This paper explores clickbait detection using Transformer models, specifically IndoBERT and RoBERTa. The objective is to leverage the models specifically for clickbait detection accuracy by employing balancing and augmentation techniques on the dataset. The research demonstrates the benefit of balancing techniques in improving model performance. Additionally, data augmentation techniques also improved the performance of RoBERTa. However, it resulted differently for IndoBERT with slightly decreased performance. These findings underline the importance of considering model selection and dataset characteristics when applying augmentation. Based on the result, IndoBERT, with a balanced distribution, outperformed the previous study and the other models used in this research. Furthermore, by incorporating balancing and augmentation techniques, the research surpasses previous studies, contributing to the advancement of clickbait detection accuracy. This work highlights the value of leveraging pre-trained Transformer models and specific dataset-handling techniques. The implications include the necessity of dataset balancing for accurate detection and the varying impact of augmentation on different models. These insights aid researchers and practitioners in making informed decisions for clickbait detection tasks, benefiting content moderation, online user experience, and information reliability. The study emphasizes the significance of utilizing state-of-the-art models and tailored approaches to improve clickbait detection performance.

Disaster is a sudden event, such as an accident or a natural catastrophe, that causes great damage or loss of life. Disasters can occur at any time, therefore a mechanism is needed to evacuate out of the building during a disaster. Generally public facilities have provided instructions and evacuation routes outside the building. Based on the severity of the disaster and the evacuation capability of the victim, the evacuation strategy can range from evacuation as soon as possible, evacuate slowly, move to a safe location inside the building or take refuge in the available protection room and wait for the rescue team to arrive. The algorithm for finding the shortest paths can be used to determine the evacuation route. But this path is still static, if the route damaged in then the evacuation route would become useless. The time for the evacuation process can also increase if the condition of the evacuation route is not known whether damaged or not. The solution to solve this problem is to make a system that can help find  the safest and shortest evacuation routes during emergencies. This system consist of microcontroller Arduino Mega to control the system and led for evacuation sign. The evacuation routes is determined by implementing dijkstra algorithm with priority queue to search the shortest path.

Air Conditioners (AC) are increasingly used to get the room temperature as desired, starting from home use to keep the room cool and enjoyable, to specific room like server rooms or ATM which are focused on keeping the room cool in order to keep the equipment cool. The role of AC is quite important to maintain room temperature, and its increasing use has led to the growing need for users to control the AC. The Internet of Things allows users to remotely control air conditioners using the gadgets used and get real time room temperature information. The AC control system based on Internet of Things (IoT) utilizes an internet connection to monitor room temperature and control AC remotely. The devices used are DHT11 as a temperature sensor to get room temperature, Infrared Receiver to read the code sent by the remote AC, Infrared Transmitter to send commands to the AC in the room, and ESP8266 as a microcontroller and a link to the internet. The IoT platform used is Blynk which has the ability to access the microcontroller from the user's gadget. The tests are running on room air conditioners such as the Panasonic CS-PC18PKP series, the Panasonic CS-YN18TKP series and the Samsung AR09TGHQASINSE series. The test results showed that the room air conditioner was successfully controlled, and the room temperature was read in real time via an android smartphone.