In this research, a prosthetic robot hand that features a lever-based finger movement mechanism is proposed as the new approach to the T-FLoW 3.0 humanoid robot’s hand development. The proposed approach performs both grasping and releasing movements by pushing or pulling the finger-attached lever. The lever is pushed or pulled by micro-servo, which uses a stiff bar to transfer the force from the servo-horn to the finger’s lever. Our prosthetic robot hand is equipped with six joints, six SG92R micro-servos as actuators, and six force-sensitive resistors (FSR) as grasping feedback. 3D printing manufacturing technology is utilized to give the hand a realistic appearance, and PLA filament material is used in the manufacturing process to provide low-cost, lightweight, and easy maintenance. Static structural analysis simulation result lead to the conclusion that our prosthetic robot hand could sustain a load of around 30N. With the lever-based finger movement mechanism, the proposed approach is expected to overcome the mechanical slip issues from finger movements, which are often experienced in the old approach of the T-FLoW 3.0 humanoid robot’s hand development.

Autonomous navigation is one of the important functions of the Healthcare Robot to produce obstacle-free movements in the social environment inhabited by humans. In carrying out its duties, the robot will do a lot of navigation from the origin to the destination. Healthcare Robot uses a Laser Range Finder to detect objects around the robot. The results of detection are distance and angle data from the object. Then, the data is used as input for the Fuzzy Inference System (FIS) process to produce an appropriate gain value to control static and dynamic force of the Social Force Model (SFM). The parameters of the SFM influence the robot's response to the detected object. To obtain the optimal gain value, the FIS is used to change the parameters adaptively. Adaptive parameters are used to prevent the robot from making unexpected navigational behavior that may be dangerous, threatening to others, and potentially self-destructive. From the tests carried out in two conditions, the robot successfully navigated from its initial position to the goal and was able to respond to objects around it with the percentage of success in all scenes was 79.9%.

A robot that can move independently is an essential aspect towards replacing humans in hazardous work conditions. This paper shows the development of a mobile robot called ROISC-v1.0 (Robotic Disinfectant), which executes the sterilizing procedure in the room using UV light with a wavelength of 222nm. The goal of this study is to create a wall-following navigation system with obstacle avoidance capabilities. The modeling of the behavior-based control method is used in the application of a navigation system, including wall following and obstacle avoidance so that the mobile robot can modify linear and angular speeds based on the course of motion. Behavior-based control is used to eliminate the robot's reliance on its work area conditioning. To identify the distance between the robot and the wall, as well as the existence of obstacles in the robot's work area, the ROISC-v1.0 robot uses array of 12 Lidar sensors type VL53L0X. As a result, the robot navigates successfully to follow the contours of the wall and avoiding static and dynamic obstacles. When there are no obstacles in the way, the ROISC-v1.0 robot can perform optimally and efficiently in a 4.5m x 2.8m work area with an average robot travel time of 73.4 seconds and an average robot distance of 880.4 cm. With an average travel time of 109 seconds and a distance of 1052.4 cm, the robot can perform optimally and efficiently in regions where there are obstacles. The VL53L0X ToF sensor, which uses light waves in the transmission process and has an average inaccuracy of 0.7cm, allows the robot to read bright objects more accurately. The ROISC-v1.0 robot is hoped to aid medical professionals as the result of this research, minimizing the impact of virus dissemination caused by the sterilization process.

Prevention of the spread of disease is the most critical effort before the domino effect occurs. One way to prevent its spread is to practice social distancing. Prevention of the spread of covid-19 can be easier by doing Contact Tracing. Contact tracing is done to find out who is infected. This paper proposes an accurate and energy-efficient tracing system based on an android application by utilizing Bluetooth Low Energy (BLE) signals from smartphones to automatically detect possible contacts between users. Using BLE level signals, each app estimates its distance to its opponent. However, calculating the approximate distance with a BLE level signal cannot yield a sufficiently accurate number. Therefore, the prototype of the contact tracing system implements zoning based on the signal level. The results show that the Received Signal Strength Indicator (RSSI) threshold at one meter is accurate for tracing, provided that the devices share the same chipset. Moreover, it is pretty energy-efficient to be installed on a user’s Android smartphone because it only requires 14.17 mAh for 6 hours of operation or the equivalent of 2.14 mAh per hour.

