Pub Date : 2021-05-31DOI: 10.25139/IJAIR.V3I1.3827
M. Riza, N. Charibaldi
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.
情感检测在教育、商业、员工招聘等各个领域都很重要。在本研究中，情感将通过来自Twitter的文本进行检测，因为社交媒体使用户倾向于通过文本帖子来表达情感。Twitter是印尼用户增长率最高的社交媒体之一。本研究将使用LSTM方法，因为该方法被证明比以往的研究更好。本研究还将使用单词嵌入快速文本来改进Word2Vec和GloVe无法处理out of vocabulary (OOV)的问题。本研究得出的每个词嵌入的最佳准确率如下，Word2Vec产生的准确率为73,15%，GloVe产生的准确率为66,10%，fast text产生的准确率为73,15%。本研究的结论是使用Word2Vec和fast text获得了最好的准确率。快速文本在处理词汇量不足(OOV)问题上具有优势，但在本研究中，它并不能提高单词2vec的准确率。这项研究还不能产生很好的准确性。这是因为所使用的数据。在未来的工作中，为了得到更好的结果，我们希望应用其他的深度学习方法，如CNN、BiLSTM等。希望在未来的研究中使用更多的数据。
{"title":"Emotion Detection in Twitter Social Media Using Long Short-Term Memory (LSTM) and Fast Text","authors":"M. Riza, N. Charibaldi","doi":"10.25139/IJAIR.V3I1.3827","DOIUrl":"https://doi.org/10.25139/IJAIR.V3I1.3827","url":null,"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.","PeriodicalId":365842,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132566307","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 : 2020-12-01DOI: 10.25139/ijair.v2i2.3194
Eldha Nur Ramadhana Putra, Edi Prihartono, Budi Santoso
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%.
{"title":"Traffic Light Automation with Camera Tracker and Microphone to Recognize Ambulance Using the HAAR Cascade Classifier Method","authors":"Eldha Nur Ramadhana Putra, Edi Prihartono, Budi Santoso","doi":"10.25139/ijair.v2i2.3194","DOIUrl":"https://doi.org/10.25139/ijair.v2i2.3194","url":null,"abstract":"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%.","PeriodicalId":365842,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122836322","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 : 2020-12-01DOI: 10.25139/ijair.v2i2.3150
Widad Muhammad, D. H. Sulaksono, S. Agustini
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.
{"title":"Message Security Using Rivest-Shamir-Adleman Cryptography and Least Significant Bit Steganography with Video Platform","authors":"Widad Muhammad, D. H. Sulaksono, S. Agustini","doi":"10.25139/ijair.v2i2.3150","DOIUrl":"https://doi.org/10.25139/ijair.v2i2.3150","url":null,"abstract":"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.","PeriodicalId":365842,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130060140","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 : 2020-07-01DOI: 10.25139/ijair.v2i1.2706
Yudi Kristyawan, Achmad Dicky Rizhaldi
The door is an important component in a building as security. It is used as access in and out of a room. People in the modern era now want everyday life that is completely automated, so that the work can be done easily without wasting energy and can shorten the time. Along with the rapid development, the need for effectiveness and efficiency is prioritized in various fields. The purpose of this paper is to design an automatic sliding door that only detects one Radio Frequency Identification (RFID) card to open and close. The use of RFID systems can strengthen the security level of building access. This study uses a data processing method in the form of an ID number generated from a tag. Specifications in the discussion of the results in this study include a motor that uses a 12-volt DC motor, a maximum door weight of 5 kg, can only detect one RFID to open and close the door, and the sliding door used is one door. The results of system testing are obtained to open a door that is without load, and the door can move 14 cm from the distance of the door hole so that it opens. Doors with a load of 1-1.5 kg also move 14 cm from the distance of the door opening when open. Doors with a load of 2-3 kg only move 12.5-9.5 cm from the distance of the door so that it opens. When the door gets heavier 3.5-4 kg, the door moves only 7.5-3 cm from the distance the door hole remains closed.
