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160. And the total of class is 93. To fill the 93 genes in one chromosome, the author should take a gene randomly chosen from those available are
160 genes.
2. Recombination Crossover
Crossing over is the important step to prevent the redundancy of the chromosome’s content in each individu and to produce a
variety of genes. In this case, the writer will use mutiple point cross over. It called multiple point because the point for crossing over is
more than one. Chromosome 1:
135 215
442 128
111 312
444 548
223 125
1 2
3 4
5 6
7 8
…….. 93 Chromosome 2:
241 131
211 117
413 251
222 547
121 441
` 1
2 3
4 5
6 7
8 …… 93
Crossover result 1: 241
131 211
128 111
312 444
547 121
441
Crossover result 2: 135
215 442
117 413
251 222
548 223
125
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From the result above, we can see that there are no same gene inside the Chromosome so there are no redundant data. From
the result above, we can see that the result of chromosome is more variable, this is because of using multiple point in crossing over so
the changes is not only in one place. For good lecturing scheduling system, it must not to have
same gene data in one chromosome, because it will produce the ambiguity in one lecturing schedule should not consist double
course subject . By using genetic algorithm, the problem occur can be
overcome and approved by showing no redundant data like the result of crossing over above.
3. Mutation
In this lecturing scheduling case at Informatic Engineering major, the writer choose the mutation which work only for moving
the gene inside the chromosome itself so it does not use any number from outside the chromosome. This type of mutation is
called as swap mutation. The mutation functions are for anticipating a subject which
does not match with the room, anticipating a willingness of faculty teaching and anticipating day that can not be in teaching.
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From the value of mutation above, it shows that the mutation only changing the structure of the gene itself. When the mutation is
done, the schedule will be different because the day, the session and the room changed.
4. Fitness Calculating
To calculate the fitness value, the writer must find the influential factors which are giving influence in lecturing scheduling at
Informatic Engineering major. When the writer tries to find the factor, the writer analyzes some conditions by paying attention to
some conditions, they are: a. Is there a time in the morning empty or not in use?
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1 To increase the productivity usage of the room, then in the desired sequence can be immediately used the room
since morning. 2 if the program offers a solution where there is a time
schedule in the morning which is not used, then this will reduce the fitness value of solution
b. Is there any lecturers who teach high frequency in one day? Lecturers teaching more than 5 credits hour are considered
to have high frequency. c. Are there any classes that have a high rate in each day?
1 To maintain the performance of students, expected no class schedules that are too crowded in one day.
2 If college class schedule offer solutions that too high, then the value of fitness solution reduced.
The writer uses the condition above as a parameter to find the fitness value. The parameter will be different for each case. It
depends on what the influential factor for each case. Based on the previous research Aria 2008, the formula for
calculating fitness value is:
Fitness
= 1B1xF1+B2xF2+B3xF3
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This below is the table of limitation and priority for scheduling.
TABLE 4.4. The Limitation and Priority Value for Scheduling Influential Factor
The weight Reason
The empty class in the morning 5
This is soft constraint which is not the
priority
Frequency of lecturer’s teaching 20
This is hard constraint because if this
happens then the lecturers performance
will be reduced
Frequency of class lectures 20
This is hard constraint because if this
happens then the students performance
will be reduced
The table above is showing that each influential factor has a weight to calculate. Based on Suyanto 2008, Hard constraint is a
condition that must not be violated and soft constraint is a
The variable that the writer uses at Informatic Engineering major: • F1= the highest class
• F2= the total of the empty class in the morning • F3= the total of the highest lecturing in a day from the lecturer
• B1= the weight of the highest class • B2= the weight of the empty class in the morning
• B3= the weight of the total of the highest lecturing in a day from the lecturer
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condition that not be the first priority. Based on Suyanto 2008, The hard constraint has a weight larger than soft constraint because
it is the priority that can not be violated.The Different case will produce different influential factor.
Based on the observation and data analyze, the writer found data at Informatic Engineering major like in the table below:
TABLE 4.5. Influential Factor for Calculating Fitness at Informatic Engineering Major Islamic University Syarif Hidayatullah Jakarta
Influential Factor The Total Number
The empty class in the morning 4
Frequency of lecturer’s teaching 5
Frequency of class usage 5
The empty class in the morning is a situation in where no process study at 07.30 Am because the morning class starts from
that time. The frequency of lecturer teaching is a condition in which a
lecturer has high frequency in giving lecture in each day. A lecturer categorized as a lecturer who has high frequency if heshe teaches
more than 5 credits hour in a day. This kind of influential factor could give a bad performance to the lecturer and to the student
brain.
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The frequency of class is a condition when the class capability to receive the subject. If a class full from 1
st
session till the last session, it will give bad effect to the student because the
student will have a bad performance in receiving the lecture. Fitness Calculating at Informatic Engineering Major Islamic
University, year 20092010, 28 course subjects, 4 classes A-D and 8 room classes before applying Genetic Algorithm into
scheduling system:
B1 is the weight for empty class in the morning, B2 is the weight for giving lecture in high frequency and B3 is the weight for
class in high frequency. From the result above, the fitness value is 0.0045. It means that the lecturing scheduling system at Informatic
Engineering Major is not efficient. Based on the previous research Aria 2008, the value for the efficient scheduling is 0.008. When
the system reaches the fitness value under the 0.008, it indicates that: the clash that still occur in the use of class, there remains the
possibility of an empty class room in the morning and lecturers teaching with high frequency.
Fitness = 1B1Xf1+B2XF2+B3XF3
= 15x4+20x5+20x5
= 1220
= 0.004545
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5. Selection