Nowadays, wireless sensor networks (wsn) have many implementations in agriculture, animal husbandry, education, and many more. To support the lifetime of wsn nodes to achieve battery-saving protocol, we propose the head cluster method. This protocol will reduce the power consumption in data transmission by using the concept of head cluster and cluster member. In this method, communication using the cluster head is only carried out by the group head to save energy consumed. Based on the simulation results, the network lifetime with the cluster head method can be increased compared to a single hop.

Robots are a tool that is widely developed today, such as Humanoid, Animal, and others. In this study we discussed about animal robots. One such type of robot is Quadruped Robot. The problem that often arises in quadruped robots is that when performing stunts to be able to walk up or climb a ladder, the robot will not be able to walk with the posture adjusting the slope in the arena. This is due to the effect of earth's gravitational force that results in robots always being attracted to earth. This results in the robot's body losing balance and can accelerate damage to the servo motor due to the robot's unbalanced load. With this problem, this paper makes the control system with Fuzzy Logic place the load position in the middle of the COG (center of gravity) to balance the robot on the trajectory. The balance of the robot uses the IMU (Inertia Measurement Unit) position sensor reference with the reference derived from the angle slope (Yaw, Pitch and Roll) which is processed to adjust changes in the x, y and z axes, so that the robot can adapt to the trajectory of the stairs.

Wireless Body Area Network (WBAN) is a health service that consists of small sensors attached to the patient's body to improve health services. Apart from the importance and necessity of the WBAN, there are some problems in WBAN such as inaccurate measurements, hardware failures, discharged sensors, and sensors running out of energy which can cause false alarms and reduce confidence in using the WBAN system for remote health services. This research aims to create a system or modelling to solve anomalous data problems in WBAN health data. The system will process data to produce an analysis of information in an anomalous condition by applying the statistical method using Mahalanobis Distance and the prediction method using SMOreg. The process of collecting data and pre-processing is carried out before completing the detection of anomalous features. The data normalization process is carried out to make the process faster. This approach was tested on a real health dataset on the WBAN sensor. The results of the implementation show that the method produced a high-performance detection ratio (DR), which is above 80%. In addition, the experiment resulted that the size of the sliding window affected the anomaly detection results.

This paper presents the use of the Adaptive Boosting and Cascade Classifier based method to detect someone wearing a mask or not on various facial poses. In general, masks are used to protect the nose and mouth to prevent dirt or bacteria or virus from entering the respiratory tract Simply stated, a person is said to use a mask well if the nose and mouth areas are not visually visible on the face. OpenCV has already provided a model for human face detection for both frontal and profile faces. These models are good for detecting human frontal faces and profile faces, but not to detect people with masks. To deals with multi-pose people's faces using masks, we have to train new nose and mouth-based Adaptive Boosting and Cascade Classifies based model on various face poses independently to achieve our expectation. The Caspeal face database and AISL face database are used to train the Haar-like feature, LBP feature, and HOG feature based Adaptive Boostings and Cascade Classifiers. Based on the results of experiments, Haar-like feature outperforms LBP feature and HOG feature by achieving the best detection accuracy of 86.9%. On the other hand, LBP features outperforms Haar- like feature and HOG feature in computation time by achieving less than 30 msec from image loading until showing the detection result.

Virtualization and Software-defined Networking (SDN) are emerging technologies that play a major role in cloud computing. Cloud computing provides efficient utilization, high performance, and resource availability on demand. However, virtualization environments are vulnerable to various types of intrusion attacks that involve installing malicious software and denial of services (DoS) attacks. Utilizing SDN technology, makes the idea of SDN-based security applications attractive in the fight against DoS attacks. Network intrusion detection system (IDS) which is used to perform network traffic analysis as a detection system implemented on SDN networks to protect virtualization servers from HTTP DoS attacks. The experimental results show that SDN-based IDS is able to detect and mitigate HTTP DoS attacks effectively.

