PENGGABUNGAN ALGORITMA GENETIKA DENGAN TABU SEARCH UNTUK PENGEMBANGAN METODE PENJADWALAN MATA KULIAH DI UNIVERSITAS SEBELAS MARET SURAKARTA.
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PENGGABUNGAN ALGORITMA GENETIKA DENGAN TABU SEARCH
UNTUK PENGEMBANGAN METODE PENJADWALAN MATA KULIAH
DI UNIVERSITAS SEBELAS MARET SURAKARTA
SKRIPSI
Diajukan untuk Memenuhi Salah Satu Syarat Mencapai Gelar Strata Satu
Jurusan Informatika
Disusun oleh :
Ahmad Miftah Fajrin
NIM. M0510003
JURUSAN INFORMATIKA
FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM
UNIVERSITAS SEBELAS MARET
SURAKARTA
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MOTTO
Lakukanlah kebaikan tanpa mengharapkan kembali
(Penulis)
Without action, you aren’t going anywhere.
(Mahatma Gandhi)
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PERSEMBAHAN
Karya ini Penulis persembahkan kepada:
“Ayah, Ibu, Adik, dan seluruh keluarga besar.”
“Teman-teman Informatika 2010 khususnya Ashar, Aji, Yusuf, Hedik, Cerren,
Adit, Taufik, Viko, April, Lydia, Dian”
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KATA PENGANTAR
Segala puji dan syukur penulis ucapkan kepada Allah SWT, yang hanya karena
rahmat dan karunia-Nya, penulis dapat menyelesaikan Tugas Akhir dengan judul
“Penggabungan Algoritma Genetika dengan Tabu Search untuk Pengembangan
Metode Penjadwalan Mata Kuliah di Universitas Sebelas Maret Surakarta”. Penulis
menyadari akan keterbatasan yang dimiliki. Begitu banyak bantuan dan bimbingan
yang diberikan dalam penyusunan Tugas Akhir ini. Oleh karena itu, penulis
mengucapkan terima kasih kepada :
1.
Bapak Drs. Bambang Harjito, M.App.Sc., Ph.D. selaku Ketua Jurusan
Informatika FMIPA UNS sekaligus sebagai anggota penguji yang telah
memberikan masukan, kritik dan saran yang membangun,
2.
Bapak Antonius Bima Murti Wijaya S.T.,M.T. Selaku Dosen Pembimbing I
yang telah memberikan pengarahan selama proses penyusunan Tugas Akhir
ini,
3.
Bapak Abdul Aziz, S.Kom, M.Cs. selaku Dosen Pembimbing II yang telah
memberikan masukan, kritik dan saran yang membangun,
4.
Bapak Drs. YS. Palgunadi, M.Sc. sebagai anggota penguji yang telah
memberikan masukan, kritik dan saran yang membangun
5.
Ayah, ibu, dan adik-adikku yang senantiasa memberikan dukungan dan
motivasi,
6.
Teman-teman yang senantiasa selalu berbagi pengetahuan, pengalaman, dan
memberikan dukungan dan motivasi.
Semoga Tugas Akhir ini bermanfaat bagi semua pihak yang berkepentingan.
Surakarta, Januari 2015
Penulis
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THE MERGER OF GENETIC ALGORITHM WITH TABU SEARCH
ALGORITHM FOR DEVELOPMENT OF COURSE TIMETABLING METHOD
AT SEBELAS MARET UNIVERSITY
Ahmad Miftah Fajrin
Jurusan Informatika. Fakultas Matematika dan Ilmu Pengetahuan Alam.
Universitas Sebelas Maret
ABSTRACT
University course timetabling is often to be critical concern for most
education institution. Sebelas Maret University (UNS) has developed a
computerized sxystem for a timetabling problem using simple additive weighting
algorithm. However, there are some study programs do not satisfied of the result
because there are many soft constraint are violated.
The purpose of this research is to develop the course timetabling algorithm
by combining the genetic algorithm and tabu search algorithm. Genetic Algorithm
has the power of a good exploitation to produce a visible solution meanwhile tabu
search can increase the power of searching in genetic algorithm and reduce the
possibility of trapped in local optimum
The combining of genetic algorithm with tabu search can decrease the
fitness by 47% for the smallest dataset and 21.3% for the biggest dataset compared
by simple additive weighting algorithm. The merge of genetic algorithm with tabu
search method can be used for the development of course timetabling method at
UNS.
