Pub Date : 2021-11-30DOI: 10.25139/ijair.v3i2.3347
Yudi Kristyawan, Atinus Yikwa
Farming rabbits in large numbers produce large amounts of feces and urine. A cage full of feces and urine can cause health problems for rabbits. This research aims to produce an automation system for the disposal of feces and urine in rabbit cages using an Arduino board and implemented in a prototype form. This system uses electronic devices including load cells, HX711 Module to make it easier to read load cells in measuring weight, real-time clock (RTC) for timing, ultrasonic sensor HC-SR04 to detect the presence of a certain object, dc motor, L298N motor driver module to control a dc motor, an LCD 16x2 module to display the weight and height of feces and urine, a buzzer as a notification of the status of the container if it is full, and an Arduino Uno as a controller of the entire system. The system operates so that the feces excreted by the rabbit fall onto the conveyor belt. At the same time, the urine passes via the conveyor belt and falls into the cross-section before being pumped into the urine collection container. The feces on the conveyor belt will be moved with a dc motor towards the stool container based on a certain time. Each stool and urine container is weighed with a load cell and ultrasonic sensor to detect when the container is full. Then the condition of the load cell and the ultrasonic sensor is displayed on an LCD 16x2. When one or both containers are full, a buzzer will sound as a notification. The method used in this research is an experimental method by manipulating or controlling natural situations into artificial conditions. The artificial condition is the provision of deliberate control over the object of study. The test results show that this system can remove waste based on the time using a belt conveyor and monitoring the weight and height of the dirt. If the dirt has met the specified limit, the system can activate an alarm as a notification.
{"title":"Automation System for the Disposal of Feces and Urine in Rabbit Cages Using Arduino","authors":"Yudi Kristyawan, Atinus Yikwa","doi":"10.25139/ijair.v3i2.3347","DOIUrl":"https://doi.org/10.25139/ijair.v3i2.3347","url":null,"abstract":"Farming rabbits in large numbers produce large amounts of feces and urine. A cage full of feces and urine can cause health problems for rabbits. This research aims to produce an automation system for the disposal of feces and urine in rabbit cages using an Arduino board and implemented in a prototype form. This system uses electronic devices including load cells, HX711 Module to make it easier to read load cells in measuring weight, real-time clock (RTC) for timing, ultrasonic sensor HC-SR04 to detect the presence of a certain object, dc motor, L298N motor driver module to control a dc motor, an LCD 16x2 module to display the weight and height of feces and urine, a buzzer as a notification of the status of the container if it is full, and an Arduino Uno as a controller of the entire system. The system operates so that the feces excreted by the rabbit fall onto the conveyor belt. At the same time, the urine passes via the conveyor belt and falls into the cross-section before being pumped into the urine collection container. The feces on the conveyor belt will be moved with a dc motor towards the stool container based on a certain time. Each stool and urine container is weighed with a load cell and ultrasonic sensor to detect when the container is full. Then the condition of the load cell and the ultrasonic sensor is displayed on an LCD 16x2. When one or both containers are full, a buzzer will sound as a notification. The method used in this research is an experimental method by manipulating or controlling natural situations into artificial conditions. The artificial condition is the provision of deliberate control over the object of study. The test results show that this system can remove waste based on the time using a belt conveyor and monitoring the weight and height of the dirt. If the dirt has met the specified limit, the system can activate an alarm as a notification.","PeriodicalId":208192,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132621459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-30DOI: 10.25139/ijair.v3i2.4256
Bonifacius Vicky Indriyono, Widyatmoko
A game containing elements of artificial intelligence, of course, requires an algorithm in its application. One example of a game that includes elements of artificial intelligence is the Labyrinth game. Maze is a simple educational game. This game is known as finding a way out of the maze to arrive at a predetermined goal. The labyrinth encounters numerous obstacles along the way, such as dead ends and parapets, to reach the target location. In this game, players are required to think logically about how to find the right maze path. The obstacle faced in this game is that sometimes players have difficulty finding a way out, especially if the game level has reached a high level in the process of finding a way out. To solve this problem, a graph tracing technique is needed. The Breadth-First Search (BFS) strategy can be used in conjunction with various graph search algorithms. An example of a broad search method is the Breadth-First Search Algorithm, which works by visiting nodes at level n first before moving on to nodes at level n+1. The advantage of the Breadth-First Search algorithm is that it can find a solution as the shortest path and find the minimum solution if there is more than one solution. This study will discuss how to find a path for the Labyrinth using the BFS algorithm. The result of applying this BFS algorithm is the shortest route solution raised so that the Labyrinth can arrive at the destination point through the route provided.
