1. Breadth First Search & Depth-First Search
Kecerdasan Buatan
Breadth First Search &
Depth-First Search

2. Konsep yang perlu difahami sebelumnya:
Algoritma Searching
Stack
Queue
Graph
Object Oriented Programming

3. Graph

4. Contoh Implementasi Teori Graph

5. Contoh Implementasi Teori Graph
Facebook's example Graph Search queries, which is to find all of the Sushi restaurants in New York that my friends like.

6. Breadth First Search

7. Breadth First Search
BFS (Breadth First Search) merupakan salah satu algoritma penelusuran struktur graf / pohon
BFS melakukan pencarian secara melebar atau per level pohon. Simpul ditelusuri dari root kemudian menelusuri semua simpul pada setiap level di bawahnya ( misalnya prioritas penelusuran dari kiri ke kanan ), maka penelusuran dilakukan terus dari simpul paling kiri ke simpul anak – anak tetangganya yang selevel.

8. Algoritma Breadth First Search
Masukkan simpul root ke dalam antrian (Queue)
Periksa antrian terdepan apakah memiliki anak simpul
Jika ya, masukan semua anak simpul ke dalam antrian
Hapus antrian terdepan
Jika antrian kosong berhenti, tapi jika tidak kembali ke langkah dua

9. Breadth First Search - A B C D E F G H I
front
FIFO Queue

10. Breadth First Search A - enqueue source node FIFO Queue front A B C DG H I
enqueue source node A
front
FIFO Queue

11. Breadth First Search A - dequeue next vertex FIFO Queue front A B C DG H I
dequeue next vertex A
front
FIFO Queue

12. Breadth First Search - visit neighbors of A FIFO Queue front A B C D E

13. Breadth First Search - visit neighbors of A FIFO Queue front A B C D E

14. Breadth First Search B - A B discovered FIFO Queue front A B C D E F G

15. Breadth First Search B - A visit neighbors of A FIFO Queue front A B C

16. Breadth First Search B I - A A I discovered FIFO Queue front A B C D EG H I A
I discovered
B I
front
FIFO Queue

17. Breadth First Search B I - A A finished with A FIFO Queue front A B CG H I A
finished with A
B I
front
FIFO Queue

18. Breadth First Search B I - A A dequeue next vertex FIFO Queue front AG H I A
dequeue next vertex
B I
front
FIFO Queue

19. Breadth First Search I - A A visit neighbors of B FIFO Queue front A B

20. Breadth First Search I - A A visit neighbors of B FIFO Queue front A B

21. Breadth First Search I F - A B A F discovered FIFO Queue front A B C DG H B I A
F discovered
I F
front
FIFO Queue

22. Breadth First Search I F - A B A visit neighbors of B FIFO Queue front

23. Breadth First Search I F - A B A A already discovered FIFO Queue frontG H B I A
A already discovered
I F
front
FIFO Queue

24. Breadth First Search I F - A B A finished with B FIFO Queue front A BG H B I A
finished with B
I F
front
FIFO Queue

25. Breadth First Search I F - A B A dequeue next vertex FIFO Queue frontG H B I A
dequeue next vertex
I F
front
FIFO Queue

26. Breadth First Search F - A B A visit neighbors of I FIFO Queue front A

27. Breadth First Search F - A B A visit neighbors of I FIFO Queue front A

28. Breadth First Search F - A B A A already discovered FIFO Queue front AG H B I A
A already discovered F
front
FIFO Queue

29. Breadth First Search F - A B A visit neighbors of I FIFO Queue front A

30. Breadth First Search F E - A I B A E discovered FIFO Queue front A B CG H I B I A
E discovered
F E
front
FIFO Queue

31. Breadth First Search F E - A I B A visit neighbors of I FIFO Queue
front
FIFO Queue

32. Breadth First Search F E - A I B A F already discovered FIFO QueueG H I B I A
F already discovered
F E
front
FIFO Queue