Kata Kunci — Development of Course Timetabling Method, Genetic Algorithm,
Tabu search.
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PENGGABUNGAN ALGORITMA GENETIKA DENGAN TABU SEARCH
UNTUK PENGEMBANGAN METODE PENJADWALAN MATA KULIAH DI
UNIVERSITAS SEBELAS MARET SURAKARTA
Ahmad Miftah Fajrin
Jurusan Informatika. Fakultas Matematika dan Ilmu Pengetahuan Alam.
Universitas Sebelas Maret
ABSTRAK
Penjadwalan mata kuliah sering menjadi bahan perhatian penting bagi setiap
organisasi atau institusi khususnya bagian pendidikan di universitas. Universitas
Sebelas Maret (UNS) telah membangun sistem komputerisasi untuk menyelesaikan
masalah penjadwalan mata kuliah menggunakan algoritma simpe additive
weighting. Akan tetapi, dibeberapa prodi masih ada yang kurang puas karena
banyak soft constraint yang dilanggar.
Tujuan dari penelitian ini adalah mengembangkan algoritma penjadwalan
mata kuliah dengan menggabungkan algoritma genetika dengan tabu search.
Algoritma genetika mempunyai kekuatan exploitation atau pencarian yang baik
sehingga menghasilkan solusi yang layak dan algoritma tabu search akan
meningkatkan kekuatan pencarian di algoritma genetika sehingga dapat
mengurangi kemungkinan terjebak di local optimum.
Penggabungan algoritma genetika dengan tabu search dapat menurunkan
nilai fitness sebesar 47% untuk dataset yang jumlahnya paling kecil dan 21.3%
untuk dataset yang jumlahnya paling besar dibandingkan dengan algoritma simple
additive weighting. Secara umum metode penggabungan algoritma genetika dengan
tabu search dapat digunakan untuk pengembangan metode penjadwalan mata
kuliah di UNS.
Kata Kunci — Pengembangan Metode Penjadwalan Mata Kuliah, Algoritma
genetika, Tabu search.
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DAFTAR ISI
HALAMAN JUDUL................................................................................................ i
HALAMAN PERSETUJUAN ............................... Error! Bookmark not defined.
HALAMAN PENGESAHAN ................................ Error! Bookmark not defined.
MOTTO ................................................................................................................. iv
PERSEMBAHAN ................................................................................................... v
KATA PENGANTAR ........................................................................................... vi
ABSTRACT .......................................................................................................... vii
ABSTRAK ........................................................................................................... viii
DAFTAR ISI .......................................................................................................... ix
DAFTAR TABEL ................................................................................................. xii
DAFTAR GAMBAR ........................................................................................... xiii
DAFTAR LAMPIRAN ........................................................................................ xiv
BAB I PENDAHULUAN ...................................................................................... 1
1.1.
Latar Belakang ......................................................................................... 1
1.2.
Rumusan Masalah .................................................................................... 3
1.3.
Batasan Masalah ....................................................................................... 3
1.4.
Tujuan Penelitian ...................................................................................... 4
1.5.
Manfaat Penelitian .................................................................................... 4
1.6.
Sistematika Penulisan ............................................................................... 4
BAB II LANDASAN TEORI ................................................................................ 6
2.1.
Landasan Teori ......................................................................................... 6
2.1.1. Penjadwalan Mata Kuliah ..................................................................... 6
2.1.2. Algoritma Simple Additive Weighting .................................................. 6
2.1.3. Algoritma Genetika............................................................................... 7
2.1.3.1. Chromosom Representation .............................................................. 9
2.1.3.2. Initial Population .............................................................................. 9
2.1.3.3. Evaluate Fitness .............................................................................. 11
2.1.3.4. Selection .......................................................................................... 12
2.1.3.5. Crossover ........................................................................................ 13
2.1.3.6. Mutation ..........................................................................................
13
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2.1.3.7. Elitism ............................................................................................. 14
2.1.4. Tabu Search ........................................................................................ 14
2.1.5. Hybrid Algoritma Genetika ................................................................ 16
2.2.
Penelitian Terkait ................................................................................... 16
2.3.
