Big data dan data science - kuliah umum IBS
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Big data dan data science - kuliah umum IBS
- 1. YUDY YUNARDY SE, MM D A N D A T A S C I E N C E
- 2. DEFINISI B i g D a t a a d a l a h t e r m i n o l o g i u n t u k k u m p u l a n d a t a y a n g s a n g a t b e s a r d a n k o m p l e k s s e h i n g g a c a r a p e m r o s e s a n s e c a r a t r a d i s i o n a l t i d a k d a p a t d i l a k u k a n 2
- 4. 4 BIG DATA
- 6. 6
- 7. ARSITEKTUR • DBC1012 • HPCC • Hadoop (based on MapReduce by Google) • Mike 2.0 • Multiple layer/distributed parallel architecture 7
- 8. TEKNOLOGI • Machine learning • Natural Language processing • Artificial intelligence 8
- 9. APLIKASI
- 10. PEMERINTAH •Sensus penduduk •Algoritma lampu lalu lintas •Digital forensic •Monitor financial market activity •Pariwisata •Pencegahan bencana
- 11. KESEHATAN •Penemuan vaksin •Genom sequence processing •Contact tracing Covid-19
- 12. PENDIDIKAN • Penentuan kurikulum sesuai kemampuan individu • Penilaian efektivitas pengajaran, sehingga tercipta suasana belajar mengajar yg menyenangkan buat guru dan murid
- 13. MEDIA • Konten yg berbeda untuk pemirsa yg berbeda • Rekomendasi konten sesuai karakteristik individu • Mengetahui kinerja konten
- 14. BISNIS • Penentuan harga yg lebih akurat per individu • User experience yg unik untuk tiap individu • Mengetahui produk apa yg harus dijual, kapan dan dimana
- 15. STUDI KASUS B a ga i m a n a B i g D a t a d i g u n a ka n d a l a m b i s n i s 15
- 16. 16
- 17. • R e t a i l t e r b e s a r d i d u n i a , 2 j u t a s t a f d a n 2 0 . 0 0 0 t o k o d i 2 8 N e g a r a • T h 2 0 0 4 k e t i k a h u r r i c a n e S a n d y t e r j a d i l o n j a k a n p e r m i n t a a n t h d l a m p u s e n t e r d a n s t r a w b e r r y p o p t a r t • T h 2 0 1 5 m e m b u a t p r i v a t e d a t a c l o u d y g d p t m e m p r o s e s 2 . 5 P B i n f o r m a s i p e r j a m 17 LATAR BELAKANG
- 18. • M e m b e n t u k D a t a C a f é y g m e n g a n a l i s a 2 0 0 a l i r a n d a t a i n t e r n a l d a n e x t e r n a l s e c a r a r e a l t i m e t e r m a s u k 4 0 P B d a t a t r a n s a k s i m i n g g u s e b e l u m n y a • S o c i a l g e n o m e p r o j e c t y g m e m o n i t o r p e r c a k a p a n d i s o s m e d u n t u k m e m p r e d i k s i b r g a p a y g a k a n d i b e l i b e r d a s a r k a n p e r c a k a p a n m e r e k a 18 BIG DATA DLM PRAKTEK
- 19. • M e m p e r s i n g k a t w a k t u m e n e m u k a n m a s a l a h s a m p a i m e r u m u s k a n s o l u s i n y a , d a r i 2 - 3 m i n g g u m e n j a d i 2 0 m e n i t 19 HASILNYA?
- 20. • 2 0 0 m i l y a r b a r i s d a t a t r a n s a k s i b e b e r a p a m i n g g u t e r a k h i r • 2 0 0 s u m b e r d a t a l a i n , m e t e o r o l o g i , e k o n o m i , t e l e k o m , s o c i a l m e d i a , h a r g a B B M d a n d a t a b a s e k e j a d i a n t e r k i n i d i s e k i t a r l o k a s i t o k o 20 DATA YG DIGUNAKAN
- 21. 21
- 22. 22 • T i d a k a d a y g d a p a t m e m p r e d i k s i f i l m a p a y g a k a n m e n j a d i b o x o f f i c e ( W i l l i a m G o l d m a n ) • N e t f l i x m e m b u k t i k a n s e b a l i k n y a , d e n g a n m e m b a n g u n b i s n i s y g m e m p r e d i k s i s e c a r a a k u r a t a p a s a j a y g a k a n m e n j a d i h i t / e n a k d i t o n t o n LATAR BELAKANG
- 23. 23 • M e m i l i k i a n a l i t i k u n t u k b e r b a g a i b i d a n g , p e r s o n a l i z a t i o n , m e s s a g i n g , c o n t e n t d e l i v e r y , d e v i c e d l l • N e t f l i x p r i z e • S e l a i n a l g o r i t m a , N e t f l i x j u g a m e n g g u n a k a n t a g g i n g BIG DATA DLM PRAKTEK
- 24. 24 • M e n a m b a h 4 . 9 j u t a p e l a n g g a n d i Q 1 2 0 1 5 ( d i Q 1 2 0 1 4 m e m i l i k i 4 j u t a p e l a n g g a n ) • P e l a n g g a n N e t f l i x m e n g h a b i s k a n 1 0 m i l y a r j a m d i Q 1 2 0 1 5 HASILNYA?