{"title":"Optimization of Breadth-First Search Algorithm for Path Solutions in Mazyin Games","authors":"Bonifacius Vicky Indriyono, Widyatmoko","doi":"10.25139/ijair.v3i2.4256","DOIUrl":"https://doi.org/10.25139/ijair.v3i2.4256","url":null,"abstract":"A game containing elements of artificial intelligence, of course, requires an algorithm in its application. One example of a game that includes elements of artificial intelligence is the Labyrinth game. Maze is a simple educational game. This game is known as finding a way out of the maze to arrive at a predetermined goal. The labyrinth encounters numerous obstacles along the way, such as dead ends and parapets, to reach the target location. In this game, players are required to think logically about how to find the right maze path. The obstacle faced in this game is that sometimes players have difficulty finding a way out, especially if the game level has reached a high level in the process of finding a way out. To solve this problem, a graph tracing technique is needed. The Breadth-First Search (BFS) strategy can be used in conjunction with various graph search algorithms. An example of a broad search method is the Breadth-First Search Algorithm, which works by visiting nodes at level n first before moving on to nodes at level n+1. The advantage of the Breadth-First Search algorithm is that it can find a solution as the shortest path and find the minimum solution if there is more than one solution. This study will discuss how to find a path for the Labyrinth using the BFS algorithm. The result of applying this BFS algorithm is the shortest route solution raised so that the Labyrinth can arrive at the destination point through the route provided.","PeriodicalId":208192,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122333432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-30DOI: 10.25139/ijair.v3i2.4210
Anang Aris Widodo, Muchammad Yuska Izza Mahendra, Mohammad Zoqi Sarwani
The popularity of Korean culture today attracts many people to learn everything about Korea, especially in learning the Korean language. To learn Korean, you must first know Korean letters (Hangul), which are non-Latin characters. Therefore, a digital approach is needed to recognize handwritten Korean (Hangul) words easily. Handwritten character recognition has a vital role in pattern recognition and image processing for handwritten Character Recognition (HCR). The backpropagation method trains the network to balance the network's ability to recognize the patterns used during training and the network's ability to respond correctly to input patterns that are similar but not the same as the patterns used during training. This principle is used for character recognition of Korean characters (Hangul), a sub-topic in fairly complex pattern recognition. The results of the calculation of the backpropagation artificial neural network with MATLAB in this study have succeeded in identifying 576 image training data and 384 Korean letter testing data (Hangul) quite well and obtaining a percentage result of 80.83% with an accuracy rate of all data testing carried out on letters. Korean (Hangul).
{"title":"Recognition of Korean Alphabet (Hangul) Handwriting into Latin Characters Using Backpropagation Method","authors":"Anang Aris Widodo, Muchammad Yuska Izza Mahendra, Mohammad Zoqi Sarwani","doi":"10.25139/ijair.v3i2.4210","DOIUrl":"https://doi.org/10.25139/ijair.v3i2.4210","url":null,"abstract":"The popularity of Korean culture today attracts many people to learn everything about Korea, especially in learning the Korean language. To learn Korean, you must first know Korean letters (Hangul), which are non-Latin characters. Therefore, a digital approach is needed to recognize handwritten Korean (Hangul) words easily. Handwritten character recognition has a vital role in pattern recognition and image processing for handwritten Character Recognition (HCR). The backpropagation method trains the network to balance the network's ability to recognize the patterns used during training and the network's ability to respond correctly to input patterns that are similar but not the same as the patterns used during training. This principle is used for character recognition of Korean characters (Hangul), a sub-topic in fairly complex pattern recognition. The results of the calculation of the backpropagation artificial neural network with MATLAB in this study have succeeded in identifying 576 image training data and 384 Korean letter testing data (Hangul) quite well and obtaining a percentage result of 80.83% with an accuracy rate of all data testing carried out on letters. Korean (Hangul).","PeriodicalId":208192,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124443033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-30DOI: 10.25139/ijair.v3i2.4395
Yulianti Fauziah, Kevin Aprilianta, H. Rustamaji
Javanese script is one of the languages which are a typical Javanese culture. Javanese script is seen in its use in writing the name of a particular agency or location that has historical and tourism value. The use of Javanese script in public places makes the existence of this script seen by many people, not only by the Javanese people. Some of them have difficulty recognizing the Javanese characters they encounter. One method of pattern recognition and image processing is Convolutional Neural Network (CNN). CNN is a method that uses convolution operations in performing feature extraction on images as a basis for classification. The process consists of initial data processing, classification, and syllable formation. The classification consists of 48 classes covering Javanese script types, namely basic letters (Carakan) and voice-modifying scripts (Sandhangan). It is tested with multi-class confusion matrix scenarios to determine the accuracy, precision, and recall of the built CNN model. The CNN architecture consists of three convolution layers with max-pooling operations. The training configuration includes a learning rate of 0.0001, and the number of filters for each convolution layer is 32, 64, and 128 filters. The dropout value used is 0.5, and the number of neurons in the fully-connected layer is 1,024 neurons. The average performance value of accuracy reached 87.65%, the average precision value was 88.01%, and the average recall value was 87.70%.
{"title":"Convolutional Neural Network Method for Classification of Syllables in Javanese Script","authors":"Yulianti Fauziah, Kevin Aprilianta, H. Rustamaji","doi":"10.25139/ijair.v3i2.4395","DOIUrl":"https://doi.org/10.25139/ijair.v3i2.4395","url":null,"abstract":"Javanese script is one of the languages which are a typical Javanese culture. Javanese script is seen in its use in writing the name of a particular agency or location that has historical and tourism value. The use of Javanese script in public places makes the existence of this script seen by many people, not only by the Javanese people. Some of them have difficulty recognizing the Javanese characters they encounter. One method of pattern recognition and image processing is Convolutional Neural Network (CNN). CNN is a method that uses convolution operations in performing feature extraction on images as a basis for classification. The process consists of initial data processing, classification, and syllable formation. The classification consists of 48 classes covering Javanese script types, namely basic letters (Carakan) and voice-modifying scripts (Sandhangan). It is tested with multi-class confusion matrix scenarios to determine the accuracy, precision, and recall of the built CNN model. The CNN architecture consists of three convolution layers with max-pooling operations. The training configuration includes a learning rate of 0.0001, and the number of filters for each convolution layer is 32, 64, and 128 filters. The dropout value used is 0.5, and the number of neurons in the fully-connected layer is 1,024 neurons. The average performance value of accuracy reached 87.65%, the average precision value was 88.01%, and the average recall value was 87.70%.","PeriodicalId":208192,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125670588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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