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All Journal International Journal of Artificial Intelligence and Robotics (IJAIR)
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Institut Teknologi Adhi Tama Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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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.
A LOF K-Means Clustering on Hotspot Data 1
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 2 No. 1 (2020): May 2020
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (556.964 KB) | DOI: 10.25139/ijair.v2i1.2634

Abstract

K-Means is the most popular of clustering method, but its drawback is sensitivity to outliers. This paper discusses the addition of the outlier removal method to the K-Means method to improve the performance of clustering. The outlier removal method was added to the Local Outlier Factor (LOF). LOF is the representative outlier’s detection algorithm based on density. In this research, the method is called LOF K-Means. The first applying clustering by using the K-Means method on hotspot data and then finding outliers using the LOF method.  The object detected outliers are then removed.  Then new centroid for each group is obtained using the K-Means method again. This dataset was taken from the FIRM are provided by the National Aeronautics and Space Administration (NASA).  Clustering was done by varying the number of clusters (k = 10, 15, 20, 25, 30, 35, 40, 45 and 50) with cluster optimal is k = 20. The result based on the value of Sum of Squared Error (SSE) shown the LOF K-Means method was better than the K-Means method. 
Comparison of Clustering K-Means, Fuzzy C-Means, and Linkage for Nasa Active Fire Dataset 1
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 2 No. 2 (2020): November 2020
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2292.074 KB) | DOI: 10.25139/ijair.v2i2.3030

Abstract

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.
Message Security Using Rivest-Shamir-Adleman Cryptography and Least Significant Bit Steganography with Video Platform 1
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 2 No. 2 (2020): November 2020
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2176.307 KB) | DOI: 10.25139/ijair.v2i2.3150

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.
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