bagaimana media sosial bisa membuat bangsa lebih cerdas? media sosial bisa...bagaimana media sosial...
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Bagaimana Media Sosial Bisa
Membuat BangsaLebih Cerdas?
Ismail Fahmi, PhD.
Drone Emprit
Media Kernels Indonesia
Ismail.fahmi@gmail.com
KONFERENSI BIG DATA INDONESIA 2018
Jakarta
13 Mei 2018
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
Armenia Smart Nation
9
Armenia
Innovation
Invention
Intellectual property
Technology
Foreign affairs
Diaspora
Indonesia: Connectivity & ContentNational digital identity, e-payment, sensor platforms, urban mobility, open data: long way
11
Sumber: Mastel, 2015
Behavior:
Twitter First
15
Quick and immediate source of information
Further information and analysis
Proposed Model:
Social media and Big data
19
Government
Big Data Analytics
Signal
Response,
engagement
Policy & PR
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
Da
ta In
ge
st
Managem
ent &
Queue
Realtim
eJob
Pro
cessin
g
Google Custom
Search
Database Framework
Schedule
d J
ob
Pro
cessin
g
Map Reduce
Sentim
ent
Analy
sis
Oth
er
Pro
cessin
gs
Da
ta &
Work
flow
Managem
ent
Access
Vis
ualiz
atio
n
Other sources
Analytics UI
21
Social Media
Sea
rch +
JS
ON
Detik (ID)
Reuters (EN)
Etc..RS
S +
HT
ML
Gatra (ID)
Bloomberg (EN)
Etc..
HT
ML
Kaskus
Detik Forum
Etc..
HT
ML
Online News
Forums
Twitter StreamJS
ON
Kompas
TE
XT
Warta Ekonomi
Etc..
PU
SH
JS
ONSubscriber
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
ScreensSmart phones,
tabletsDesktops
Client(s)
Converter
System Architecture
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
Sentiment Analysis Methods
39http://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.
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
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