A robot guide is a robot that is used to guide users from a place of origin to their destination. During carrying out the guiding task, the robot must ensure that the user always follows wherever the direction is headed by the robot until it reaches the destination location. To get this certainty, one of the things that must be considered is the direction the user must walk in the direction of the robot's movement. In this paper, we built a system to monitor user awareness levels to coordinate with robots. We use RGB-D data from the Kinect sensor to detect target partners, their position, and orientation. The level of awareness is calculated using the Social Force Model (SFM) based on the target partner's position and body orientation parameters. This level of awareness will be used by the robot to evaluate the appropriate actions according to the target partner's activities. Based on the results of experiments, the level of awareness of target partners can be calculated and transformed in the form of attractive or repulsive forces towards robots.

Drone service is an Unmanned Aerial Vehicle (UAV) whose function is to deliver goods using air routes. Drone services sometimes have to travel long distances with a guaranteed security system to get to the customer's location and return safely to the base. But in fact, in the use of an insecure Internet of Drone (IoD) environment, there will be many attacks that try to manipulate the approximate location or interrupt transmission of data from fake nodes. Therefore, this paper focuses on a reliable data communication security system between drone services and server. Because the drone service uses a lightweight device, we propose a Hybrid cryptographic security scheme that has simple computing but a high level of security. It has layered encryption using AES- 256, ECC, and SHA256 which are provides high data authentication and encryption services for each node. The proposed scheme is efficient in terms of processing time so that it will not significantly affect the realtime sensor data from the drone. The experimental results show that the proposed system reaches 88.61 milliseconds to encrypt and decrypt messages using the Raspberry Fi device.

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.

E-learning is distance learning that uses computer technology, networks of computers and the internet. E-Learning allows students to study via computers in their respective places without having to go to study/lectures in class physically. Moodle is a Learning Management System that is used as a medium for delivering E-Learning. The problem that often arises in e-learning is that in the learning process, students interact more with e-learning media so that teachers will find it difficult to monitor student behavior when using learning media. In fact, students in some cases tend to drop out or attend lesser classes. Moodle can capture student interactions and activities while studying online using log files. From the results of student interactions and activities on e-learning, it can be used to determine their learning style. Identifying student learning styles can improve the performance of the learning process. This research suggests an approach to automatically predicting learning styles based on the Felder and Silverman learning style (FSLSM) model using the Decision Tree algorithm and the ensemble Gradient Boosted Tree method. We've used actual data sets derived from e-learning program log files to perform our work. We use precision and accuracy to assess the results. The results show that our approach is delivering excellent results.

In general, a socially-aware mobile robot must have an ability to safely navigate among human environment. To address with this competency, the mobile robots must be able to detect the existence of humans around. This paper proposes the use of Fast Fourier Transform (FFT) to analyze shape-models of human legs that is obtained from Laser Range Finder (LRF) scanning results. A 240° of LRF was used to capture and visualize the environment in one dimensional plane. The plane is then converted into one dimensional signal that consists of 1,024 data points. These data points represented the distance of all measured points. A specific set of points formed the pattern of human legs only is then resized into 32 data. This resized-data is transformed into frequency by using FFT. The result of FFT is then fed into Support Vector Machine (SVM) to be classified into two classes, they are human or not human. Based on the experimental results, our proposed method shows a promising result in order to detect human based on one dimensional feature of his legs by achieving more than 80% of accuracy.

The study on balancing one-wheeled human transporter has previously done before in numerous studies. The main problem discussed in the study of one-wheeled human transporter is its balancing control system. The variation in the weight of the rider of the one-wheeled self-balancing human transporter becomes an important issue to the balance control system. The weight variation of the rider causes the response from the balance of the transporter become unstable. This paper will provide solution to solve these problems. In this paper a method of control system is presented therefore it provide robustness towards disturbance of rider's weight variation. The control method presented is the Proportional Integral Derivative-Active Force Control (PID-AFC). The PID-AFC method has the advantage of being able to provide robustness to disturbance in a system. This paper will seek for evidence that the PID-AFC can provide robustness towards disturbance, this method is compared to PID method. The experiment was carried out through a simulation with various rider's weights. After the experiment, it was found that the PID-AFC method is able to provide a better balance and provide robustness toward the disturbance of rider's weight variation on one-wheeled human transporters.

