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MK ID Big Data 2018

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Bagaimana Media Sosial Bisa
Membuat Bangsa Lebih Cerdas?

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MK ID Big Data 2018

  1. 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. 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. 3 Terinspirasi dari buku ini
  4. 4. 4 1960-an Gagal Panen di India dan Rusia Mengejutkan Dunia
  5. 5. 5 LANDSAT, Descartes Labs, dan Prediksi Pengolahan Data Satelit
  6. 6. 6 USDA (ratusan surveyor) v.s. Descartes Labs (20 data scientist)
  7. 7. 7 SmartUsing Big Data No more large-scale surprise crop failures
  8. 8. 8 Smart Nation?
  9. 9. Armenia Smart Nation 9 Armenia Innovation Invention Intellectual property Technology Foreign affairs Diaspora
  10. 10. Singapore Smart Nation 10 Through the adoption of digital and smart technologies
  11. 11. Indonesia: Connectivity & Content National digital identity, e-payment, sensor platforms, urban mobility, open data: long way 11 Sumber: Mastel, 2015
  12. 12. 12
  13. 13. 13
  14. 14. The Idea: Social Media as a “Modern Town Hall” 14 Balai Kota DKI
  15. 15. Behavior: Twitter First 15 Quick and immediate source of information Further information and analysis
  16. 16. The Problem: Echo Chambers, Hoax, Hate Speech, Noises 16 SNA ‘Jokowi’ Haters Lovers
  17. 17. Current Situation: Vicious cycle of noises 17 Government Noise Public Noise Buzzers Buzzers
  18. 18. Proposed Solution: Increasing S/N Ratio 18
  19. 19. Proposed Model: Social media and Big data 19 Government Big Data Analytics Signal Response, engagement Policy & PR
  20. 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. 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. 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. 23. Why Twitter & Online News? Cross-platform Resonance 23
  24. 24. Opinion Analysis Twitter: Industry, foreign workers, lands 24
  25. 25. Opinion Analysis Online News: Industry, foreign workers, education 25
  26. 26. Social Network Analysis Asian Games (Case Study) 26
  27. 27. Asian Games Hashtags 27
  28. 28. Twitter Sebaran percakapan ‘Asian Games’ 28
  29. 29. Online News Sebaran berita ‘Teroris’ 29
  30. 30. Online News Co-occurrence: person and organization 30
  31. 31. Online News SPO Triples Tree Map 31
  32. 32. Topic Mapping Online News Agenda Settings 32
  33. 33. Sentiment Analysis 33
  34. 34. Sentiment Analysis Machine Learning or Dictionary Based 34
  35. 35. Sentiment Analysis 35 Positif Negatif Netral ? MENTIONS
  36. 36. Sentiment Analysis 36 Positif ? MENTIONS Untuk Setya Novanto
  37. 37. Sentiment Analysis 37 Negatif? MENTIONS Untuk KPK
  38. 38. Sentiment Analysis 38 Netral ? MENTIONS Untuk Hakim Cepi Iskandar
  39. 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. 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. 41. Terimakasih 41 Ismail Fahmi, PhD Drone Emprit PT Media Kernels Indonesia Email: ismail.fahmi@gmail.com Hp: 0812 8908 3894

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