowledge can be realized through the SIL Model integrated with NGSS.  The research results allow prospective teachers to utilize students' experiences as learning sources and hopefully can contribute on the development of prospective teacher education policies oriented toward preserving Indigenous knowledge in Indonesia.