Rangkuman Hasil Penelitian Terdahulu ................................................. 18
BAB III METODOLOGI PENELITIAN ............................................................ 20
3.1.
Penentuan Constraint ............................................................................. 20
3.1.1. Hard Constraint .................................................................................. 20
3.1.2. Soft Constraint .................................................................................... 22
3.2.
Penentuan Nilai Pinalti ........................................................................... 22
3.3
Desain Constraint ................................................................................... 23
3.4
Desain Algoritma Genetika. ................................................................... 23
3.5
Desain Algoritma Genetika dengan Tabu Search. ................................. 24
3.6
Implementasi .......................................................................................... 25
3.7
Pengujian ................................................................................................ 25
3.7.1
Pengujian Constraint ........................................................................... 25
3.7.2
Pengujian Algoritma ........................................................................... 25
BAB IV HASIL DAN PEMBAHASAN ............................................................. 26
4.1.
Tahap Pembahasan ................................................................................. 26
4.1.1. Desain Algoritma Genetika ................................................................ 26
4.1.1.1. Chromomosom Representation ....................................................... 26
4.1.1.2. Initial Population ............................................................................ 26
4.1.1.3. Evaluate Fitness .............................................................................. 29
4.1.1.4. Selection .......................................................................................... 29
4.1.1.5. Crossover ........................................................................................ 31
4.1.1.6. Mutation .......................................................................................... 32
4.1.1.7. Elitsm .............................................................................................. 32
4.1.2. Desain Penggabungan Algoritma Genetika dengan Tabu Search ...... 33
4.2.
Tahap Hasil ............................................................................................. 36
4.2.1. Pengujian Constraint .......................................................................... 36
4.2.2. Visualisasi Algoritma ......................................................................... 37
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4.2.3. Analisis Empiris..................................................................................
38
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4.2.3.1. Perbandingan Metode...................................................................... 38
4.2.3.2. Analisis Hasil Perbandingan ............................................................ 43
BAB V KESIMPULAN DAN SARAN ............................................................... 44
5.1.
Kesimpulan ............................................................................................. 44
5.2.
Saran ....................................................................................................... 44
BAB VI ................................................................................................................. 45
DAFTAR PUSTAKA ........................................................................................... 45
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DAFTAR TABEL
Tabel 3.1 Hard Constraint ..................................................................................... 21
Tabel 3.2 Soft Constraint ...................................................................................... 22
Tabel 4.1 Mata Kuliah Penawaran S1 Informatika ............................................... 26
Tabel 4.2 Chromosom 1 ........................................................................................ 27
Tabel 4.3 Chromosom 2 ........................................................................................ 27
Tabel 4.4 Chromosom 3 ........................................................................................ 27
Tabel 4.5 Chromosom 4 ........................................................................................ 27
Tabel 4.6 Chromosom hasil seleksi ...................................................................... 30
Tabel 4.7 Susunan Parent 1 ................................................................................... 30
Tabel 4.8 Susunan Parent 2 ................................................................................... 30
Tabel 4.9 Susunan Offerspring ............................................................................. 32
Tabel 4.10 Susunan Offerspring Baru ................................................................... 32
Tabel 4.11 Solusi Awal Tabu Search .................................................................... 33
Tabel 4.12 Susunan Tabulist ................................................................................. 33
