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%.

ABSTRACT
The writing of this article is motivated by the problem of students who are less disciplined at school. The causal factors are fixed very variously. Consequently, this article aims to describe the management of student discipline development in SMK 1 Muhammadiyah. In writing, this article is a type of qualitative descriptive research. Data collection techniques using interviews, observation, and documentation. While the analysis tool is to uses data condensation, presenting data, and drawing and drawing conclusions. This study shows that the management of disciplinary development is contained in a corrective plan through steps that are applied to students with problems. Organizing is carried out in collaboration between BK teachers, student affairs, and homeroom teachers. Students often encounter implementation through socialization with various media or the right way. Supervision is carried out in two ways by the principal, namely field observations and receipt of weekly and monthly reports. Based on the explanation above, the role of school personnel in fostering student discipline at SMK 1 Muhammadiyah North Sumatra can primarily be categorized as "good" at this time. This research can be recommended to school personnel to pay more attention and monitor and provide discipline guidance to students to be more disciplined in school.
Keywords: student coaching, discipline management

Indonesia merupakan negara yang secara geografis, demografis, sosioekonomis dan politis merupakan kawasan yang rawan bencana, dan juga perpotensi mengalami bencana alam, non alam dan sosial, seperti bencana: gempa bumi, banjir, letusan gunung api, kebakaran, tanah longsor, wabah penyakit, kegagalan teknologi, konflik sosial, terorisme. Kejadian bencana akan mengakibatkan timbulnya kedarurtan, korban massal serta permasalahan kesehatan pada masyarakat, selain itu kejadian bencana juga mengakibatkan, rusaknya fasilitas umum, fasilitas kesehatan, terganggunya saluran komunikasi dan lain-lain yang akan mengganggu kehidupan dan penghidupan masyarakat. Banjir besar melanda wilayah kota Semarang pada pekan pertama dan kedua Februari 2021. Banjir di ibu kota Provinsi Jawa Tengah itu merendam setidaknya 43 titik pada 5-7 Februari lalu, demikian data Badan Penanggulangan Bencana Daerah (BPBD) Jawa Tengah. Selain banjir kiriman dan banjir lokal, banjir rob merendam sejumlah kawasan di Kota Semarang, seperti di Kelurahan Kaligawe (Kecamatan Gayamsari) dan wilayah di Kecamatan Genuk. Kondisi pandemic Covid-19 memperburuk dalam penanganan bencana yang terjadi.