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

3

Terinspirasi dari buku ini

4

1960-an

Gagal Panen di India dan

Rusia Mengejutkan Dunia

5

LANDSAT, Descartes Labs, dan Prediksi

Pengolahan Data Satelit

6

USDA (ratusan surveyor) v.s.

Descartes Labs (20 data scientist)

7

SmartUsing Big DataNo more large-scale surprise crop failures

8

Smart

Nation?

Armenia Smart Nation

9

Armenia

Innovation

Invention

Intellectual property

Technology

Foreign affairs

Diaspora

Singapore Smart Nation

10

Through the adoption

of digital and smart

technologies

Indonesia: Connectivity & ContentNational digital identity, e-payment, sensor platforms, urban mobility, open data: long way

11

Sumber: Mastel, 2015

12

13

The Idea:

Social Media as a “Modern Town Hall”

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Balai Kota DKI

Behavior:

Twitter First

15

Quick and immediate source of information

Further information and analysis

The Problem:

Echo Chambers, Hoax, Hate Speech, Noises

16

SNA ‘Jokowi’

Haters Lover

s

Current Situation:

Vicious cycle of noises

17

Government

Noise

Public

Noise

Buzzers Buzzers

Proposed Solution:

Increasing S/N Ratio

18

Proposed Model:

Social media and Big data

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Government

Big Data Analytics

Signal

Response,

engagement

Policy & PR

Media Kernels Big Data Architecture

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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

Twitter

Facebook

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..

Print

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

Instagram

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

Why Twitter & Online News?

Cross-platform Resonance

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Opinion Analysis

Twitter: Industry, foreign workers, lands

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Opinion Analysis

Online News: Industry, foreign workers, education

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Social Network Analysis

Asian Games (Case Study)

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Asian Games Hashtags

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Twitter

Sebaran percakapan ‘Asian Games’

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Online News

Sebaran berita ‘Teroris’

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Online News

Co-occurrence: person and organization

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Online News

SPO Triples Tree Map

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Topic Mapping

Online News Agenda Settings

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Sentiment Analysis

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Sentiment AnalysisMachine Learning or Dictionary Based

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Sentiment Analysis

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Positif

Negatif

Netral

?

MENTIONS

Sentiment Analysis

36

Positif

?

MENTIONS

Untuk Setya Novanto

Sentiment Analysis

37

Negatif?

MENTIONS

Untuk KPK

Sentiment Analysis

38

Netral

?

MENTIONS

Untuk Hakim Cepi Iskandar

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

Terimakasih

41

Ismail Fahmi, PhDDrone Emprit

PT Media Kernels Indonesia

Email: ismail.fahmi@gmail.com

Hp: 0812 8908 3894