Internet data has grown very fast and becomes very large. Thus continuous improvement will always be required to face this challenge. The Sustainable Development Goals (SDGs) are defined by the United Nations (UN) to encourage improvements in the field of life in each country. We proposed a combination of Distributed System (RabbitMQ) and Machine Learning (Naïve Bayes Classifier) as one of the support to measure the achievement level of Sustainable Development Goals (SDGs) in Indonesia. The methods will categorize the Detik.com news into two classes; the relevant to SDGs of Indonesia and the irrelevant to SDGs of Indonesia. Our work shows that the use of the load-balance feature in RabbitMQ could shorten the processing time of the Naïve Bayes Classifier. RabbitMQ as a load-balancer can divide the workload equally, thus reducing the latency time of the Naïve Bayes Classifier classification process by 30.3 percent.

Dalam rangka meningkatkan keamanan, SMKS kesehatan Utama Insani memperkerjakan seorang security untuk memegang kendali keamanan siswa dan lingkungannya. Security akan bekerja memantau perilaku siswa dan memantau keamanan setiap ruang yang ada. Namun sistem keamanan yang ada tidak berjalan dengan efektif, dimana hal ini disebabkan karena keterbatasan seorang security yang terkadang masih melakukan kesalahan dan kelalaian dalam memantau keamanan siswa dan ruangan. Untuk membantu meringankan tugas security dibutuhkan sistem keamanan yang lebih canggih dan efektif. Dalam penelitian ini, sistem keamanan yang digunankan adalah keamanan pintu berbasis Arduino Uno menggunakan RFID (Radio Frequency Identification), dimana RFID sering juga dikenal dengan istilah sensor tag ID yang cukup terkenal dan sering digunakan dalam bidang elektronika mikrokontroler. RFID merupakan sensor yang mengidentifikasi suatu objek dengan menggunakan frekuensi radio.

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School attributes are a series of clothes and accessories that must be worn by students in the school environment. The implementation of this rule aims to create discipline in students. However, in practice, not all rules can be implemented properly because there are still students who violate these rules. One of the rules applied at school is the use of complete attributes. Currently, attribute checks in schools are done manually or through teacher supervision. However, this takes more time, is prone to errors, and is inefficient due to the large number of students being checked. This study proposes an improved YOLOv5 architecture with the replacement of the backbone to MobileNetV3s to detect school attributes. This method uses deep learning and the YOLOv5 algorithm to detect in real time the use of school attributes by students. In this study, the experimental results show that the enhanced YOLOv5 with MobileNetV3s has higher accuracy compared to the original YOLOv5. In addition, the improved model is more efficient in memory usage and weight file size. With an accuracy result of 0.912 on mAP50 and a weight size of about 90 MB and a memory usage of <7 GB, it shows the potential of replacing the backbone in this technology in overcoming attribute detection challenges in schools and can be applied in other cases. However, further research is needed to generalize these results to other problems. This research also shows that backbone replacement in YOLOv5 can affect the accuracy of the model.