Garuda - Garba Rujukan Digital

Nurochman Nurochman

UIN Sunan Kalijaga

Author-ID : 2654478

Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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Articles

Implementasi Algoritma Genetika untuk Penjadwalan Instruktur Training ICT UIN Sunan Kalijaga Niki Min Hidayati Robbi; Nurochman Nurochman
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 1 No. 3 (2017): Januari 2017
Publisher : UIN Sunan Kalijaga Yogyakarta

ICT training instructor scheduling involves components such as the division of the day, sessions, classroom, and availability time of instructor which is different every day. One of the methods of artificial intelligence that is suitable for case scheduling is a Genetic Algorithm. Genetic algorithm succesfully impelemented for scheduling ICT training instructor with the parameter crossover probability (Pc) 0.4, the probability of mutation (Pm) 0.1, and the total population of 30 individuals. The best fitness value is 0.9523 with a 1 value error on constraint division of classrooms that weighs 0,05. Keywords: Genetic Algorithm, Scheduling

Sistem Pakar Rekomendasi Profesi Berdasarkan Multiple Intelligences Menggunakan Teorema Bayesian Elvanisa Ayu Muhsina; Nurochman Nurochman
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 2 No. 1 (2017): Mei 2017
Publisher : UIN Sunan Kalijaga Yogyakarta

Intelligence is perhaps to be the one of the most logical way to determine how smart people is. That fact has always been a problem at job because there are number of job that attract people but require a high GPA for them. Employee with high GPA doesn’t always fit in his skill and work role. They unable to understand and maintain their performance. This expert system is a necessary for recommend job using Intelligence. This research use a Bayesian theorem calculation to find out probability value and job recommendation. The value of MI (Multiple Intelligences)’s user, MI probability to a job and job probability to previous result without any evidence produce a Calculation Variable.Result of the test shows output recommendation as expert system to 81.25% match with expert recommendation. 100% users statistically states the system running well. Expert system usability test shows 80% users strongly agree, 15.7% users agree and 4.3% users are neutral.Keywords: Multiple Intelligences, Profession, Bayesian theorem

Kriptanalisis Algoritma Vigenere Chiper dengan Algoritma Genetika untuk Penentuan Kata Kunci Tsuraya Ats Tsauri; Nurochman Nurochman
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 2 No. 2 (2017): September 2017
Publisher : UIN Sunan Kalijaga Yogyakarta

Cryptanalysis is the art to solve without key ciphers, in contrast to cryptography, namely to maintain the confidentiality of data by encode a plaintext. Vigenere ciphers is one of the kriptanalisis algorithm. Brute force attack and exhaustive attack is a technique of kriptanalisis vigenere ciphers, but less optimal in result. In my research this time proposed a way of solving the secret key (Cryptanalysis), using a genetic algorithm on text Indonesia-speaking ciphers. .The first step in this study performed a chromosome design would be the length of the keyword, the method used is the coincidence index (IOC), the IOC values with text Indonesian is 0,075. To get the value of fitness done the search weights by comparison Word decryption of keywords with Indonesian Language Dictionary. Genetic algorithms will seek all possible keywords, there are genetic algorithms in the process of reproduction includes crossover, mutation and elitisme. There are parameters that are included in the process of a keyword search that is the value of the probability of crossover, mutation probability and population, number of the parameter that you want to optimize to get keywordsThis analysis is performed on the five scenarios with any combination of parameters, number of characters chipertext and two types of different keywords. After 1000 times testing with a combination of parameters generated 467 the data successfully guessing keywords within approximately 60 minutes. With the testing of two different keywords and two different ciphers text amount done by as much as five times the test showed that both have the value of the average test time the fastest standard deviation value. After an analysis of the results of the research, the optimal parameters is obtained with a value Pc 0.09, Pm 0.3 and Pop_size 20.Keywords : Cryptanalysis, Genetic Algorithm, Vigenere Chiper, Index Coincidence.

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