Smart waste management and recycling have become critical issues in urban planning and environmental sustainability due to the increasing volume of waste generated by modern societies. In this study, we investigated the performance of Support Vector Machine (SVM) and Neural Network (NN) methods in an Arduino-based waste sorting system. Our testing phase revealed exceptional performance, with SVM achieving an accuracy of 92% and NN achieving 96%, alongside perfect precision, recall, and F1-score metrics. The consistent True Positive (TP) results across all waste categories underscored the system's capability to accurately direct waste into correspondingcolored bins. These findings highlight the significance of automated waste management systems in promoting effective waste sorting practices and facilitating sustainable recycling efforts. Moreover, advancements in technology and machine learning algorithms offer promising prospects for further enhancing the efficiency and scalability of such systems, thereby contributing to a cleaner and healthier environment for future generations. Future research in smart waste management could focus on exploring additional machine learning algorithms, advanced sensor technologies, and Internet of Things integration. Investigating alternative algorithms beyond SVM and NN, adopting advanced sensors like hyperspectral imaging or lidar, and incorporating IoT devices for real-time monitoring could enhance waste sorting accuracy and scalability.

Film sentiment analysis is a process for evaluating a sentiment value that exists in film reviews, so that positive or negative responses from films can be identified. In this study, a sentiment analysis will be carried out on film reviews on IMBD. The analysis was carried out to find out which reviews were positive and negative from film critics. The method used to carry out sentiment analysis in this study is review analysis and processing with TF-IDF and a positive or negative prediction process based on reviews that have been processed using a logistic regression algorithm and support vector classification. The data to be used is film reviews on IMBD, which consists of 2000 data, which is divided into 1000 positive data and 1000 negative data. Which is where the data will be preprocessed first and split with a percentage of 70% training data and 30% testing data. In the prediction process using the logistic regression algorithm, obtaining a test accuracy of 80.61%. While the prediction process using the support vector classification algorithm obtains a test accuracy of 82.42%.