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Safety is one of the most crucial factors in transportation due to so many factors, including human-error, that might result in various small to fatal accidents. This is why lane keeping assist (LKA) is an important system that needs to be developed. LKA will prevent the vehicle from moving to another lane because the driver is negligent to maintain the direction of the vehicle.

In this study, LKA works using camera and ultrasonic sensors. The data coming from these two types of sensors are then fused to determine how should the car reacts to some certain situation so that it stays on the lane. The sensor fusion method was used to further ensure the car's safety.

The results of the hardware simulation show that LKA can successfully avoid the car from getting off the lane with safety index of 95% for high-speed mode and 100% for medium and low-speed mode; meanwhile on the use of ultrasonic sensor, the speed of the car is adjusted to the distance of the car's side disturbance.

This research has succeeded in simulating a sensor fusion–based LKA system.

In the context of overcoming malnutrition in elementary school children and increasing public awareness of this issue, the Indonesian Government has created a “Card for Healthy School Children” (KMS-AS) program in the form of a paper health card. However, currently, the KMS-AS record data are still written on paper, which is less effective in terms of the health process. An integrated measuring device and an online data-recording system are needed to promote children’s health and facilitate access and transfer of data from one place to another. This study aims to develop NFC and IoT-based KMS-AS using sensor fusion method.

The results of this study show that an integrated measuring device for weight, height, body temperature and Spo2 level can be connected with mobile and Web applications using IoT technology, facilitating data recording and monitoring of children’s nutritional status. The sensor fusion method was used for the classification of nutritional status and health status, based on the results of measurement tools. Near field communication (NFC) technology was used to facilitate user identification when making measurements.

The results show that KMS-AS can facilitate classification of nutritional status and children's health status. Measurement and classification data can be monitored via Web and mobile applications. The accuracy of height, weight, body temperature and Spo2 measurements was 98.21%, 98.59%, 98.93% and 98.93%, respectively.

In this research, the authors successfully produced a system using sensor fusion method for measuring body weight, height, temperature and Spo2 level, which is integrated and can be connected to mobile applications and the Web using the IoT and NFC.