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Studi Kasus :
Pariwisata Kota Cirebon
Implementasi
Metode TSP pada
Rute Pariwisata
Menggunakan
Algoritma Ant
Colony Optimization
Frida Putriassa
NATURE-INSPIRED
OPTIMIZATION
Content
Berikut merupakan
beberapa bahasan terkait
topik yang diambil.
PENDAHULUAN
Berisi latar belakang dan penjelasan
mengenai pariwisata dan destinasi
wisata yang ada di Kota Cirebon.
ALGORITMA ACO
Terdiri dari penentuan parameter dan
matriks, implementasi menggunakan
python dan representasi graph.
KESIMPULAN
Berisi kesimpulan dan saran
Pendahuluan
Pariwisata menjadi sektor yang semakin penting dalam
pengembangan suatu daerah, tidak hanya dari segi ekonomi, tetapi
juga dari aspek budaya dan sosial. Efisiensi dalam menentukan rute
pariwisata menjadi krusial untuk memberi pengalaman terbaik bagi
wisatawan. Salah satu tantangan dalam hal ini adalah Travelling
Salesman Problem (TSP), yang mengharuskan penentuan rute
terpendek untuk mengunjungi sejumlah titik tertentu.
Potensi Pariwisata
Kota Cirebon
Kota Cirebon kaya akan warisan
budaya dan sejarah, menawarkan
sejumlah destinasi pariwisata yang
menarik. Dengan adanya Nasi
Jamblang dan Empal Gentong Ibu Nur,
Alun-alun Kota Cirebon, Keraton
Kasepuhan, Taman Sari Goa Sunyaragi,
Gedung BAT, dan Kawasan Batik
Trusmi, potensi wisata Kota Cirebon
perlu dioptimalkan. Penentuan rute
yang efisien dapat meningkatkan daya
tarik dan kenyamanan wisatawan.
Wisata Kota Cirebon
NASI JAMBLANG DAN
EMPAL GENTONG IBU NUR
ALUN-ALUN KOTA
CIREBON
KERATON KASEPUHAN
Wisata Kota Cirebon
TAMAN SARI GOA
SUNYARAGI
GEDUNG BRITISH
AMERICAN TOBACCO
KERKAWASAN BATIK
TRUSMIATON KASEPUHAN
Apa itu Algoritma ACO?
Algoritma ACO, terinspirasi dari perilaku semut dalam
mencari rute terpendek terbukti efektif dalam
menyelesaikan TSP. Keunggulannya adalah
menemukan solusi yang mendekati optimal dengan
memanfaatkan jejak feromon dan kombinasi faktor
probabilitas. Oleh karena itu, ACO menjadi pilihan yang
tepat untuk mengatasi kompleksitas permasalahan
penentuan rute pariwisata, seperti di Kota Cirebon.
Penentuan
Parameter
num_iterations = 100
num_ants = 10
alpha = 1
beta = 2
evaporation_rate = 0.5
ALPHA DAN BETA
EVAPORATION RATE
Alpha menentukan pengaruh
pheromone pada pemilihan rute.
Beta Mengontrol seberapa besar
pengaruh visibility (kebalikan jarak)
pada pemilihan rute.
Mengontrol laju pengurangan
pheromone pada jalur seiring waktu.
Pengaturan evaporation rate
memastikan pembaruan
berkelanjutan dan menjaga
keseimbangan antara eksplorasi dan
eksploitasi rute terbaik.
MATRIKS JARAK
KETERANGAN
Matriks Jarak
A = Nasi Jamblang dan Empal Gentong Ibu Nur
B = Alun-alun Kota Cirebon
C = Keraton Kasepuhan
D = Taman Sari Goa Sunyaragi
E = Gedung BAT
F = Kawasan Batik Trusmi
[ 0 1 3.4 4.1 2.6 6.2 ]
[ 1 0 3.3 4.7 1.9 6.4 ]
[ 3.4 3.3 0 5.5 1.6 9.7 ]
[ 4.1 4.7 5.5 0 7.5 6.9 ]
[ 2.6 1.9 1.6 7.5 0 8.3 ]
[ 6.2 6.4 9.7 6.9 8.3 0 ]
IMPLEMENTASI ALGORITMA ACO
IMPLEMENTASI ALGORITMA ACO
IMPLEMENTASI ALGORITMA ACO
IMPLEMENTASI ALGORITMA ACO
IMPLEMENTASI ALGORITMA ACO
IMPLEMENTASI ALGORITMA ACO
REPRESENTASI GRAPH
Kesimpulan
Dalam implementasi Metode Traveling Salesman Problem (TSP) pada rute
pariwisata Kota Cirebon menggunakan Algoritma Ant Colony Optimization (ACO),
hasil terbaik yang ditemukan adalah rute [A, B, E, C, F, D] dengan panjang rute 3.33
unit. Algoritma ACO membuktikan kemampuannya dalam mencari rute terpendek
dengan menjelajahi berbagai kemungkinan, memberikan solusi yang efisien untuk
mengeksplorasi keindahan pariwisata Kota Cirebon. Hasil ini menggambarkan
potensi ACO sebagai alat yang efektif untuk mengoptimalkan pengalaman wisata
dengan menentukan rute terbaik secara otomatis.