Tabel 4.13 Hasil Neighbourhood .......................................................................... 35
Tabel 4.14 Hasil Tabulist Baru ............................................................................. 35
Tabel 4.15 Hasil Akhir Gabungan Algoritma Genetika dengan Tabu Search ...... 36
Tabel 4.16 Perbandingan Nilai Fitness di Prodi S1 Informatika. ......................... 38
Tabel 4.17 Hasil Perbandingan Waktu Eksekusi dalam (s) di Jurusan S1
Informatika ............................................................................................................ 39
Tabel 4.18 Hasil Perbandingan Nilai Fitness di Prodi D3 Teknik Informatika. ... 40
Tabel 4.19 Hasil Perbandingan Waktu Eksekusi dalam (s) di Prodi D3 Teknik
Informatika ............................................................................................................ 41
Tabel 4.20 Hasil Perbandingan Nilai Fitness di Prodi S1 Hukum ........................ 41
Tabel 4.21 Perbandingan Waktu Eksekusi dalam (s) di Prodi S1 Hukum............ 42
Tabel 4.22 Perbandingan Fitness dan Waktu di Prodi S1 Informatika, D3
Informatika, S1 Hukum ......................................................................................... 43
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DAFTAR GAMBAR
Gambar 2.1 General Pseudo-code Algoritma Genetika .......................................... 8
Gambar 2.2 Pseudo-code Initial Population using Population Reduction Method10
Gambar 2.3 Pseudo-code Evaluate Fitness ........................................................... 11
Gambar 2.4 Pseudo Code Tournament Selection ................................................ 12
Gambar 2.5 Pseudo-code Uniform Crossover ..................................................... 13
Gambar 2.6 Pseudo-code VDM ............................................................................ 14
Gambar 2.7 Pseudo-code Tabu Search ................................................................. 15
Gambar 2.8 General Pseudo-code of Hybrid Genetic Algorithms. ...................... 16
Gambar 3.1 Diagram Metodologi Penelitian. ....................................................... 20
Gambar 3.2 Pseudo-code Modified Algoritma Genetika ...................................... 24
Gambar 3.3 Pseudo-code Gabungan Algoritma Genetika dengan Tabu Search .. 24
Gambar 4.1 Hasil Bilangan Random Offerspring ................................................. 31
Gambar 4.2 Proses Memasukkan Gen dari Parent 1 ke Offerspring .................... 31
Gambar 4.3 Proses Memasukkan Gen dari Parent 2 ke Offerspring .................... 31
Gambar 4.4 Proses � .......................................................................................... 34
Gambar 4.5 Hasil acak Makul Penawaran Pertama .............................................. 34
Gambar 4.6 Hasil acak Makul Penawaran Kedua ................................................. 34
Gambar 4.7 Hasil tukar ruang dan waktu � ....................................................... 34
Gambar 4.8 Grafik perbandingan fitness setiap generasi di Algoritma Genetika,
Gabungan Algoritma Genetika dengan Tabu Search, dan Algoritma Simple additive
weighting ............................................................................................................... 37
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DAFTAR LAMPIRAN
Lampiran A ........................................................................................................... 48
Lampiran B............................................................................................................ 58
Lampiran C............................................................................................................ 64
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PENGGABUNGAN ALGORITMA GENETIKA DENGAN TABU SEARCH
UNTUK PENGEMBANGAN METODE PENJADWALAN MATA KULIAH
DI UNIVERSITAS SEBELAS MARET SURAKARTA
SKRIPSI
Diajukan untuk Memenuhi Salah Satu Syarat Mencapai Gelar Strata Satu
Jurusan Informatika
Disusun oleh :
Ahmad Miftah Fajrin
NIM. M0510003
JURUSAN INFORMATIKA
FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM
UNIVERSITAS SEBELAS MARET
SURAKARTA
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MOTTO
Lakukanlah kebaikan tanpa mengharapkan kembali
(Penulis)
Without action, you aren’t going anywhere.
(Mahatma Gandhi)
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PERSEMBAHAN
Karya ini Penulis persembahkan kepada:
“Ayah, Ibu, Adik, dan seluruh keluarga besar.”
“Teman-teman Informatika 2010 khususnya Ashar, Aji, Yusuf, Hedik, Cerren,
Adit, Taufik, Viko, April, Lydia, Dian”
commit to user
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KATA PENGANTAR
Segala puji dan syukur penulis ucapkan kepada Allah SWT, yang hanya karena
rahmat dan karunia-Nya, penulis dapat menyelesaikan Tugas Akhir dengan judul
“Penggabungan Algoritma Genetika dengan Tabu Search untuk Pengembangan
Metode Penjadwalan Mata Kuliah di Universitas Sebelas Maret Surakarta”. Penulis
menyadari akan keterbatasan yang dimiliki. Begitu banyak bantuan dan bimbingan
yang diberikan dalam penyusunan Tugas Akhir ini. Oleh karena itu, penulis
mengucapkan terima kasih kepada :
1.
Bapak Drs. Bambang Harjito, M.App.Sc., Ph.D. selaku Ketua Jurusan
Informatika FMIPA UNS sekaligus sebagai anggota penguji yang telah
memberikan masukan, kritik dan saran yang membangun,
2.