33. Breadth First Search F E - A I B A I finished FIFO Queue front A B C DG H I B I A
I finished
F E
front
FIFO Queue

34. Breadth First Search F E - A I B A dequeue next vertex FIFO QueueG H I B I A
dequeue next vertex
F E
front
FIFO Queue

35. Breadth First Search E - A I B A visit neighbors of F FIFO Queue front

36. Breadth First Search E G - A I B F A G discovered FIFO Queue front A B

37. Breadth First Search E G - A I B F A F finished FIFO Queue front A B C

38. Breadth First Search E G - A I B F A dequeue next vertex FIFO Queue
front
FIFO Queue

39. Breadth First Search G - A I B F A visit neighbors of E FIFO Queue
front
FIFO Queue

40. Breadth First Search G - A I B F A E finished FIFO Queue front A B C D

41. Breadth First Search G - A I B F A dequeue next vertex FIFO Queue
front
FIFO Queue

42. Breadth First Search - A I B F A visit neighbors of G FIFO Queue front

43. Breadth First Search C - A G I B F A C discovered FIFO Queue front A B

44. Breadth First Search C - A G I B F A visit neighbors of G FIFO Queue
front
FIFO Queue

45. Breadth First Search C H - A G I B F G A H discovered FIFO Queue front

46. Breadth First Search C H - A G I B F G A G finished FIFO Queue front A

47. Breadth First Search C H - A G I B F G A dequeue next vertex
front
FIFO Queue

48. Breadth First Search H - A G I B F G A visit neighbors of C FIFO Queue
front
FIFO Queue

49. Breadth First Search H D - A G C I B F G A D discovered FIFO Queue
front
FIFO Queue

50. Breadth First Search H D - A G C I B F G A C finished FIFO Queue front

51. Breadth First Search H D - A G C I B F G A get next vertex FIFO Queue
front
FIFO Queue

52. Breadth First Search D - A G C I B F G A visit neighbors of H
front
FIFO Queue

53. Breadth First Search D - A G C I B F G A finished H FIFO Queue front A

54. Breadth First Search D - A G C I B F G A dequeue next vertex
front
FIFO Queue

55. Breadth First Search - A G C I B F G A visit neighbors of D FIFO Queue
front
FIFO Queue

56. Breadth First Search - A G C I B F G A D finished FIFO Queue front A B

57. Breadth First Search - A G C I B F G A dequeue next vertex FIFO Queue
front
FIFO Queue

58. Breadth First Search - A G C I B F G A STOP FIFO Queue front A B C D E

59. Implementasi BFS dengan menggunakan Java

60. Pemanfaatan Algoritma BFS dalam dunia nyata
Shortest Path
Peer to Peer Networks. (ex: BitTorrent, is used to find all neighbor nodes.)
Crawlers in Search Engines
Social Networking Websites
GPS Navigation systems
Broadcasting in Network
Path Finding

61. Depth First Search

62. Penjelasan DFS DFS(G) DFS-VISIT(G, u) for each uV do color[u] gray
color[u] white
[u]  NIL
time  0
if color[u]  white then
DFS-VISIT(G, u)
DFS-VISIT(G, u)
color[u] gray
d[u] time  time 1
for each v Adj[u] do
if color[v]  white then
[v]  u
DFS-VISIT(G, v)
color[u] black
f[u] time  time 1

63. Penjelasan DFS
DFS (Depth-First-Search) adalah salah satu algoritma penelusuran struktur graf / pohon berdasarkan kedalaman. Simpul ditelusuri dari root kemudian ke salah satu simpul anaknya ( misalnya prioritas penelusuran berdasarkan anak pertama [simpul sebelah kiri] ), maka penelusuran dilakukan terus melalui simpul anak pertama dari simpul anak pertama level sebelumnya hingga mencapai level terdalam.
Setelah sampai di level terdalam, penelusuran akan kembali ke 1 level sebelumnya untuk menelusuri simpul anak kedua pada pohon biner [simpul sebelah kanan] lalu kembali ke langkah sebelumnya dengan menelusuri simpul anak pertama lagi sampai level terdalam dan seterusnya.

