Development of Healthcare Kiosk for Checking Heart Health

The main problem encountered nowadays in the health field, especially in health care is the growing number of population and the decreasing health facilities. In this regard, healthcare kiosk is used as an alternative to the health care facilities. Heart disease is a dangerous one which could threaten human life. Many people have died due to heart disease and the surgery itself is still very expensive. To analyze heart diseases, doctor usually takes a video of the heart movement using ultrasound equipment to distinguish between normal and abnormal case. The results of analysis vary depending on the accuracy and experience of each doctor so it is difficult to determine the actual situation. Therefore, a method using healthcare kiosk to check the heart health is needed to help doctor and improve the health care facilities. The aim of this research is to develop healthcare kiosk which can be used to check the heart health. This research method is divided into three main parts: firstly, preprocessing to clarify the quality of the image.In this section, the writers propose a Median High Boost Filter method which is a combined method of Median Filtering and High Boost Filtering. Secondly, segmentation is used to obtain local cavities of the heart. In this part, the writers propose using Triangle Equation that is a new method to be developed. Thirdly, classification using Partial Monte Carlo method and artificial neural network method; these methods are used to measure the area of the heart cavity and discover the possibility of cardiac abnormalities. Methods for detecting heart health are placed in the kiosk. Therefore, it is expected to facilitate and improve the healthcare facilities.Keywords: Healthcare kiosk, heart health, reprocessing, segmentation, classification

Sistem Kontrol Inverted Pendulum Pada Balancing Mobile Robot

Sistem kontrol merupakan suatu sistem yang menjadi pusat perhatian di bidang robotika. Dengan adanya sistem kontrol ini, robot bisa menjadi lebih cerdas dan canggih. Robot berpendulum ini akan berusaha menyetimbangkan sistemnya agar pendulum tetap tegak 900. Sebagai deteksi kemiringan antara robot dengan lantai digunakan sensor Sharp GP2D12 yang menghasilkan output tegangan analog. Data dari sensor ini akan diolah oleh Mikrokontroler ATMega 16, karena data yang dibawa oleh Sharp GP2D12 ini mempunyai noise, maka data harus difilter. Filter yang dipakai adalah Single Exponential Filtering yang bisa mereduksi noise secara optimal disamping juga mempunyai respon waktu yang cepat. Setelah selesai pemrosesan, hasil filter akan diolah dengan kontrol PID. Dengan sistem kontrol ini, robot mempunyai respon yang cepat untuk bergerak maju mundur searah dengan arah gerak jatuhnya pendulum. Prosentasi sistem keberhasilan PID ini adalah 90%

SISTEM KONTROL INVERTED PENDULUM PADA BALANCING MOBILE ROBOT

ABSTRAK Sistem kontrol merupakan suatu sistem yang menjadi pusat perhatian di bidang robotika. Dimana dengan adanya sistem kontrol ini, robot bisa menjadi lebih cerdas dan canggih. Pada proyek akhir ini akan dibangun sebuah sistem kontrol inverted pendulum pada mobile robot. Inverted pendulum adalah pendulum yang mempunyai titik berat diatas titik tumpunya. Balancing Robot ini selalu menyetimbangkan sistemnya agar pendulum tetap tegak. Sebagai deteksi kemiringan antara robot dengan lantai digunakan sensor Sharp GP2D12. Sensor ini mempunyai output tegangan analog. Data dari sensor ini akan diolah oleh Mikrokontroler ATMega 16. Karena data yang dibawa oleh Sharp GP2D12 ini mempunyai noise, maka data harus difilter. Filter yang dipakai adalah Single Exponential Filter yang bisa mereduksi noise secara optimal disamping juga mempunyai respon waktu yang cepat. Setelah selesai pemrosesan, data diolah dengan menggunakan kontrol PID. Dengan sistem kontrol ini, robot akan bergerak maju mundur searah dengan arah gerak jatuhnya pendulum. Prosentase keberhasilan kontrol PID pada robot ini adalah 90% dengan robot tidak pernah jatuh tetapi berosilasi kecil. Kata Kunci : Inverted Pendulum, Sharp GP2D12, Single Exponential Filter, PID, Balancing Robot

Simulation Strategic Positioning for Mobile Robot Roccer Wheels

Soccer robot is a combination of sports, robotics technology and multi agent system. The achieve goals in playing the ball, requires individual intelligence, and the ability of cooperation for individual skills. The success of a soccer robot team is influenced by the success of the robot player to enter the ball into the opponent's goal. In entering the ball into the opponent's goal needed an appropriate position and strategic. This research makes the design in finding a strategic position at the time of attack. The design divides the field into several small areas or the main grid, where each region gives different action. The position is said to be strategic if the robot passing success and has a fairly wide perspective against the goal. The strategic position is gained from the greatest opportunity of some of the decisive conditions. The calculation used is to use the Probability Naïve Bayes Classifier by taking the maximum value that serve as a strategic position. So this research resulted in a design in finding strategic position

Position Data Estimation System Based on Recognized Field Landmark Using Deep Neural Network for ERSOW Soccer Robot

One of the problems faced by soccer robots is how to find out the position of the robot itself and other robots on the field. A simple way to find out the robot's position is to use the odometry method. However, odometry is weak in accumulating position errors that reduce the accuracy of the moving robot's absolute position estimation and orientation. This paper presents a robot position data estimation system that is to be implemented on the ERSOW wheeled soccer robot. The robot can determine its position based on a unique landmark: an L-shaped line on the soccer robot field. We use a deep neural network method to recognize landmark L-shaped. Vision systems and deep learning inferences run on the Robot Operating System platform. After obtaining the distance of the robot to the L-shaped landmark, the robot's orientation and position relative to the field can be accurately determined based on the omnidirectional camera's perception. The results of the position estimation system in this study can be used to reduce position errors resulting from the odometry method. Based on the landmark L-shape recognition test results conducted on 641 datasets, the validation accuracy value is 0.806. The results of testing the robot position generated by vision obtained the largest error x about 2.32 cm and y about 1.99 cm