Bapak Antonius Bima Murti Wijaya S.T.,M.T. Selaku Dosen Pembimbing I
yang telah memberikan pengarahan selama proses penyusunan Tugas Akhir
ini,
3.
Bapak Abdul Aziz, S.Kom, M.Cs. selaku Dosen Pembimbing II yang telah
memberikan masukan, kritik dan saran yang membangun,
4.
Bapak Drs. YS. Palgunadi, M.Sc. sebagai anggota penguji yang telah
memberikan masukan, kritik dan saran yang membangun
5.
Ayah, ibu, dan adik-adikku yang senantiasa memberikan dukungan dan
motivasi,
6.
Teman-teman yang senantiasa selalu berbagi pengetahuan, pengalaman, dan
memberikan dukungan dan motivasi.
Semoga Tugas Akhir ini bermanfaat bagi semua pihak yang berkepentingan.
Surakarta, Januari 2015
Penulis
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THE MERGER OF GENETIC ALGORITHM WITH TABU SEARCH
ALGORITHM FOR DEVELOPMENT OF COURSE TIMETABLING METHOD
AT SEBELAS MARET UNIVERSITY
Ahmad Miftah Fajrin
Jurusan Informatika. Fakultas Matematika dan Ilmu Pengetahuan Alam.
Universitas Sebelas Maret
ABSTRACT
University course timetabling is often to be critical concern for most
education institution. Sebelas Maret University (UNS) has developed a
computerized sxystem for a timetabling problem using simple additive weighting
algorithm. However, there are some study programs do not satisfied of the result
because there are many soft constraint are violated.
The purpose of this research is to develop the course timetabling algorithm
by combining the genetic algorithm and tabu search algorithm. Genetic Algorithm
has the power of a good exploitation to produce a visible solution meanwhile tabu
search can increase the power of searching in genetic algorithm and reduce the
possibility of trapped in local optimum
The combining of genetic algorithm with tabu search can decrease the
fitness by 47% for the smallest dataset and 21.3% for the biggest dataset compared
by simple additive weighting algorithm. The merge of genetic algorithm with tabu
search method can be used for the development of course timetabling method at
UNS.
Kata Kunci — Development of Course Timetabling Method, Genetic Algorithm,
Tabu search.
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PENGGABUNGAN ALGORITMA GENETIKA DENGAN TABU SEARCH
UNTUK PENGEMBANGAN METODE PENJADWALAN MATA KULIAH DI
UNIVERSITAS SEBELAS MARET SURAKARTA
Ahmad Miftah Fajrin
Jurusan Informatika. Fakultas Matematika dan Ilmu Pengetahuan Alam.
Universitas Sebelas Maret
ABSTRAK
Penjadwalan mata kuliah sering menjadi bahan perhatian penting bagi setiap
organisasi atau institusi khususnya bagian pendidikan di universitas. Universitas
Sebelas Maret (UNS) telah membangun sistem komputerisasi untuk menyelesaikan
masalah penjadwalan mata kuliah menggunakan algoritma simpe additive
weighting. Akan tetapi, dibeberapa prodi masih ada yang kurang puas karena
banyak soft constraint yang dilanggar.
Tujuan dari penelitian ini adalah mengembangkan algoritma penjadwalan
mata kuliah dengan menggabungkan algoritma genetika dengan tabu search.
Algoritma genetika mempunyai kekuatan exploitation atau pencarian yang baik
sehingga menghasilkan solusi yang layak dan algoritma tabu search akan
meningkatkan kekuatan pencarian di algoritma genetika sehingga dapat
mengurangi kemungkinan terjebak di local optimum.
Penggabungan algoritma genetika dengan tabu search dapat menurunkan
nilai fitness sebesar 47% untuk dataset yang jumlahnya paling kecil dan 21.3%
untuk dataset yang jumlahnya paling besar dibandingkan dengan algoritma simple
additive weighting. Secara umum metode penggabungan algoritma genetika dengan
tabu search dapat digunakan untuk pengembangan metode penjadwalan mata
kuliah di UNS.
Kata Kunci — Pengembangan Metode Penjadwalan Mata Kuliah, Algoritma
genetika, Tabu search.