{"title":"An Automatic Sliding Doors Using RFID and Arduino","authors":"Yudi Kristyawan, Achmad Dicky Rizhaldi","doi":"10.25139/ijair.v2i1.2706","DOIUrl":"https://doi.org/10.25139/ijair.v2i1.2706","url":null,"abstract":" The door is an important component in a building as security. It is used as access in and out of a room. People in the modern era now want everyday life that is completely automated, so that the work can be done easily without wasting energy and can shorten the time. Along with the rapid development, the need for effectiveness and efficiency is prioritized in various fields. The purpose of this paper is to design an automatic sliding door that only detects one Radio Frequency Identification (RFID) card to open and close. The use of RFID systems can strengthen the security level of building access. This study uses a data processing method in the form of an ID number generated from a tag. Specifications in the discussion of the results in this study include a motor that uses a 12-volt DC motor, a maximum door weight of 5 kg, can only detect one RFID to open and close the door, and the sliding door used is one door. The results of system testing are obtained to open a door that is without load, and the door can move 14 cm from the distance of the door hole so that it opens. Doors with a load of 1-1.5 kg also move 14 cm from the distance of the door opening when open. Doors with a load of 2-3 kg only move 12.5-9.5 cm from the distance of the door so that it opens. When the door gets heavier 3.5-4 kg, the door moves only 7.5-3 cm from the distance the door hole remains closed.","PeriodicalId":365842,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116825253","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 : 2020-06-03DOI: 10.25139/ijair.v2i1.2653
Dian Ahkam Sani, Mohammad Zoqi Sarwani, Muhamad Agus Setiawan
Around the world, the internet (interconnection network) has developed into one of the most popular data communication media. With a variety of illegal information retrieval techniques that are developing, many people are trying to access information that is not their right. Various techniques to protect confidential information from unauthorized persons have been carried out to secure important data. Steganography is a science and art for writing hidden messages so that no other party knows the existence of the message. The three results of tests conducted by the LSB method can be used to hide messages into images. The first test was successful by writing a message that less than 31 characters stored in the picture, the second succeeded in writing a message equal to 31 characters stored in the picture, the third failed to write a message of more than 31 characters stored in the picture.
{"title":"An Implementation of MMS Steganography With The LSB Method","authors":"Dian Ahkam Sani, Mohammad Zoqi Sarwani, Muhamad Agus Setiawan","doi":"10.25139/ijair.v2i1.2653","DOIUrl":"https://doi.org/10.25139/ijair.v2i1.2653","url":null,"abstract":"Around the world, the internet (interconnection network) has developed into one of the most popular data communication media. With a variety of illegal information retrieval techniques that are developing, many people are trying to access information that is not their right. Various techniques to protect confidential information from unauthorized persons have been carried out to secure important data. Steganography is a science and art for writing hidden messages so that no other party knows the existence of the message. The three results of tests conducted by the LSB method can be used to hide messages into images. The first test was successful by writing a message that less than 31 characters stored in the picture, the second succeeded in writing a message equal to 31 characters stored in the picture, the third failed to write a message of more than 31 characters stored in the picture.","PeriodicalId":365842,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126629849","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}
The concept of Traveling Salesman Problem (TSP) used in the discussion of this paper is the Traveling Salesman Problem with Time Windows (TSP-TW), where the time variable considered is the time of availability of attractions for tourists to visit. The algorithm used for optimizing the solution of Traveling Salesman Problem with Time Windows (TSP-TW) is a genetic algorithm. The search for a solution for determining the best route begins with the formation of an initial population that contains a collection of individuals. Each individual has a combination of different tourist sequence. Then it is processed by genetic operators, namely crossover with Partially Mapped Crossover (PMX) method, mutation using reciprocal exchange method, and selection using ranked-based fitness method. The research method used is GRAPPLE. Based on tests conducted, the optimal generation size results obtained in solving the TSP-TW problem on the tourist route in the Province of DIY using genetic algorithms is 700, population size is 40, and the combination of crossover rate and mutation rate is 0.70 and 0.30 There is a tolerance time of 5 seconds between the process of requesting distance and travel time and the process of forming a tourist route for the genetic algorithm process.
