SIBI (Sistem Isyarat Bahasa Indonesia) is a sign language established by the Government of Indonesia as a standard of communication that used in Special School. Hence, not everyone can understand the sign language, even though people have ability to hear. They also must be able to understand the sign language in order to conduct a communication. Currently, technology of recognition has been applied to hand gestures that can translate sign language. Various studies regarding sign language recognition have been developed and there are many applications that can recognize sign language through hand gesture as datasets using webcam, but there still restricted on the datasets in SIBI. Therefore, the study aims to create SIBI datasets via hand gesture using experimental method. So that, the application can be used by SIBI users. SIBI datasets were successfully created with signs that consisting of 8 static word. Based on the confusion matrix, all the signs that has been created have a precision of 100%, meanwhile based on the test results with sufficient lighting conditions and plain background, SIBI datasets has a prediction success rate of 97,5%.

This research explained about the learning module development of Wearable Computing for IOT which was conducted in XI class of Engineering Electronics Industry at SMK Negeri 1 Cimahi which is one of the Vocational Secondary education Institutions in Cimahi City, West Java, Indonesia. This research focused on several aspects such as the project-based learning module development with ADDIE model, teachers' and students' feedback towards this module. This research involved teachers, four students to get involved in limited test, and 32 students who expressed their feedback regarding the module. The data analysis of students' response questionnaire was done with the Mann Whitney's U Test and Kruskal-Wallis to compare students' responses which were divided into several categories. This research produced the learning module of WearableComputing for IoT, the contents of this module has been quite detailed and reliable based on teachers' point of view, and 94% (n = 32) of students believed that the module is able to be used in the classroom, and limited test resulted the experiment that suitable with the module objectives. Thus, to maximize the use of learning module of Wearable Computing for IOT in the classroom, students might need to improve creativity in terms of the development of each experiment and teachers can use the module as a source of learning with managing to do a development.

Sinusitis is inflammation of the cavities around the nose, caused by viruses or allergic reactions of bacteria, fungi, and ARI. Other influential factors are polluting the environment, cold air, and dry. There is no specific device that can help sinusitis sufferers in monitoring air condition. This research was developed to be easy to use for people with sinusitis to be able to display temperature and humidity in real-time using a BME280 sensor and providing action in the form of a humidifier when the humidity is below the set point. The trial results show that the data sent has been successfully received by the output and can turn on the humidifier. From the measurement data obtained, 2,571% error for temperature measurement and 5,898% for humidity measurement. The results of interviews with participants found that this device can help sufferers of sinusitis as a preventive measure if the air condition is not good.