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DAFTAR ISI
HALAMAN JUDUL................................................................................................ i
HALAMAN PERSETUJUAN ............................... Error! Bookmark not defined.
HALAMAN PENGESAHAN ................................ Error! Bookmark not defined.
MOTTO ................................................................................................................. iv
PERSEMBAHAN ................................................................................................... v
KATA PENGANTAR ........................................................................................... vi
ABSTRACT .......................................................................................................... vii
ABSTRAK ........................................................................................................... viii
DAFTAR ISI .......................................................................................................... ix
DAFTAR TABEL ................................................................................................. xii
DAFTAR GAMBAR ........................................................................................... xiii
DAFTAR LAMPIRAN ........................................................................................ xiv
BAB I PENDAHULUAN ...................................................................................... 1
1.1.
Latar Belakang ......................................................................................... 1
1.2.
Rumusan Masalah .................................................................................... 3
1.3.
Batasan Masalah ....................................................................................... 3
1.4.
Tujuan Penelitian ...................................................................................... 4
1.5.
Manfaat Penelitian .................................................................................... 4
1.6.
Sistematika Penulisan ............................................................................... 4
BAB II LANDASAN TEORI ................................................................................ 6
2.1.
Landasan Teori ......................................................................................... 6
2.1.1. Penjadwalan Mata Kuliah ..................................................................... 6
2.1.2. Algoritma Simple Additive Weighting .................................................. 6
2.1.3. Algoritma Genetika............................................................................... 7
2.1.3.1. Chromosom Representation .............................................................. 9
2.1.3.2. Initial Population .............................................................................. 9
2.1.3.3. Evaluate Fitness .............................................................................. 11
2.1.3.4. Selection .......................................................................................... 12
2.1.3.5. Crossover ........................................................................................ 13
2.1.3.6. Mutation ..........................................................................................
13
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2.1.3.7. Elitism ............................................................................................. 14
2.1.4. Tabu Search ........................................................................................ 14
2.1.5. Hybrid Algoritma Genetika ................................................................ 16
2.2.
Penelitian Terkait ................................................................................... 16
2.3.
Rangkuman Hasil Penelitian Terdahulu ................................................. 18
BAB III METODOLOGI PENELITIAN ............................................................ 20
3.1.
Penentuan Constraint ............................................................................. 20
3.1.1. Hard Constraint .................................................................................. 20
3.1.2. Soft Constraint .................................................................................... 22
3.2.
Penentuan Nilai Pinalti ........................................................................... 22
3.3
Desain Constraint ................................................................................... 23
3.4
Desain Algoritma Genetika. ................................................................... 23
3.5
Desain Algoritma Genetika dengan Tabu Search. ................................. 24
3.6
Implementasi .......................................................................................... 25
3.7
Pengujian ................................................................................................ 25
3.7.1
Pengujian Constraint ........................................................................... 25
3.7.2
Pengujian Algoritma ........................................................................... 25
BAB IV HASIL DAN PEMBAHASAN ............................................................. 26
4.1.
Tahap Pembahasan ................................................................................. 26
4.1.1. Desain Algoritma Genetika ................................................................ 26
4.1.1.1. Chromomosom Representation ....................................................... 26
4.1.1.2. Initial Population ............................................................................ 26
4.1.1.3. Evaluate Fitness .............................................................................. 29
4.1.1.4. Selection .......................................................................................... 29
4.1.1.5. Crossover ........................................................................................ 31
4.1.1.6. Mutation .......................................................................................... 32
4.1.1.7. Elitsm .............................................................................................. 32
4.1.2. Desain Penggabungan Algoritma Genetika dengan Tabu Search ...... 33
4.2.
Tahap Hasil ............................................................................................. 36
4.2.1. Pengujian Constraint .......................................................................... 36
4.2.2. Visualisasi Algoritma ......................................................................... 37
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4.2.3. Analisis Empiris..................................................................................
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4.2.3.1. Perbandingan Metode...................................................................... 38
4.2.3.2. Analisis Hasil Perbandingan ............................................................ 43
BAB V KESIMPULAN DAN SARAN ............................................................... 44
5.1.
Kesimpulan ............................................................................................. 44
5.2.