{"title":"Genetic Algorithm for Optimizing Traveling Salesman Problems with Time Windows (TSP-TW)","authors":"Juwairiah Juwairiah, Dicky Pratama, H. Rustamaji, Herry Sofyan, Dessyanto Boedi Prasetyo","doi":"10.25139/ijair.v1i1.2024","DOIUrl":"https://doi.org/10.25139/ijair.v1i1.2024","url":null,"abstract":"The concept of Traveling Salesman Problem (TSP) used in the discussion of this paper is the Traveling Salesman Problem with Time Windows (TSP-TW), where the time variable considered is the time of availability of attractions for tourists to visit. The algorithm used for optimizing the solution of Traveling Salesman Problem with Time Windows (TSP-TW) is a genetic algorithm. The search for a solution for determining the best route begins with the formation of an initial population that contains a collection of individuals. Each individual has a combination of different tourist sequence. Then it is processed by genetic operators, namely crossover with Partially Mapped Crossover (PMX) method, mutation using reciprocal exchange method, and selection using ranked-based fitness method. The research method used is GRAPPLE. Based on tests conducted, the optimal generation size results obtained in solving the TSP-TW problem on the tourist route in the Province of DIY using genetic algorithms is 700, population size is 40, and the combination of crossover rate and mutation rate is 0.70 and 0.30 There is a tolerance time of 5 seconds between the process of requesting distance and travel time and the process of forming a tourist route for the genetic algorithm process.","PeriodicalId":365842,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121100605","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 : 2019-10-31DOI: 10.25139/ijair.v1i1.2026
M. Zikky, R. Hakkun, Buchori Rafsanjani
We create an API (Application Programming Interface) for Indonesian Sign Language (Sistem Isyarat Bahasa Indonesia/SIBI) which is called OpenSIBI. In this case study, we use the Myo Armband device to capture hand gesture data movement. It uses five sensors: Accelerometer, Gyroscope, Orientation, Orientation-Euler, and EMG. First, we record, convert and save those data into JSON dataset in the server as data learning. Then, every data request (trial data) from the client will compare them using k-NN Normalization process. OpenSIBI API works as the middleware which integrated to RabbitMQ as the queue request arranger. Every service request from the client will automatically spread to the server with the queue process. As the media observation, we create a client data request by SIBI Words and Alphabeth Game, which allows the user to answer several stages of puzzle-game with Indonesian Sign Language hand gesture. Game-player must use the Myo armband as an interactive device that reads the hand movement and its fingers for answering the questions given. Thus, the data will be classified and normalized by the k-NN algorithm, which will be processed on the server. In this process, data will pass OpenAPI SIBI (which connected to RabbitMQ) to queue every incoming data-request. So, the obtained data will be processed one by one and sent it back to the client as the answer.
我们为印尼手语(SIBI)创建了一个API(应用程序编程接口)，称为OpenSIBI。在本案例研究中，我们使用Myo Armband设备来捕获手势数据运动。它使用五个传感器:加速度计、陀螺仪、方向、方向-欧拉和肌电图。首先，我们将这些数据记录、转换并保存到服务器端的JSON数据集中作为数据学习。然后，来自客户端的每个数据请求(试验数据)将使用k-NN归一化过程对它们进行比较。OpenSIBI API作为中间件集成到RabbitMQ中作为队列请求安排器。来自客户机的每个服务请求都将通过队列进程自动传播到服务器。作为媒体观察，我们通过SIBI Words and Alphabeth Game创建了一个客户数据请求，允许用户用印度尼西亚手语手势回答几个阶段的益智游戏。游戏玩家必须使用Myo臂环作为一个互动设备，读取手的运动和手指来回答给定的问题。因此，数据将通过k-NN算法进行分类和归一化，并在服务器上进行处理。在这个过程中，数据将通过OpenAPI SIBI(连接到RabbitMQ)来对每个传入的数据请求进行排队。因此，获得的数据将被逐一处理并作为答案发送回客户端。
{"title":"Indonesian Sign Language API (OpenSIBI API) as The Gateway Services for Myo Armband","authors":"M. Zikky, R. Hakkun, Buchori Rafsanjani","doi":"10.25139/ijair.v1i1.2026","DOIUrl":"https://doi.org/10.25139/ijair.v1i1.2026","url":null,"abstract":"We create an API (Application Programming Interface) for Indonesian Sign Language (Sistem Isyarat Bahasa Indonesia/SIBI) which is called OpenSIBI. In this case study, we use the Myo Armband device to capture hand gesture data movement. It uses five sensors: Accelerometer, Gyroscope, Orientation, Orientation-Euler, and EMG. First, we record, convert and save those data into JSON dataset in the server as data learning. Then, every data request (trial data) from the client will compare them using k-NN Normalization process. OpenSIBI API works as the middleware which integrated to RabbitMQ as the queue request arranger. Every service request from the client will automatically spread to the server with the queue process. As the media observation, we create a client data request by SIBI Words and Alphabeth Game, which allows the user to answer several stages of puzzle-game with Indonesian Sign Language hand gesture. Game-player must use the Myo armband as an interactive device that reads the hand movement and its fingers for answering the questions given. Thus, the data will be classified and normalized by the k-NN algorithm, which will be processed on the server. In this process, data will pass OpenAPI SIBI (which connected to RabbitMQ) to queue every incoming data-request. So, the obtained data will be processed one by one and sent it back to the client as the answer.","PeriodicalId":365842,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133607587","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 : 2019-10-31DOI: 10.25139/ijair.v1i1.2029
