Design Of 4DOF 3D Robotic Arm to Separate the Objects Using a Camera

Nowadays robotic arm is widely used in various industries, especially those engaged in manufacturing. Robotic arms are usually used to perform jobs such as picking up and moving goods from their place of origin to the location desired by the operator. In this study, a 3d 4 DOF (Degree of Freedom) robotic arm. The prototype was made to move goods with random coordinates to places or boxes whose coordinates were determined in advance. The robot can know the coordinates of the object to be taken or moved. The arm robot prototype design is completed with a camera connected to a computer, where the camera is installed statically (fixed position) above the robot's work area. The camera functions like image processing to detect the object's position by taking the coordinates of the object. Then the object coordinates will be input into inverse kinematics that will produce an angle in every point of the servo arm so that the position of the end effector on the robot arm can be founded and reach the intended object. From the results of testing and analysis, it was found that the error in the webcam test to detect object coordinates was 2.58%, the error in the servo motion test was 12.68%, and the error in the inverse kinematics test was 7.85% on the x-axis, the error was 6.31% on the y-axis and an error of 12.77% on the z-axis. The reliability of the whole system is 66.66%.

Emotion Detection in Twitter Social Media Using Long Short-Term Memory (LSTM) and Fast Text

Emotion detection is important in various fields such as education, business, employee recruitment. In this study, emotions will be detected with text that comes from Twitter because social media makes users tend to express emotions through text posts. One of the social media that has the highest user growth rate in Indonesia is Twitter. This study will use the LSTM method because this method is proven to be better than previous studies. Word embedding fast text will also be used in this study to improve Word2Vec and GloVe that cannot handle the problem of out of vocabulary (OOV). This research produces the best accuracy for each word embedding as follows, Word2Vec produces an accuracy of 73,15%, GloVe produces an accuracy of 60,10%, fast text produces an accuracy of 73,15%. The conclusion in this study is the best accuracy was obtained by Word2Vec and fast text. The fast text has the advantage of handling the problem of out of vocabulary (OOV), but in this study, it cannot improve the accuracy of word 2vec. This study has not been able to produce very good accuracy. This is because of the data used. In future works, to get even better results, it is expected to apply other deep learning methods, such as CNN, BiLSTM, etc. It is hoped that more data will be used in future studies.

Optimization Design of Coal Dryer Using Genetic Algorithm in Power Plant

Coal that is often used in Steam Power Plants is a type of Low Rank Coal (Low Rank Coal) which has a caloric value of 4200 kcal / kg with a moisture content of 40%. Coal with water content that reaches 40% can cause the efficiency process of the plant to be not optimal. Low efficiency values will cause the use of electricity to increase and the combustion process to be incomplete so that it can cause many losses to the Steam Power Plant. From this problem, there needs to be a process of drying coal in order to reduce water content, the technology used in the process of drying coal is coal dryer. Design of coal dryer required source of steam or heat for drying process. Steam Power Plant there is steam waste extraction from turbines that can be used as a heat source to heat coal. If this extraction vapor is utilized, it can reduce the load from the condenser. The amount of turbine extraction steam that can be received by the coal dryer depends on the design of the coal dryer, because the design process of the coal dryer will affect the availability of energy in the coal dryer. This paper will discuss about optimization calculation with genetic algorithm method, to obtain the best design of coal dryer so that the heat received can be maximized so that the drying process becomes faster.

Bezier Curve Collision-Free Route Planning Using Meta-Heuristic Optimization

A collision-free route is very important for achieving sustainability in a manufacturing process and vehicle robot trajectories that commonly operate in a hazardous environment surrounded by obstacles. This paper presents a collision avoidance algorithm using a Bezier curve as a route path. The route planning is modeled as an optimization problem with the objective optimization is to minimize the route length considering an avoiding collision constraint. The collision-avoidance algorithm based on curve point analysis is developed incorporating metaheuristic optimizations, namely a Genetic Algorithm (GA) and a Grey Wolf Optimizer (GWO). In the collision avoidance algorithm, checking of curve point's position is important to evaluate the individual fitness value. The curve points are analyzed in such a way so that only the paths which are outside the obstacle area are selected. In this case, besides the minimum length as a fitness function, the constraint is the position of curve points from an obstacle. With the help of meta-heuristic optimization, the developed collision avoidance algorithm has been applied successfully to different types of obstacle geometries. The optimization problem is converted to the maximization problem so that the highest fitness value is used to measure the performance of the GA and GWO. In general, results show that the GWO outperforms the GA, where it exhibits the highest fitness value. However, the GA has shown better performance for the narrow passage problem than that of the GWO. Thus, for future research, implementing the hybrid technique combining the GA and the GWO to solve the advanced path planning is essential.