Saran ....................................................................................................... 44
BAB VI ................................................................................................................. 45
DAFTAR PUSTAKA ........................................................................................... 45
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DAFTAR TABEL
Tabel 3.1 Hard Constraint ..................................................................................... 21
Tabel 3.2 Soft Constraint ...................................................................................... 22
Tabel 4.1 Mata Kuliah Penawaran S1 Informatika ............................................... 26
Tabel 4.2 Chromosom 1 ........................................................................................ 27
Tabel 4.3 Chromosom 2 ........................................................................................ 27
Tabel 4.4 Chromosom 3 ........................................................................................ 27
Tabel 4.5 Chromosom 4 ........................................................................................ 27
Tabel 4.6 Chromosom hasil seleksi ...................................................................... 30
Tabel 4.7 Susunan Parent 1 ................................................................................... 30
Tabel 4.8 Susunan Parent 2 ................................................................................... 30
Tabel 4.9 Susunan Offerspring ............................................................................. 32
Tabel 4.10 Susunan Offerspring Baru ................................................................... 32
Tabel 4.11 Solusi Awal Tabu Search .................................................................... 33
Tabel 4.12 Susunan Tabulist ................................................................................. 33
Tabel 4.13 Hasil Neighbourhood .......................................................................... 35
Tabel 4.14 Hasil Tabulist Baru ............................................................................. 35
Tabel 4.15 Hasil Akhir Gabungan Algoritma Genetika dengan Tabu Search ...... 36
Tabel 4.16 Perbandingan Nilai Fitness di Prodi S1 Informatika. ......................... 38
Tabel 4.17 Hasil Perbandingan Waktu Eksekusi dalam (s) di Jurusan S1
Informatika ............................................................................................................ 39
Tabel 4.18 Hasil Perbandingan Nilai Fitness di Prodi D3 Teknik Informatika. ... 40
Tabel 4.19 Hasil Perbandingan Waktu Eksekusi dalam (s) di Prodi D3 Teknik
Informatika ............................................................................................................ 41
Tabel 4.20 Hasil Perbandingan Nilai Fitness di Prodi S1 Hukum ........................ 41
Tabel 4.21 Perbandingan Waktu Eksekusi dalam (s) di Prodi S1 Hukum............ 42
Tabel 4.22 Perbandingan Fitness dan Waktu di Prodi S1 Informatika, D3
Informatika, S1 Hukum ......................................................................................... 43
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DAFTAR GAMBAR
Gambar 2.1 General Pseudo-code Algoritma Genetika .......................................... 8
Gambar 2.2 Pseudo-code Initial Population using Population Reduction Method10
Gambar 2.3 Pseudo-code Evaluate Fitness ........................................................... 11
Gambar 2.4 Pseudo Code Tournament Selection ................................................ 12
Gambar 2.5 Pseudo-code Uniform Crossover ..................................................... 13
Gambar 2.6 Pseudo-code VDM ............................................................................ 14
Gambar 2.7 Pseudo-code Tabu Search ................................................................. 15
Gambar 2.8 General Pseudo-code of Hybrid Genetic Algorithms. ...................... 16
Gambar 3.1 Diagram Metodologi Penelitian. ....................................................... 20
Gambar 3.2 Pseudo-code Modified Algoritma Genetika ...................................... 24
Gambar 3.3 Pseudo-code Gabungan Algoritma Genetika dengan Tabu Search .. 24
Gambar 4.1 Hasil Bilangan Random Offerspring ................................................. 31
Gambar 4.2 Proses Memasukkan Gen dari Parent 1 ke Offerspring .................... 31
Gambar 4.3 Proses Memasukkan Gen dari Parent 2 ke Offerspring .................... 31
Gambar 4.4 Proses � .......................................................................................... 34
Gambar 4.5 Hasil acak Makul Penawaran Pertama .............................................. 34
Gambar 4.6 Hasil acak Makul Penawaran Kedua ................................................. 34
Gambar 4.7 Hasil tukar ruang dan waktu � ....................................................... 34
Gambar 4.8 Grafik perbandingan fitness setiap generasi di Algoritma Genetika,
Gabungan Algoritma Genetika dengan Tabu Search, dan Algoritma Simple additive
weighting ............................................................................................................... 37
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DAFTAR LAMPIRAN
Lampiran A ........................................................................................................... 48
Lampiran B............................................................................................................ 58
Lampiran C............................................................................................................ 64
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