S. Agustini, W. M. Rahmawati, M. Kurniawan
The rapid progression of exchange data by public networks is important, especially in information security. We need to keep our information safe from attackers or intruders. Furthermore, information security becomes needed for us. Many kind cipher methods of cryptography are improved to secure information such as monoalphabetic cipher and polyalphabetic cipher. Cryptography makes readable messages becoming non-readable messages. One of the popular algorithms of a polyalphabetic cipher is Vigenere cipher. Vigenere cipher has been used for a long time, but this algorithm has weaknesses. The calculation of the encryption process is only involving additive cipher, it makes this algorithm vulnerability to attacker based on frequency analysis of the letter. The proposed method of this research is making Vigenere cipher more complex by combining monoalphabetic cipher and Vigenere cipher. One of the monoalphabetic ciphers is Affine cipher. Affine cipher has two steps in the encryption process that are an additive cipher and a multiplicative cipher. Our proposed method has been simulated with Matlab. We also tested the vulnerability of the result of encryption by Vigenere Analyzer and Analysis Monoalphabetic Substitution. It shows that our method overcomes the weakness of Vigenere Cipher. Vigenere cipher and Affine cipher are classical cryptography that has a simple algorithm of cryptography. By combining Vigenere cipher and Affine cipher will make a new method that more complex algorithm.
{"title":"Modified Vegenere Cipher to Enhance Data Security Using Monoalphabetic Cipher","authors":"S. Agustini, W. M. Rahmawati, M. Kurniawan","doi":"10.25139/ijair.v1i1.2029","DOIUrl":"https://doi.org/10.25139/ijair.v1i1.2029","url":null,"abstract":"The rapid progression of exchange data by public networks is important, especially in information security. We need to keep our information safe from attackers or intruders. Furthermore, information security becomes needed for us. Many kind cipher methods of cryptography are improved to secure information such as monoalphabetic cipher and polyalphabetic cipher. Cryptography makes readable messages becoming non-readable messages. One of the popular algorithms of a polyalphabetic cipher is Vigenere cipher. Vigenere cipher has been used for a long time, but this algorithm has weaknesses. The calculation of the encryption process is only involving additive cipher, it makes this algorithm vulnerability to attacker based on frequency analysis of the letter. The proposed method of this research is making Vigenere cipher more complex by combining monoalphabetic cipher and Vigenere cipher. One of the monoalphabetic ciphers is Affine cipher. Affine cipher has two steps in the encryption process that are an additive cipher and a multiplicative cipher. Our proposed method has been simulated with Matlab. We also tested the vulnerability of the result of encryption by Vigenere Analyzer and Analysis Monoalphabetic Substitution. It shows that our method overcomes the weakness of Vigenere Cipher. Vigenere cipher and Affine cipher are classical cryptography that has a simple algorithm of cryptography. By combining Vigenere cipher and Affine cipher will make a new method that more complex algorithm.","PeriodicalId":365842,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130093363","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 : 2019-10-31DOI: 10.25139/ijair.v1i1.2030
Dian Ahkam Sani, Muchammad Saifulloh
The development of science and technology is one way to replace the method of human interaction with computers, one of which is to provide voice input. Conversion of sound into text form with the Backpropagation method can be understood and realized through feature extraction, including the use of Linear Predictive Coding (LPC). Linear Predictive Coding is one way to represent the signal in obtaining the features of each sound pattern. In brief, the way this speech recognition system worked was by inputting human voice through a microphone (analog signal) which then sampled with a sampling speed of 8000 Hz so that it became a digital signal with the assistance of sound card on the computer. The digital signal from the sample then entered the initial process using LPC, so that several LPC coefficients were obtained. The LPC outputs were then trained using the Backpropagation learning method. The results of the learning were classified with a word and stored in a database afterwards. The results of the test were in the form of an introduction program that able display the voice plots. the results of speech recognition with voice recognition percentage of respondents in the database iss 80% of the 100 data in the test in Real Time