64. Algoritma DFS Masukkan simpul root ke dalam tumpukan dengan push
Ambil dan simpan isi elemen (berupa simpul pohon) dari tumpukan teratas
Hapus isi stack teratas dengan prosedur pop
Periksa apakah simpul pohon yang disimpan tadi memiliki anak simpul
Jika ya, push semua anak simpul yang dibangkitkan ke dalam stack
Jika tumpukan kosong berhenti, tapi jika tidak kembali ke langkah dua

65. Task: Conduct a depth-first search of the graph starting with node D
Walk-Through
Visited Array F C A B C D E F G H A B D H G E
Task: Conduct a depth-first search of the graph starting with node D

66. Walk-Through F C A B D H G E The order nodes are visited: D Visit D
Visited Array F C A B C D √ E F G H D A B D H G E
The order nodes are visited: D
Visit D

67. Walk-Through F C A B D H G E The order nodes are visited: D
Visited Array F C A B C D √ E F G H D A B D H G E
The order nodes are visited: D
Consider nodes adjacent to D, decide to visit C first (Rule: visit adjacent nodes in alphabetical order)

68. Walk-Through F C A B D H G E The order nodes are visited: D, C Visit C
Visited Array F C A B C √ D E F G H C D A B D H G E
The order nodes are visited:
D, C
Visit C

69. Walk-Through F C A B D H G E The order nodes are visited: D, C
Visited Array F C A B C √ D E F G H C D A B D H G E
The order nodes are visited:
D, C
No nodes adjacent to C; cannot continue  backtrack, i.e., pop stack and restore previous state

70. Back to D – C has been visited, decide to visit E next
Walk-Through
Visited Array F C A B C √ D E F G H D A B D H G E
The order nodes are visited:
D, C
Back to D – C has been visited, decide to visit E next

71. Back to D – C has been visited, decide to visit E next
Walk-Through
Visited Array F C A B C √ D E F G H E D A B D H G E
The order nodes are visited:
D, C, E
Back to D – C has been visited, decide to visit E next

72. Walk-Through F C A B D H G E The order nodes are visited: D, C, E
Visited Array F C A B C √ D E F G H E D A B D H G E
The order nodes are visited:
D, C, E
Only G is adjacent to E

73. Walk-Through F C A B D H G E The order nodes are visited: D, C, E, G
Visited Array F C A B C √ D E F G H G E D A B D H G E
The order nodes are visited:
D, C, E, G
Visit G

74. Walk-Through F C A B D H G E The order nodes are visited: D, C, E, G
Visited Array F C A B C √ D E F G H G E D A B D H G E
The order nodes are visited:
D, C, E, G
Nodes D and H are adjacent to G. D has already been visited. Decide to visit H.

75. Walk-Through F C A B D H G E The order nodes are visited:
Visited Array F C A B C √ D E F G H H G E D A B D H G E
The order nodes are visited:
D, C, E, G, H
Visit H

76. Nodes A and B are adjacent to F. Decide to visit A next.
Walk-Through
Visited Array F C A B C √ D E F G H H G E D A B D H G E
The order nodes are visited:
D, C, E, G, H
Nodes A and B are adjacent to F. Decide to visit A next.

77. Walk-Through F C A B D H G E The order nodes are visited:
Visited Array F C A √ B C D E F G H A H G E D A B D H G E
The order nodes are visited:
D, C, E, G, H, A
Visit A

78. Only Node B is adjacent to A. Decide to visit B next.
Walk-Through
Visited Array F C A √ B C D E F G H A H G E D A B D H G E
The order nodes are visited:
D, C, E, G, H, A
Only Node B is adjacent to A. Decide to visit B next.

79. Walk-Through F C A B D H G E The order nodes are visited:
Visited Array F C A √ B C D E F G H B A H G E D A B D H G E
The order nodes are visited:
D, C, E, G, H, A, B
Visit B

80. No unvisited nodes adjacent to B. Backtrack (pop the stack).
Walk-Through
Visited Array F C A √ B C D E F G H A H G E D A B D H G E
The order nodes are visited:
D, C, E, G, H, A, B
No unvisited nodes adjacent to B. Backtrack (pop the stack).

