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International Journal of Artificial Intelligence and Robotics (IJAIR)
Published by Universitas Dr. Soetomo Surabaya
ISSN : -     EISSN : 26866269     DOI : 10.25139
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
International Journal of Artificial Intelligence & Robotics (IJAIR) is One of the journals published by Informatics Department, Universitas Dr Soetomo, was established in November 2019. IJAIR a double-blind peer-reviewed journal, the aim of this journal is to publish high-quality articles dedicated to the field of information and communication technology, Published 2 times a year in November and May. Focus and Scope: Machine Learning & Soft Computing, Data Mining & Big Data, Computer Vision & Pattern Recognition dan Robotics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol. 3 No. 1 (2021): IJAIR : May" : 5 Documents clear
An Efficient Technique for Automation of The NFT (Nutrient Film Technique) Hydroponic System Using Arduino Wibisono, Vicky; Kristyawan, Yudi
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 3 No. 1 (2021): IJAIR : May
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (339.138 KB) | DOI: 10.25139/ijair.v3i1.3209

Abstract

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.
Design Of 4DOF 3D Robotic Arm to Separate the Objects Using a Camera Fahruzi, Akhmad; Agomo, Bimo Satyo; Prabowo, Yulianto Agung
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 3 No. 1 (2021): IJAIR : May
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (472.136 KB) | DOI: 10.25139/ijair.v3i1.3787

Abstract

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%.
Bezier Curve Collision-Free Route Planning Using Meta-Heuristic Optimization Machmudah, Affiani; Parman, Setyamartana
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 3 No. 1 (2021): IJAIR : May
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (652.198 KB) | DOI: 10.25139/ijair.v3i1.3821

Abstract

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.
Optimization Design of Coal Dryer Using Genetic Algorithm in Power Plant Luluk Karlina, Diyajeng; Bintang, Ilham
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 3 No. 1 (2021): IJAIR : May
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (235.244 KB) | DOI: 10.25139/ijair.v3i1.3825

Abstract

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.
Emotion Detection in Twitter Social Media Using Long Short-Term Memory (LSTM) and Fast Text Riza, M Alfa; Charibaldi, Novrido
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 3 No. 1 (2021): IJAIR : May
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (621.699 KB) | DOI: 10.25139/ijair.v3i1.3827

Abstract

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.

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