- 25. 25 • J u d u l k o n t e n , j a m b e r a p a k o n t e n d i t o n t o n , b e r a p a w a k t u y g d i h a b i s k a n , s e b e r a p a s e r i n g k o n t e n d i s t o p / p a u s e • N e t f l i x j u g a m e n g u m p u l k a n d a t a t e n t a n g d e l a y y g d i s e b a b k a n o l e h b u f f e r i n g d a n b i t r a t e ( y g m e m p e n g a r u h i k u a l i t a s g a m b a r ) , d a n j u g a l o k a s i p e l a n g g a n DATA YG DIGUNAKAN
- 26. 26
- 27. 27 • S o s i a l m e d i a t e r b e s a r d i d u n i a , l e b i h d a r i 1 . 5 m i l y a r p e n g g u n a a k t i f • 1 0 . 0 0 0 s t a f d i 3 5 n e g a r a • 2 j u t a p e n g i k l a n a k t i f LATAR BELAKANG
- 28. 28 • B e r i k l a n m e n j a d i s a n g a t a k u r a t d a n e f e k t i f d i f a c e b o o k • S e l a i n p l a t f o r m u n t u k b e r b a g i g a m b a r , s u a r a d a n v i d e o j u g a u n t u k s o f t w a r e • D a t a p e n g g u n a y g s u d a h b e s a r d i t a m b a h d g i n s t a g r a m d a n w h a t s a p p • M e m p e r s i a p k a n V R s e b a g a i p l a t f o r m m a s a d e p a n BIG DATA DLM PRAKTEK
- 29. 29 • M e m p e r o l e h p a n g s a p a s a r 2 4 % d a r i o n l i n e d i s p l a y a d s d i A m e r i k a ( 2 0 1 4 ) • M e m p e r o l e h p e m a s u k a n 5 . 3 m i l y a r d o l a r d a r i i k l a n ( 2 0 1 4 ) • T h n 2 0 1 7 p a n g s a p a s a r m e n j a d i 2 7 % d e n g a n p e m a s u k a n 1 0 m i l y a r d o l a r HASILNYA?
- 30. 30 • D a t a a k t i f i t a s p e n g g u n a y g d i h a s i l k a n s e c a r a o t o m a t i s ( 2 . 5 j u t a k o n t e n t i a p m e n i t ) • M e n g o l a h d a t a i n t e r a k s i p e n g g u n a t i d a k h a n y a d i d a l a m f a c e b o o k n a m u n j u g a s i t u s l a i n ( t e r u t a m a u n t u k k o n t e n f i l m , m u s i k , b u k u d a n s e r i a l T V ) DATA YG DIGUNAKAN
- 31. 31
- 32. 32 • S a l a h s a t u s i t u s s o s m e d t e r t u a d i d u n i a ( d i l u n c u r k a n 2 0 0 3 , f a c e b o o k t h n 2 0 0 4 ) • 4 1 0 j u t a p e n g g u n a d i 2 0 0 n e g a r a • S e l a l u b e r u s a h a t e t a p r e l e v a n u n t u k p e n g g u n a p r o f e s i o n a l y g s i b u k LATAR BELAKANG
- 33. 33 • M e r e k a m d a n m e l a c a k s e t i a p k e g i a t a n p e n g g u n a p d s i t u s L i n k e d I n • P e m b e r i a n s a r a n “ p e o p l e y o u m a y k n o w ” y g a k u r a t b e r k a t a l g o r i t m a • S a r a n t s b j u g a m e n c a k u p t e m a n d a r i t e m a n k i t a • S a r a n t s b j u g a m e n c a k u p p e n g g u n a s i t u s l a i n • M e n g g u n a k a n m a c h i n e l e a r n i n g a l g o r i t h m BIG DATA DLM PRAKTEK
- 34. 34 • 4 0 j u t a p e n g g u n a b a r u d i p a r u h p e r t a m a 2 0 1 5 • P e m a s u k a n 7 0 0 j u t a d o l a r ( m e n i n g k a t d a r i 6 4 0 j u t a d o l a r d i k u a r t a l s e b e l u m n y a ) HASILNYA?
- 35. 35 • T e k n o l o g i s t r e a m - p r o c e s s i n g , d a t a a k t i f i t a s p e n g g u n a d i o l a h s e c a r a r e a l t i m e • K o n t e n y g d i l i k e , s h a r e , j e n i s p e k e r j a a n y g d i k l i k , p e s a n y g d i k i r i m • P u l u h a n r i b u s i t u s w e b s e t i a p d e t i k d i t a m p i l k a n d l m s i t u s L i n k e d I n j u g a d i p r o s e s DATA YG DIGUNAKAN
- 36. 36 +62
- 37. T e l k o m s e l m e r u p a k a n s a l a h s a t u m o b i l e o p e r a t o r y g m e m i l i k i k e s e r i u s a n t e r h a d a p B i g D a t a . M e r e k a m e l u n c u r k a n l a y a n a n B i g D a t a b e r n a m a M - S i g h t d i t a h u n 2 0 1 4 . L a y a n a n i n i m e r u p a k a n b a g i a n d a r i l a y a n a n i k l a n m o b i l e y g t e r u s a k a n g e n c a r d i t e n g a h t r e n p e r k e m b a n g a n b i s n i s d i g i t a l i n i . 37 BAGAIMANA DI INDONESIA?
- 38. TERIMA KASIH Y U D Y Y U N A R D Y + 6 2 8 1 1 9 7 7 5 9 5 y u d y.y u n a r d y @ g m a i l . c o m