Amir Mahmud Husein, Amir Mahmud
Unknown Affiliation

Published : 4 Documents

Found 3 Documents

Analisa Frekuensi Hasil Enkripsi Pada Algoritma Kriptografi Blowfish Terhadap Keamanan Informasi Riza, Ferdy; Sridewi, Nurmala; Husein, Amir Mahmud; Harahap, Muhammad Khoiruddin
Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP) Vol 1 No 1 (2018): JUTIkomp Volume 1 Nomor 1 April Periode 2018
Publisher : Universitas Prima Indonesia

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The ease of sending data with the development of internet technology technology is now a concern, especially the problem of data confidentiality, integrity and information security. Cryptography is one of the techniques used to maintain data confidentiality and information security, the application of cryptographic techniques for information security and data integrity is highly dependent on the formation of keys. In this study proposed a frequency analysis approach to measure the level of information security of blowfish encryption results to determine the distribution form of each character used in the text and find out the exact frequency of each character used in the test text data. The encryption algorithm and description of blowfish method against plaintext are proven to be accurate, but the longer the key character used will greatly affect the level of information security that came  from encryption process, this is based on the results of the frequency analysis conducted.
Analisis Performa Rasio Kompresi Pada Metode Differensiasi ASCII Dan Lempel Ziv Welch (LZW) Tommy, Tommy; Siregar, Rosyidah; Husein, Amir Mahmud; Harahap, Mawaddah; Riza, Ferdy
Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP) Vol 1 No 2 (2018): Jutikomp Volume 1 Nomor 2 Oktober 2018
Publisher : Universitas Prima Indonesia

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ASCII differentiation is a compression method that utilizes the difference value or the difference between the bytes contained in the input character. Technically, the ASCII differentiation method can be done using a coding dictionary or using windowing block instead of the coding dictionary. Previous research that has been carried out shows that the ASCII differentiation compression ratio is good enough but still needs to be analyzed on performance from the perspective of the compression ratio of the method compared to other methods that have been widely used today. In this study an analysis of the comparison of the ASCII Difference method with other compression methods such as LZW will be carried out. The selection of LZW itself is done by reason of the number of data compression applications that use the method so that it can be the right benchmark. Comparison of the compression ratio performed shows the results of ASCII differentiation have advantages compared to LZW, especially in small input characters. Whereas in large input characters, LZW can optimize the probability of pairs of characters that appear compared to ASCII differentiation which is glued to the difference values ​​in each block of input characters so that in large size characters LZW has a greater compression ratio compared to ASCII differentiation.
Generative Adversarial Networks Time Series Models to Forecast Medicine Daily Sales in Hospital Husein, Amir Mahmud; Arsyal, Muhammad; Sinaga, Sutrisno; Syahputa, Hendra
SinkrOn Vol 3 No 2 (2019): SinkrOn Volume 3 Number 2, April 2019
Publisher : Politeknik Ganesha Medan

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The success of the work of Generative Adversarial Networks (GAN) has recently achieved great success in many fields, such as stock market prediction, portfolio optimization, financial information processing and trading execution strategies, because the GAN model generates seemingly realistic data with models generator and discriminator .Planning for drug needs that are not optimal will have an impact on hospital services and economics, so it requires a reliable and accurate prediction model with the aim of minimizing the occurrence of shortages and excess stock, In this paper, we propose the GAN architecture to estimate the amount of drug sales in the next one week by using the drug usage data for the last four years (2015-2018) for training, while testing using data running in 2019 year , the classification results will be evaluated by Actual data uses indicators of Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). From the results of the experiment, seen from the value ​​of MAE, RMSE and MAPE, the proposed model has promising performance, but it still needs to be developed to explore ways to extract factors that are more valuable and influential in the trend disease progression, thus helping in the selection of optimal drugs