An Efficient Technique for Automation of The NFT (Nutrient Film Technique) Hydroponic System Using Arduino

Hydroponic Nutrient Film Technique (NFT) is most widely applied on a home and industrial scale. One of the drawbacks of the NFT hydroponic system is that it is very dependent on electricity for 24 hours to power the water pump. The basic principle of the NFT hydroponic system is to flow nutrients to plant roots with a shallow and circulating nutrient layer so that plants get enough water, nutrients, and oxygen. Therefore, the role of the water pump in the hydroponic NFT system is crucial. This research makes the automation of the NFT hydroponic system more efficient using Arduino. There are two main parts to this automation system: the control of pH levels and nutrient distribution. The pH sensor is used to control the pH level of nutrients, and the ultrasonic sensor is used for nutrient distribution. Efficiency is emphasized more on the distribution of nutrients because it absorbs more electrical energy. The method used is to flow the nutrients in the reservoir to a storage tank that is located higher than the plant using a water pump with a large discharge. Nutrients are transported to each plant using the force of gravity. The nutrient volume is controlled automatically using an ultrasonic sensor in the storage tank. The water pump is only activated by the ultrasonic sensor readings on the storage tank. So that the need for electricity to turn on the water pump is reduced, based on tests carried out on the use of a 220-volt AC / 50 Hz / 125-watt water pump and the use of a 250-liter nutrient storage tank, it can be concluded that the system that has been created can save 70% of electricity consumption.

Traffic Light Automation with Camera Tracker and Microphone to Recognize Ambulance Using the HAAR Cascade Classifier Method

Lack of knowledge by road users regarding these priorities, especially when there is a passing ambulance that is often stuck in traffic at a crossroads due to accumulated vehicles and the traffic light is still red. The purpose of this paper is to simulate traffic light automation by giving a green light every time an ambulance passes by using the HAAR and Computer Vision methods. The HAAR method is used for training data from less sharp images as part of the Ambulance object classification process. The Computer Vision method is used as a tool in image processing objects to processing the image captured by the Camera. Hardware through the microphone performs pattern recognition to pick up ambulance sirens. The test result at the average frequency caught by the microphone is 1.3 kHz. The test results of the System to capture ambulance objects received a precision value of 75%, a recall of 100%, and an accuracy of 75%.

Comparison of Clustering K-Means, Fuzzy C-Means, and Linkage for Nasa Active Fire Dataset

One of the causes of forest fires is the lack of speed of handling when a fire occurs. This can be anticipated by determining how many extinguishing units are in the center of the hot spot. To get hotspots, NASA has provided an active fire dataset. The clustering method is used to get the most optimal centroid point. The clustering methods we use are K-Means, Fuzzy C-Means (FCM), and Average Linkage. The reason for using K-means is a simple method and has been applied in various areas. FCM is a partition-based clustering algorithm which is a development of the K-means method. The hierarchical based clustering method is represented by the Average Linkage method. The measurement technique that uses is the sum of the internal distance of each cluster. Elbow evaluation is used to evaluate the optimal cluster. The results obtained after conducting the K-Means trial obtained the best results with a total distance of 145.35 km, and the best clusters from this method were 4 clusters. Meanwhile, the total distance values obtained from the FCM and Linkage methods were 154.13 km and 266.61 km.

Automatic Garden Umbrella Prototype with Light and Rain Sensor Based on Arduino Uno Microcontroller

Park is a green open space widely used by the community to carry out various activities ranging from recreation, playing, sports, and other passive activities. Current weather conditions are often uncertain. This makes people inconvenient when it rains suddenly, especially when outdoors such as in parks. Because if they don't immediately take shelter when it rains, it can make the body sick, besides that, rainwater can damage the non-waterproof gadgets they carry. In other conditions, when the weather is bright, and the sun is shining hot, it can make people feel hot and lazy to do outdoor activities in the park. Therefore, an automatic umbrella tool was made that functions as a shelter in the garden. In this tool, there is a light sensor module and also a rain sensor, which is controlled with the Arduino Uno microcontroller as an input data processor and an L298N motor driver, which functions to regulate the speed and direction of the DC motor rotation (to the right and left) as an umbrella drive. When the motor rotates to the right, the umbrella will open, while when the motor rotates to the left, the umbrella will close again.

Message Security Using Rivest-Shamir-Adleman Cryptography and Least Significant Bit Steganography with Video Platform

All over the world, information technology has developed into a critical communication medium. One of them is digital messaging. We can connect and share information in real-time using digital messages. Without us knowing it, advances in message delivery are not only followed by kindness. Message security threats are also growing. Many unauthorized parties try to intercept critical information sent for the benefit of certain parties. As a countermeasure, various message security techniques exist to protect the messages we send. One of them is cryptography and steganography. Cryptography is useful for converting our messages into coded text so that unauthorized parties cannot read them. Meanwhile, steganography is useful for hiding our encrypted messages into several media, such as videos. This research will convert messages into ciphertext using the Rivest-Shamir-Adleman method and then insert them into video media using the Least Significant Bit method. There are four types of messages tested with different sizes. All messages will be encrypted and embedding using the Python programming language. Then the video will be tested using the MSE, PSNR, and Histogram methods. So we get a value that shows which message gets the best results. So that the message sent is more guaranteed authenticity and reduces the possibility of message leakage.

Switching Systems Designing Based on IoT

The development of technology has progressed very rapidly in a short period, as has technology that has recently been developed in various aspects of life, namely the Internet of Things. In the past, controlling household electrical appliances was usually done directly by pressing a button on the house's wall and was very ineffective when the house owner was out of town while the house was empty. With the Internet of Things technology, a system can be applied in everyday life, namely controlling household electrical appliances to turn off and remotely using internet communication via an android smartphone. In this system design, a control design using a series of microcontrollers and relays connected to a smartphone via the internet is used because the microcontroller already has a Wireless Fidelity (WIFI) module. The results of controlled tests on household electrical appliances can run well. All components of the design of the device are well integrated with smartphones and the internet. Control can be done anywhere and anytime. System response during the day between 1-4 seconds and at night between 1-2 seconds.