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Anik Vega Vitianingsih
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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. 1 No. 1 (2019): November 2019" : 5 Documents clear
Genetic Algorithm for Optimizing Traveling Salesman Problems with Time Windows (TSP-TW) 1
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 1 No. 1 (2019): November 2019
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (570.738 KB) | DOI: 10.25139/ijair.v1i1.2024

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.
Personality Classification through Social Media Using Probabilistic Neural Network Algorithms 1
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 1 No. 1 (2019): November 2019
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (305.425 KB) | DOI: 10.25139/ijair.v1i1.2025

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%.
Indonesian Sign Language API (OpenSIBI API) as The Gateway Services for Myo Armband 1
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 1 No. 1 (2019): November 2019
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (757.419 KB) | DOI: 10.25139/ijair.v1i1.2026

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.
Modified Vegenere Cipher to Enhance Data Security Using Monoalphabetic Cipher 1
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 1 No. 1 (2019): November 2019
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (611.85 KB) | DOI: 10.25139/ijair.v1i1.2029

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.
Speech to Text Processing for Interactive Agent of Virtual Tour Navigation 1
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 1 No. 1 (2019): November 2019
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (271.812 KB) | DOI: 10.25139/ijair.v1i1.2030

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

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