The safety and comfort of a vehicle is very important when driving. One of the security and convenience that can be achieved is to maintain the stable condition of the vehicle when it turns. For this reason a prototype was designed with a control method to control the condition of turning a vehicle. In this paper electronic device system is designed to control turn conditions using the fuzzy logic method, where fuzzy logic processes data error yaw rates and turning angles obtained from the IMU sensor. The system was applied to a three-wheeled vehicle model with a two-wheeled steering system. Based on testing with variations in turning angles and a fixed speed of 20 km / h, the largest yaw rate occurs at a 30 degree turning angle which is 0.2355 rad / s and after being given control of the yaw rate value to 0.03901 rad / s a yaw rate value reduction of 1 , 5943 rad / s. Testing with variations in vehicle speed and fixed turn angle of 10 degrees produces a yaw rate of 0.1314 rad / s and after the control is given to 0.03892 rad / s, a reduction of 0.09248 rad / s occurs. From these tests fuzzy logic is capable of improve vehicle response by minimizing slippage that occurs when the vehicle makes a turn movement so that the three-wheeled vehicle is maintained stability and security.

This work proposes building knowledge for gesture 3D Modeling Javanese Dance by using motion capture technique from the original dancer. The capturing process will record all the rotation value of the dancing motion that represent by each skeleton. A sequence of data will created by system. For the result, we made skeleton of 3D model and get the data gesture using motion capture to build an interactive learning media of traditional Javanese Dance.

The important thing that should be consist on delivery drone is landing speed and dropping object with smoothly. For the two cables and two motor pulley system, balancing control are use to balancing the cable that carrying the gripper, while the speed on landing system are controlled. The main advantage to build the system are using balancing PID controller with cable length input and speed control to makes the pulley automatically landing and pulling the object. The object can be land smoothly with speed control, and the cable will be in stable parallel position when the pulley are rotating with balancing control. To detect the object height, we use the proximity sensor and calibrating with rotary encoder. In this paper, we have installed the speed and balancing control on the two cables pulley-gripper system for delivery drone. The final result, the pulley can be land the box smoothly from 4 - 5 meters distance and stable the cable distance with error less than 2 cm respectively.

Detection and tracking of a moving target in a crowded area using camera surveillance is still an open topic that to be resolved. Determining a single target instance known among other objects in the room becomes a problem. We propose a color-based human tracking algorithm by considering the correlation distance from the color histogram used as the target selection. Furthermore, we also create a new dataset based on the INRIA dataset to minimize the detection area. Then, we apply the HOG extraction feature to the human detection system. We apply the Recall-Precision curve to evaluate the performance. The accuracy and precision rate has increased by 10 times. Experimental results are also provided in performance metrics to prove the reliability of our proposed method.

To date, malware caused by botnet activities is one of the most serious cybersecurity threats faced by internet communities. Researchers have proposed data-mining-based IDS as an alternative solution to misuse-based IDS and anomaly-based IDS to detect botnet activities. In this paper, we propose a new method that improves IDS performance to detect botnets. Our method combines two statistical methods, namely low variance filter and Pearson correlation filter, in the feature-selection process. To prove our method can increase the performance of a data-mining-based IDS, we use accuracy and computational time as parameters. A benchmark intrusion dataset (ISCX2017) is used to evaluate our work. Thus, our method reduces the number of features to be processed by the IDS from 77 to 15. Although the number of features decreases, it does not significantly change the accuracy. The computational time is decreased from 71 seconds to 5.6 seconds.

This work proposes how to build knowledge for 3D Modeling Gesture Javanese Dance. Trajectory build from precision movement from the original (real) dancer. By using basic motion 3D primitive and survey data, the degree of freedom (DOF) movement and roll-pitch-yaw rotations, could be detected. After, to get the motion gesture from motion capture, we create lots of joints which have complexity as a human dance. At the end, the purpose why we create skeletons for hands and legs also get the data gestures, is to calculate the difference percentage (error) from the gestures basic motion 3D primitive by survey data and by motion capture.