Bagaimana Media Sosial Bisa Membuat Bangsa Lebih Cerdas?

1. Bagaimana Media Sosial Bisa
Membuat Bangsa Lebih
Cerdas?
Ismail Fahmi, PhD.
Drone Emprit
Media Kernels Indonesia
Ismail.fahmi@gmail.com
KONFERENSI BIG DATA INDONESIA 2018
Jakarta
13 Mei 2018

2. 2
1992 – 1997 S1, Teknik Elektro, ITB
2003 – 2004 S2, Information Science, Universitas Groningen, Belanda
2004 – 2009 S3, Information Science, Universitas Groningen, Belanda
2000 – 2003 Inisiator IndonesiaDLN (Digital Library Network pertama di Indonesia)
Mengembangkan Ganesha Digital Library (GDL)
Mendirikan Knowledge Management Research Group (KMRG) ITB
Membangun Digital Library ITB
2009 – Sekarang Engineer di Weborama, Perusahaan berbasis big data (Paris/Amsterdam)
2014 – Sekarang Founder PT. Media Kernels Indonesia, a Drone Emprit Company
2015 – Sekarang Konsultan Perpustakaan Nasional, Inisiator Indonesia OneSearch
2017 – Sekarang Dosen Tetap Magister Teknik Informatika Universitas Islam Indonesia
Ismail Fahmi, PhD.
Ismail.fahmi@gmail.com

3. 3
Terinspirasi dari buku ini

4. 4
1960-an
Gagal Panen di India dan Rusia
Mengejutkan Dunia

5. 5
LANDSAT, Descartes Labs, dan Prediksi
Pengolahan Data Satelit

6. 6
USDA (ratusan surveyor) v.s.
Descartes Labs (20 data scientist)

7. 7
SmartUsing Big Data
No more large-scale surprise crop failures

8. 8
Smart
Nation?

9. Armenia Smart Nation
9
Armenia
Innovation
Invention
Intellectual property
Technology
Foreign affairs
Diaspora

10. Singapore Smart Nation
10
Through the adoption
of digital and smart
technologies

11. Indonesia: Connectivity & Content
National digital identity, e-payment, sensor platforms, urban mobility, open data: long way
11
Sumber:
Mastel, 2015

12. 12

13. 13

14. The Idea:
Social Media as a “Modern Town Hall”
14
Balai Kota DKI

15. Behavior:
Twitter First
15
Quick and immediate source
of information
Further information
and analysis

16. The Problem:
Echo Chambers, Hoax, Hate Speech, Noises
16
SNA ‘Jokowi’
Haters Lovers

17. Current Situation:
Vicious cycle of noises
17
Government
Noise
Public
Noise
Buzzers Buzzers

18. Proposed Solution:
Increasing S/N Ratio
18

19. Proposed Model:
Social media and Big data
19
Government
Big Data Analytics
Signal
Response,
engagement
Policy & PR

20. Media Kernels Big Data Architecture
20
News Crawler
Twitter Crawler
Twitter Streaming
FB Page Crawler
Data Pipeline
Data
SOLR Indexer 1 SOLR Indexer 2 SOLR Indexer 3 SOLR Indexer 4
Hadoop Framework
Physical Hardware
Insight
DataIngest
Management&Queue
RealtimeJob
Processing
Google Custom
Search
Database Framework
ScheduledJob
Processing
Map Reduce
Sentiment
Analysis
Other
Processings
Data&Workflow
Management
Access
Visualization
Other sources
Analytics UI

21. 21
Social Media
Twitter
Facebook
Search+JSON
Detik (ID)
Reuters (EN)
Etc..
RSS+HTML
Gatra (ID)
Bloomberg (EN)
Etc..
HTML
Kaskus
Detik Forum
Etc..
HTML
Online News
Forums
Twitter StreamJSON
Kompas
TEXT
Warta Ekonomi
Etc..
Print
PUSHJSON
Subscriber
Projects
Storage
Search + Account
Crawler
RSS + HTML
Crawler
HTML Crawler
HTML Crawler
SOLR Nodes
Shard 1
SOLR Nodes
Shard N
Index Servers
Redis Queue
Cache Manager
Mentions
Storage
Keywords +
Accounts Filters
deletes
Sentiment
Analysis
Sentiment
Models
Backtrack
Filters
Sentiment
Analysis
Analyses
Control Room
Screens
Smart phones,
tablets
Desktops
Client(s)
Converter
System Architecture
Instagram

22. Media Kernels Features
confidential
22
Trends
DASHBOARD
Comparison
Topic Map
NEWS PORTAL
Latest News
Media
ANALYTICS
News Sites
Page Ranks
Sentiment Analysis
PF-Chart
Engagement
Exposure
Retweets
TOPICS
Replies
Most Shared URLs
Most Shared Videos
Topic Map
Word Cloud
Impact
INFLUENCERS
Engagement
Reach
Most Engaged
Followers
Influencer Network
SNA
Topic Network
PR-Values
Reach
Hashtags Posts
Bubble Map
Twitter User Map
DEMOGRAPHY
User Locations
Edit Sentiments
MENTIONS
Training & Learning
Backtracking
Compare SNA
COMPARE
Compare Projects
Popularity vs
Favorability
Background Jobs
Upload Report
REPORTING
Download Report
User Management
ADMIN
Project Management
Client Management
Source Management
Label and Training
OPINION ANALYSIS
Opinion Chart
Insight Explorer

23. Why Twitter & Online News?
Cross-platform Resonance
23

24. Opinion Analysis
Twitter: Industry, foreign workers, lands
24

25. Opinion Analysis
Online News: Industry, foreign workers, education
25

26. Social Network Analysis
Asian Games (Case Study)
26

27. Asian Games Hashtags
27

28. Twitter
Sebaran percakapan ‘Asian Games’
28

29. Online News
Sebaran berita ‘Teroris’
29

30. Online News
Co-occurrence: person and organization
30

31. Online News
SPO Triples Tree Map
31

32. Topic Mapping
Online News Agenda Settings
32

33. Sentiment Analysis
33

34. Sentiment Analysis
Machine Learning or Dictionary Based
34

35. Sentiment Analysis
35
Positif
Negatif
Netral
?
MENTIONS

36. Sentiment Analysis
36
Positif
?
MENTIONS
Untuk Setya Novanto

37. Sentiment Analysis
37
Negatif?
MENTIONS
Untuk KPK

38. Sentiment Analysis
38
Netral
?
MENTIONS
Untuk Hakim Cepi Iskandar

39. Sentiment Analysis Methods
39
http://www.sciencedirect.com/science/article/pii/S2090447914000550
”one model for all” tidak bisa
memberi label yang tepat untuk
setiap subyek.
Lexicon base tergantung dari
keberadaan kata dalam kamus sentimen,
tidak bisa memberi label yang tepat
untuk subyek yang berbeda.

40. Kesimpulan
40
“Bangsa Indonesia terjebak dalam ‘noise’
yang mempolarisasi mereka. Analisis big
data terhadap percakapan dan berita, harus
mampu menangkap ‘signal’ yang dapat
digunakan untuk membangun bangsa yang
cerdas.”
Drone Emprit

41. Terimakasih
41
Ismail Fahmi, PhD
Drone Emprit
PT Media Kernels Indonesia
Email: ismail.fahmi@gmail.com
Hp: 0812 8908 3894