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IJISTECH (International Journal Of Information System & Technology)
ISSN : 25807250     EISSN : -     DOI : -
Core Subject : Science,
IJISTECH (International Journal Of Information System & Technology) is published with both online and print versions. The journal covers the frontier issues in the computer science and their applications in business, industry and other subjects. The computer science is a branch of engineering science that studies computable processes and structures. It contains theories for understanding computing systems and methods; computational algorithms and tools; methodologies for testing of concepts. The subjects covered by the journal include artificial intelligence, bioinformatics, computational statistics, database, data mining, financial engineering, hardware systems, imaging engineering, internet computing, networking, scientific computing, software engineering, and their applications etc.
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Articles 5 Documents
Search results for , issue " Vol 1, No 1 (2017): November" : 5 Documents clear
Generating Mersenne Prime Number Using Rabin Miller Primality Probability Test to Get Big Prime Number in RSA Cryptography Apdilah, Dicky; Khairina, Nurul; Harahap, Muhammad Khoiruddin
IJISTECH (International Journal Of Information System & Technology) Vol 1, No 1 (2017): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v1i1.1

Abstract

Cryptography RSA method (Rivest - Shamir - Adelman) require large-scale primes to obtain high security that is in greater than or equal to 512, in the process to getting the securities is done to generation or generate prime numbers greater than or equal to 512. Using the Sieve of Eratosthenes is needed to bring up a list of small prime numbers to use as a large prime numbers, the numbers from the result would be combined, so the prime numbers are more produced by the combination Eratosthenes. In this case the prime numbers that are in the range 1500 < prime <2000, for the next step the result of the generation it processed by using the Rabin - Miller Primarily Test. Cryptography RSA method (Rivest - Shamir - Adleman) with the large-scale prime numbers would got securities or data security is better because the difficulty to describe the RSA code gain if it has no RSA Key same with data sender.
Implementation and Analysis Zhu-Takaoka Algorithm and Knuth-Morris-Pratt Algorithm for Dictionary of Computer Application Based on Android Handrizal, Handrizal; Budiman, Andri; Ardani, Desy Rahayu
IJISTECH (International Journal Of Information System & Technology) Vol 1, No 1 (2017): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v1i1.2

Abstract

The string matching algorithm is the one of the most important parts in the various processes related to data and text types, which is the word search on computer dictionary. Computers have a basic role in the field of education, especially in teaching and learning activities. So that the classical learning model, that is by using the book as learning resource can be boring. To make it easier for users who searching words, we made an offline dictionary application based on Android by applying Zhu-Takaoka algorithm and Knuth-Morris-Pratt algorithm. The performance of Zhu-Takaoka is doing a search starts from the end of pattern that is tailored to the text, but in Knuth-Morris-Pratt algorithm starts from the beginning of pattern till match which the pattern used is word searched. The result of this research indicates that the Zhu-Takaoka algorithm is faster than the Knuth-Morris-Pratt algorithm which showed the running time of each algorithm.
Neural Network Analysis With Backpropogation In Predicting Human Development Index (HDI) Component by Regency/City In North Sumatera Siregar, Muhammad Noor Hasan
IJISTECH (International Journal Of Information System & Technology) Vol 1, No 1 (2017): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v1i1.3

Abstract

Human Development Index (HDI) measures human development outcomes based on a number of basic components of quality of life. As a measure of the quality of life, HDI is built through a basic three-dimensional approach. Data obtained from the Central Bureau of Statistics 2015 for Human Development Index (HDI) by Regency / City in North Sumatera Province consisting of 32 alternatives and with 4 parameters ie life expectancy (year), expectation, school length (%), the average length of school (year) and per capita real expenditure (Rp). By using backpropagation obtained result of 6 testing of architecture pattern that is: 4-5-1, 4-10-1, 4-5-10-1, 4-10-5-1, 4-10-20-1 and 4- 15-20-1 obtained best architectural pattern is 4-10-20-1 with epoch 2126, error 0.0011757393, execution time 00:16 and accuracy 100%.
Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting) Siregar, Sandy Putra; Wanto, Anjar
IJISTECH (International Journal Of Information System & Technology) Vol 1, No 1 (2017): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v1i1.4

Abstract

Artificial Neural Networks are a computational paradigm formed based on the neural structure of intelligent organisms to gain better knowledge. Artificial neural networks are often used for various computing purposes. One of them is for prediction (forecasting) data. The type of artificial neural network that is often used for prediction is the artificial neural network backpropagation because the backpropagation algorithm is able to learn from previous data and recognize the data pattern. So from this pattern backpropagation able to analyze and predict what will happen in the future. In this study, the data to be predicted is Human Development Index data from 2011 to 2015. Data sourced from the Central Bureau of Statistics of North Sumatra. This research uses 5 architectural models: 3-8-1, 3-18-1, 3-28-1, 3-16-1 and 3-48-1. From the 5 models of this architecture, the best accuracy is obtained from the architectural model 3-48-1 with 100% accuracy rate, with the epoch of 5480 iterations and MSE 0.0006386600 with error level 0.001 to 0.05. Thus, backpropagation algorithm using 3-48-1 model is good enough when used for data prediction.
Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density Wanto, Anjar; Windarto, Agus Perdana; Hartama, Dedy; Parlina, Iin
IJISTECH (International Journal Of Information System & Technology) Vol 1, No 1 (2017): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v1i1.6

Abstract

Artificial Neural Network (ANN) is often used to solve forecasting cases. As in this study. The artificial neural network used is with backpropagation algorithm. The study focused on cases concerning overcrowding forecasting based District in Simalungun in Indonesia in 2010-2015. The data source comes from the Central Bureau of Statistics of Simalungun Regency. The population density forecasting its future will be processed using backpropagation algorithm focused on binary sigmoid function (logsig) and a linear function of identity (purelin) with 5 network architecture model used the 3-5-1, 3-10-1, 3-5 -10-1, 3-5-15-1 and 3-10-15-1. Results from 5 to architectural models using Neural Networks Backpropagation with binary sigmoid function and identity functions vary greatly, but the best is 3-5-1 models with an accuracy of 94%, MSE, and the epoch 0.0025448 6843 iterations. Thus, the use of binary sigmoid activation function (logsig) and the identity function (purelin) on Backpropagation Neural Networks for forecasting the population density is very good, as evidenced by the high accuracy results achieved.

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