One of the keys to the success of interaction between people is communication. Communication can be done verbally or non-verbally. In this paper, we build interactions between humans and computers using hand gestures. The hand gesture is recognized by the palm of the hand which is obtained from the results of human skeleton segmentation through camera Kinect. Recognition of palm gestures is performed on a series of RGB Kinect output frames. Histogram of Oriented Gradient (HOG) is used to produce a palm frame per frame gesture feature which is arranged in 4 seconds as a gesture descriptor. Dynamic Time Warping (DTW) is used as a classifier that will compare the description of the input gesture with the template gesture description. Based on the results of the experiment, the performance of the hand gesture recognition system reached 76.7%.

The following topics are dealt with: mobile robots; Internet; learning (artificial intelligence); object detection; cameras; position control; image segmentation; computer aided instruction; feature extraction; wireless sensor networks.

Remotely operated vehicle (ROV) plays an important role in the exploration of underwater objects for observation of marine life, oil and gas exploration and rescue. In underwater diving, a variety of factors can influence the movements carried out by ROV such as water flow, water waves, water pressure, etc. Control of balance and depth in the ROV are important factor in carrying out various missions ROV found it in the form of water flow and water waves. PID controller is still ineffective due to the nonlinear nature of the ROV and therefore this paper proposes to add a Fuzzy logic controller to deal with the nonlinearity in the ROV. With a combination of PID and Fuzzy controller, the ROV is able to balance while making the diving and maneuvering moves despite receiving interference in the water. Using the proposed controller, the ROV is able to respond well with respect to disturbances in attitudes and depth motion control scenarios.

This study focuses on the feature extraction and development of tomato growth model. In Indonesia, the application of sensors and actuators to build smart farming has become increasingly popular in the past year but to produce measured harvest quality and quantity in smart farming, a model of growth and yield of the tomato plant is needed. in this paper, observations on the growth of tomato plants were carried out to extract features in the growth of tomato plants. In the experiment, a greenhouse was built to maintain the growth conditions of tomato plants which are equipped with an information system for reading sensor data to determine the greenhouse environment conditions for the growth of tomato plants with a local-global management strategy. from the experiment, we obtained a response model of the tomato plant growth with the effect of the maximum temperature that caused the blossoms to fall. and from the growth model, there are 5 growth stages of tomato plants are obtained, which at each stage has different growth variables with different nutritional needs and environmental conditions. with the important knowledge gained from this experiment, it can be used to develop a dynamic tomato plant growth model based on the growth stage, which is expected to be applied in the control system on smart farming in the future work.

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.

In human-robot interactions, robots that often interact with humans need some information from humans to be able to communicate. In human-oriented robots, the robots must pay attention wherever the human will go. Therefore, the human-oriented robots are more flexible. To estimate wherever the human will go, it can use the direction of the human or what is called intention. Intention can be taken from the perspective of the human body. An algorithm is needed to estimate the orientation of the human body. In this study, using image processing from depth image is expected to be able to estimate the orientation of the human body when a human is not moving (static orientation) so that it can be used by the robots to predict the intention direction of a human. One method that can be used to classify the orientation of a human body is the Support Vector Machine (SVM) so that it gets a classified orientation value. Then from the classified orientation value to get the static orientation value, Kalman Filter is used. Orientation classification with SVM method has accuracy of 96.528%. Static orientation estimation using this method produces precision value of 0.0970.

Technological innovations in the marine field have developed rapidly in the last decade. One of it is the innovation in the technologies related to the underwater research and exploration. It has been driven by the need of the a flexible, reconfigurable, and reliable machine for underwater operation termed Remotely Operated Vehicle (ROV). The ROV development has opened up many opportunities in the observation and exploration of underwater environment. The most important aspect in the ROV is the flexibility of motion during the maneuver in the water. The mechanical structure and also the static and dynamic analysis is very important aspect in designing ROV related to its operation environment. This paper proposes ROV body design based on analysis characteristic hydrodynamic and buoyancy effect using CFD. The characteristics of each fluid flow affect the motion of the ROV which movement is driven by the thrusters. The hydrodynamic characteristics and buoyancy effect will be analyzed. The result will be used in the further mathematical calculations in the controller design phase.

Recently, 3D visualization technologies such as point cloud are growing rapidly and are in demand for environmental mapping needs because they have the ability to represent the real world in a more complete and in depth way, as well as new and more advanced visual experiences. The point cloud model is able to represent objects well through a series of points and is increasingly used in various fields of research. While 3D visualization technology is growing, several platforms such as humanoid robots, mobile robots, and automatic cars require mapping technology that is able to properly map the surrounding environment with a better display to improve intelligence and decision-making abilities. To overcome the problem of mapping, in this research an experiment was conducted to combine point cloud to form a map based on a visual odometry from stereo camera.