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period using some methods like payback period. Schedule feasibility is a measure of how reasonable the project timetable is.
Given our technical expertise, are the project deadlines reasonable? Some projects are initiated with specific deadlines. You need to
determine whether the deadlines are mandatory or desirable.
2.14. XAMPP
XAMPP is a open source software which is supporting many operating system and it’s a compilation from some programs. The function
is as a stand alone web server localhost, it is consisting of Apache HTTP Server, MySQL database, and language translator which is written by using
PHP and PERL. XAMPP is the abbreviation from X4 operating system, Apache, MySQL, PHP and Perl. This program provided in GNU, it is a web
server that easy in use and suitable for dynamic web. To get the XAMPP installer, we can download it from the official website
2.15. Related Works
The writer has collected and learned some common research study about genetic algorithm. In this chance, the writer will explain one by one
about the common reasearch study that they related with the writer’s thesis writing. These is the common research study from the previous thesis
writing:
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According to Teddy Chandra Taliwang 2006, his thesis writing which the title is “Optimasi penjadwalan sidang tugas akhir dengan
menggunakan metode fuzzy relation dan genetic algorithms”, the schedule
in his universities was not flexible because there must five sessions in a day for all final year project period then the coordinator in that universities still
use manual way to match the schedule. From the problem above, Teddy 2006, p.1 stated that he will use a genetic algorithm to optimize the
scheduling system. After doing research and test, Teddy 2006, p.73 summarized that the software is able to use in scheduling system.
Based on Steven 2007, Genetic Algorithm is used to find the best solution. This method is based on genetic mechanism in biology process. He
stated that in the genetic algorithm, the mechanisms that will be used are mutation and crossover. Steve thesis writing title is “Perancangan Program
Aplikasi Penjadwalan Sidang Tugas Akhir Dengan Menggunakan Metode Fuzzy Relation Dan Genetic Algorithm STUDI KASUS : UNIVERSITAS
BINA NUSANTARA”. Based on Holland 1975, Genetic Algorithm GA is a robust search
method based on analogies to biology and genetics. Survival of the test among a population of individuals, selection criteria, and reproduction
strategies are concepts copied from the natural life and used as operators in this artifcial environment. According to Rogers 1992, The GA has some
advantageous features:
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1. GA begins the search with a population of parameter realizations, rather than a single realization as most of the conventional
optimization methods might. Each set of possible con_guration of the decision variables is referred as one realization or member of the
population. In this way, the search domain is covered in a random distribution;
2. The realizations are perturbed by probabilistic rules rather than deterministic ones;
3. The parameter itself is not manipulated directly by the GA operators. GA would alter the chromosome or and ones representing the whole
set of parameters put all together in one binary entity. For binary alphabets, the smaller piece of a chromosome is called bit andcan have
the values zero or one. In this way, the chromosome is the set of all parameters bits.
Based on Adam Marczyk 2004, A genetic algorithm or GA for short is a programming technique that mimics biological evolution as a
problem-solving strategy. Given a specific problem to solve, the input to the GA is a set of potential solutions to that problem, encoded in some fashion,
and a metric called a fitness function that allows each candidate to be quantitatively evaluated. These candidates may be solutions already known
to work, with the aim of the GA being to improve them, but more often they are generated at random.
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Based on Aria 2008, Genetic algorithm is an optimazion algorithm that it
created based on nature of genetic. There are many kinds in genetic algorithm. Genetic algorithm has six steps in doing permutation, the steps are :
Starting to create new population 1. Coding
2. Fitness Value 3. Selection
4. Reproduction 5. Mutation
6. Stop Condition
Based on Gen and Cheng 1997, Genetic Algorithm is evolution in artificial intelligence. Genetic algorithm is a kind of search algorithm which is
based on natural genetic. Genetic algorithm is different with conventional searching method, algorithm genetic comes from a group of solution which
produced randomly. This group, we called population. According to Goldberg 1989, After producing some generations, genetic algorithm will convert to the
best cromossom which it to get the optimal solution. According to Fadlisyah 2009, Genetic algorithm is a stochastic search
technic which works based on permutation and natural genetic. Algorithm genetic is different with conventional algorithm because it starts with random problem
solving which it called population.
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Based on Suyanto 2005, The advantages of using genetic algorithm are easy in implementing and the capability to find the best solution for high problem
dimension. Genetic algorithm is very useful and efficient to solve problems with the characteristic such as:
1. High problem dimension 2. There is no knowledge in identifying problem
3. There is not efficient mathematical analysis 4. To overcome the problem when conventional method can not solve it
5. It consists of time limitation ex : real time system
Based on Suyanto 2005, Genetic algorithm already used in many applications to solve the problem in technology modeling, business and
entertainment. The example of them are:
1. Optimization
Genetic algorithm is used in numeric optimation such Traveling Salesman Problem TSP, Integrated Circuit Design, Job Shop
Scheduling and video optimation.
2. Automatic Programing
Genetic algorithm already used in evolution process, such cellular automata and sorting networks.
3. Machine Learning
Genetic algorithm success in predicting protein structure, neural networks, learning classifier system or symbolic production system.
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4. Economic Model
Genetic algorithm already used in designing inovation process and bidding strategies.
5. Imunitation System Model
Genetic algorithm successed in designing nature immunitation system include somatic mutation.
6. Ecology Model
Genetic algorithm successed to design ecology phenomenon such as host – parasite co – evolution and symbiosis.
7. Interaction between Evolution and Learn
Genetic algorithm is used to learn how process study an individu can impact evolution process a species and vice versa.
The previous researcher, Annisa Primasari 2009 developed a system which the title is “Pengembangan Sistem Informasi Penjadwalan Kuliah “. Her system
is about lecturing scheduling system, but she did not use any specific method to build it. The system was tested and ran successfully. Even it has the advantage in
use, it has a weakness such as: the system could not be use to find the free room for lecturing process so the lecturer and the academic still find by manual way. In
this case, the writer find that the previous research was made by Annisa Primasari 2009 could not do best optimization so it still use human effort for finding the
solution.
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The writer takes a previous research which the title was” Aplikasi Algoritma
Genetik Untuk Optimasi Penjadwalan Kegiatan Belajar Mengajar“ . It was made
by Ivan Nugraha 2008 from Institute Technology Bandung ITB. The researcher successfully implemented the genetic algorithm to optimize the
lecturing scheduling system; his last fitness value is 0.0083. It means that the genetic algorithm could be successfully implemented and it showed that the last
performance is better than before. The research which it was made by Muhammad Aria and title of the
research was “Aplikasi Algoritma Genetik Untuk Optimasi Penjadwalan Mata Kuliah”
. His research was about develop in an aplication by using genetic algorithm for lecturing scheduling system. He tested the genetic algorithm by
using LabVIEW 6.1 and the test already done for 100 generations in which there was 10 cromosom in each generation. By using genetic algorithm, the
optimization could reach for lecturing scheduling system. In this research, Muhammad Aria successes in finding the best value for the fitness value so it can
be use for predicting the next generation. The weaknesses of this system are the output only producing the lecturing scheduling and there is no information about
the free room for lecturing process. Based on the explanation above, the writer will use a genetic algorithm
method in her thesis writing which the title is “Lecturing Scheduling System at Informatic Engineering Major Islamic University Syarif Hidayatullah Jakarta
Using Genetic Algorithm” . A genetic algorithm is suitable for achieving the best
solution in scheduling case and optimizing case.
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2.16. Summary