{"title":"Speech to Text Processing for Interactive Agent of Virtual Tour Navigation","authors":"Dian Ahkam Sani, Muchammad Saifulloh","doi":"10.25139/ijair.v1i1.2030","DOIUrl":"https://doi.org/10.25139/ijair.v1i1.2030","url":null,"abstract":"The development of science and technology is one way to replace the method of human interaction with computers, one of which is to provide voice input. Conversion of sound into text form with the Backpropagation method can be understood and realized through feature extraction, including the use of Linear Predictive Coding (LPC). Linear Predictive Coding is one way to represent the signal in obtaining the features of each sound pattern. In brief, the way this speech recognition system worked was by inputting human voice through a microphone (analog signal) which then sampled with a sampling speed of 8000 Hz so that it became a digital signal with the assistance of sound card on the computer. The digital signal from the sample then entered the initial process using LPC, so that several LPC coefficients were obtained. The LPC outputs were then trained using the Backpropagation learning method. The results of the learning were classified with a word and stored in a database afterwards. The results of the test were in the form of an introduction program that able display the voice plots. the results of speech recognition with voice recognition percentage of respondents in the database iss 80% of the 100 data in the test in Real Time","PeriodicalId":365842,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116302592","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 : 2019-10-31DOI: 10.25139/ijair.v1i1.2025
Mohammad Zoqi Sarwani, Dian Ahkam Sani, Fitria Chabsah Fakhrini
Today the internet creates a new generation with modern culture that uses digital media. Social media is one of the popular digital media. Facebook is one of the social media that is quite liked by young people. They are accustomed to conveying their thoughts and expression through social media. Text mining analysis can be used to classify one's personality through social media with the probabilistic neural network algorithm. The text can be taken from the status that is on Facebook. In this study, there are three stages, namely text processing, weighting, and probabilistic neural networks for determining classification. Text processing consists of several processes, namely: tokenization, stopword, and steaming. The results of the text processing in the form of text are given a weight value to each word by using the Term Inverse Document Frequent (TF / IDF) method. In the final stage, the Probabilistic Neural Network Algorithm is used to classify personalities. This study uses 25 respondents, with 10 data as training data, and 15 data as testing data. The results of this study reached an accuracy of 60%.
{"title":"Personality Classification through Social Media Using Probabilistic Neural Network Algorithms","authors":"Mohammad Zoqi Sarwani, Dian Ahkam Sani, Fitria Chabsah Fakhrini","doi":"10.25139/ijair.v1i1.2025","DOIUrl":"https://doi.org/10.25139/ijair.v1i1.2025","url":null,"abstract":"Today the internet creates a new generation with modern culture that uses digital media. Social media is one of the popular digital media. Facebook is one of the social media that is quite liked by young people. They are accustomed to conveying their thoughts and expression through social media. Text mining analysis can be used to classify one's personality through social media with the probabilistic neural network algorithm. The text can be taken from the status that is on Facebook. In this study, there are three stages, namely text processing, weighting, and probabilistic neural networks for determining classification. Text processing consists of several processes, namely: tokenization, stopword, and steaming. The results of the text processing in the form of text are given a weight value to each word by using the Term Inverse Document Frequent (TF / IDF) method. In the final stage, the Probabilistic Neural Network Algorithm is used to classify personalities. This study uses 25 respondents, with 10 data as training data, and 15 data as testing data. The results of this study reached an accuracy of 60%.","PeriodicalId":365842,"journal":{"name":"International Journal of Artificial Intelligence & Robotics (IJAIR)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132551089","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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