81. No unvisited nodes adjacent to A. Backtrack (pop the stack).
Walk-Through
Visited Array F C A √ B C D E F G H H G E D A B D H G E
The order nodes are visited:
D, C, E, G, H, A, B
No unvisited nodes adjacent to A. Backtrack (pop the stack).

82. No unvisited nodes adjacent to H. Backtrack (pop the stack).
Walk-Through
Visited Array F C A √ B C D E F G H G E D A B D H G E
The order nodes are visited:
D, C, E, G, H, A, B
No unvisited nodes adjacent to H. Backtrack (pop the stack).

83. No unvisited nodes adjacent to G. Backtrack (pop the stack).
Walk-Through
Visited Array F C A √ B C D E F G H E D A B D H G E
The order nodes are visited:
D, C, E, G, H, A, B
No unvisited nodes adjacent to G. Backtrack (pop the stack).

84. No unvisited nodes adjacent to E. Backtrack (pop the stack).
Walk-Through
Visited Array F C A √ B C D E F G H D A B D H G E
The order nodes are visited:
D, C, E, G, H, A, B
No unvisited nodes adjacent to E. Backtrack (pop the stack).

85. F is unvisited and is adjacent to D. Decide to visit F next.
Walk-Through
Visited Array F C A √ B C D E F G H D A B D H G E
The order nodes are visited:
D, C, E, G, H, A, B
F is unvisited and is adjacent to D. Decide to visit F next.

86. Walk-Through F C A B D H G E The order nodes are visited:
Visited Array F C A √ B C D E F G H F D A B D H G E
The order nodes are visited:
D, C, E, G, H, A, B, F
Visit F

87. No unvisited nodes adjacent to F. Backtrack.
Walk-Through
Visited Array F C A √ B C D E F G H D A B D H G E
The order nodes are visited:
D, C, E, G, H, A, B, F
No unvisited nodes adjacent to F. Backtrack.

88. No unvisited nodes adjacent to D. Backtrack.
Walk-Through
Visited Array F C A √ B C D E F G H A B D H G E
The order nodes are visited:
D, C, E, G, H, A, B, F
No unvisited nodes adjacent to D. Backtrack.

89. Stack is empty. Depth-first traversal is done.
Walk-Through
Visited Array F C A √ B C D E F G H A B D H G E
The order nodes are visited:
D, C, E, G, H, A, B, F
Stack is empty. Depth-first traversal is done.

90. Implementasi DFS dengan menggunakan Java

91. Pemanfaatan DFS pada dunia nyata
Detecting cycle in a graph
Path Finding
Topological Sorting
Finding Strongly Connected Components of a graph
Solving puzzles with only one solution
Game (Chess, Tictactoe, etc)

92. Which Better?
BFS
DFS L M N O P G Q H J I K F E D B C A

93. BFS
DFS
Queue (recursion no help)
Can be used to find shortest paths from the start vertex
Can be used to find short alternating paths for matching
Stack or recursion
Many applications

94. Depth- vs. breadth-first searching
When a breadth-first search succeeds, it finds a minimum-depth (nearest the root) goal node
A separate mechanism is needed to keep track of the path to the goal node
When a depth-first search succeeds, the path to the goal node is on the stack
The found goal node is not necessarily minimum depth
For a large tree, breadth-first search memory requirements may be excessive
For a large tree, a depth-first search may take an excessively long time to find even a very nearby goal node
How can we combine the advantages (and avoid the disadvantages) of these two search techniques?

95. Latihan
Lakukan proses traverse utk struktur graf berikut dengan menggunakan:
BFS
DFS
Dimulai dari vertex 0

96. Kuliah AI
Pekan 1
Pekan 2

97. Akhirnya selesai….. 
Ada pertanyaan?