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TSP Cirebon ACO

  • 1. Studi Kasus : Pariwisata Kota Cirebon Implementasi Metode TSP pada Rute Pariwisata Menggunakan Algoritma Ant Colony Optimization Frida Putriassa NATURE-INSPIRED OPTIMIZATION
  • 2. Content Berikut merupakan beberapa bahasan terkait topik yang diambil. PENDAHULUAN Berisi latar belakang dan penjelasan mengenai pariwisata dan destinasi wisata yang ada di Kota Cirebon. ALGORITMA ACO Terdiri dari penentuan parameter dan matriks, implementasi menggunakan python dan representasi graph. KESIMPULAN Berisi kesimpulan dan saran
  • 3. Pendahuluan Pariwisata menjadi sektor yang semakin penting dalam pengembangan suatu daerah, tidak hanya dari segi ekonomi, tetapi juga dari aspek budaya dan sosial. Efisiensi dalam menentukan rute pariwisata menjadi krusial untuk memberi pengalaman terbaik bagi wisatawan. Salah satu tantangan dalam hal ini adalah Travelling Salesman Problem (TSP), yang mengharuskan penentuan rute terpendek untuk mengunjungi sejumlah titik tertentu.
  • 4. Potensi Pariwisata Kota Cirebon Kota Cirebon kaya akan warisan budaya dan sejarah, menawarkan sejumlah destinasi pariwisata yang menarik. Dengan adanya Nasi Jamblang dan Empal Gentong Ibu Nur, Alun-alun Kota Cirebon, Keraton Kasepuhan, Taman Sari Goa Sunyaragi, Gedung BAT, dan Kawasan Batik Trusmi, potensi wisata Kota Cirebon perlu dioptimalkan. Penentuan rute yang efisien dapat meningkatkan daya tarik dan kenyamanan wisatawan.
  • 5. Wisata Kota Cirebon NASI JAMBLANG DAN EMPAL GENTONG IBU NUR ALUN-ALUN KOTA CIREBON KERATON KASEPUHAN
  • 6. Wisata Kota Cirebon TAMAN SARI GOA SUNYARAGI GEDUNG BRITISH AMERICAN TOBACCO KERKAWASAN BATIK TRUSMIATON KASEPUHAN
  • 7. Apa itu Algoritma ACO? Algoritma ACO, terinspirasi dari perilaku semut dalam mencari rute terpendek terbukti efektif dalam menyelesaikan TSP. Keunggulannya adalah menemukan solusi yang mendekati optimal dengan memanfaatkan jejak feromon dan kombinasi faktor probabilitas. Oleh karena itu, ACO menjadi pilihan yang tepat untuk mengatasi kompleksitas permasalahan penentuan rute pariwisata, seperti di Kota Cirebon.
  • 8. Penentuan Parameter num_iterations = 100 num_ants = 10 alpha = 1 beta = 2 evaporation_rate = 0.5 ALPHA DAN BETA EVAPORATION RATE Alpha menentukan pengaruh pheromone pada pemilihan rute. Beta Mengontrol seberapa besar pengaruh visibility (kebalikan jarak) pada pemilihan rute. Mengontrol laju pengurangan pheromone pada jalur seiring waktu. Pengaturan evaporation rate memastikan pembaruan berkelanjutan dan menjaga keseimbangan antara eksplorasi dan eksploitasi rute terbaik.
  • 9. MATRIKS JARAK KETERANGAN Matriks Jarak A = Nasi Jamblang dan Empal Gentong Ibu Nur B = Alun-alun Kota Cirebon C = Keraton Kasepuhan D = Taman Sari Goa Sunyaragi E = Gedung BAT F = Kawasan Batik Trusmi [ 0 1 3.4 4.1 2.6 6.2 ] [ 1 0 3.3 4.7 1.9 6.4 ] [ 3.4 3.3 0 5.5 1.6 9.7 ] [ 4.1 4.7 5.5 0 7.5 6.9 ] [ 2.6 1.9 1.6 7.5 0 8.3 ] [ 6.2 6.4 9.7 6.9 8.3 0 ]
  • 17. Kesimpulan Dalam implementasi Metode Traveling Salesman Problem (TSP) pada rute pariwisata Kota Cirebon menggunakan Algoritma Ant Colony Optimization (ACO), hasil terbaik yang ditemukan adalah rute [A, B, E, C, F, D] dengan panjang rute 3.33 unit. Algoritma ACO membuktikan kemampuannya dalam mencari rute terpendek dengan menjelajahi berbagai kemungkinan, memberikan solusi yang efisien untuk mengeksplorasi keindahan pariwisata Kota Cirebon. Hasil ini menggambarkan potensi ACO sebagai alat yang efektif untuk mengoptimalkan pengalaman wisata dengan menentukan rute terbaik secara otomatis.