Artificial Intelligence
Kelompok Keilmuan E
(Game Tech dan Artificial Intelligence)

Program Studi Teknik Informatika
Univ...
Kelompok Keilmuan E
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Nelly Indriani W
Irfan Maliki
Ednawati Rainarli
Hendri Karisma
Ken Kinanti Purnamasari
In fact, through our entire life we never
stop learning new things. This has been
crucial for our survival, but it also
st...
Apa itu tugas akhir??
Skripsi/Tugas Akhir merupakan capstone
pada pendidikan tinggi (undegraduate),
untuk membangun dan me...
Kurikulum berdasarkan ACM/IEEE
2005
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•
•
•
•

Computer engineering
Computer science
Information systems
Information techn...
Tujuan
• Educational
• Research
Educational
1. develop your critical thinking;
2. enhance your ability to work independently;
3. increase your understandi...
Research
• you will deepen your understanding of the
subject area, and contribute to the common
knowledge and understandin...
Computer Science
“Computer science is no more about
computers than astronomy is about
telescopes.” , Edsger W. Dijkstra
Computer Science Characteristic
•

An empirical discipline, in which each new artefact, e.g. a program, can be
seen as an ...
Ilmu dan Rekayasa Komputer
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•
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•
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Combinatorial
Graph Algorithm
Network
Sorting
Searhing
Learning
Artificial Int...
Artificial Intelligence
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Problem-solving
Knowledge and reasoning
Planning
Uncertain Knowledge (probabilistic
reaso...
AI
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Communicating
perceiving
Acting
what is AI ?
– Acting humanly
– Thinking humanly
– Acting rationally
– Thinkin...
Foundation
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•
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•

Philosophy
Mathematics
Economics
Neuroscience
Psychology
Computer Engineering
Control theory and...
AI
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Intelligent Agents
Searhing strategies
Optimal Decisions/Strategies in Game
Machine Learning
– Information The...
Applications
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Speech Recognition/Natural Language Processing
Digital Signal Processing
Computer Vi...
Learning
• Inductive
– proses belajar berdasarkan informasi yang spesifik
guna mendapatkan pola dalam menyelesaikan
masala...
Machine Learning
• Supervised
• Unsupervised
• Reinforcement Learning
Karakteristik Supervised
• Masalah yang diselesaikan biasanya berbentuk
klasifikasi, dataset yang dimiliki oleh kasus yang...
Karakteristik Unsupervised
• Unsupervised Learning biasanya memiliki kata
kunci clustering atau melakukan pengklusteran
te...
Karakteristik Reinforcement
Learning
Biasanya berupa permasalah yang
membutuhkan aktifitas eksplorasi,
sehingga cukup sesu...
Fundamentals of Algorithmic
Problem Solving
• Understanding the Problem
• Ascertaining the Capabilities of the Computation...
GOAL Dari CS?
• PERFORMANCE....
– EFEKTIVITAS...
– EFISIENSI...
Thanks...
Good Luck...

http://about.me/hendriKarisma
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Slide Presentasi Kelompok Keilmuan E

  1. 1. Artificial Intelligence Kelompok Keilmuan E (Game Tech dan Artificial Intelligence) Program Studi Teknik Informatika Universitas Komputer Indonesia 2014
  2. 2. Kelompok Keilmuan E • • • • • Nelly Indriani W Irfan Maliki Ednawati Rainarli Hendri Karisma Ken Kinanti Purnamasari
  3. 3. In fact, through our entire life we never stop learning new things. This has been crucial for our survival, but it also stimulates our curiosity.
  4. 4. Apa itu tugas akhir?? Skripsi/Tugas Akhir merupakan capstone pada pendidikan tinggi (undegraduate), untuk membangun dan menguji kemampuan dan pengetahuan selama mengikuti pendidikan di perguruan tinggi dan latihan untuk menjadi seorang profesional.
  5. 5. Kurikulum berdasarkan ACM/IEEE 2005 • • • • • Computer engineering Computer science Information systems Information technology Software engineering
  6. 6. Tujuan • Educational • Research
  7. 7. Educational 1. develop your critical thinking; 2. enhance your ability to work independently; 3. increase your understanding of how to use and appreciate scientific methods as tools for problem solving; and 4. develop your presentation skills, oral as well as written. With “critical thinking” we mean the ability to approach something new in a systematic and logical way, and to use creative and diverse, yet systematic ways to approach and solve a problem
  8. 8. Research • you will deepen your understanding of the subject area, and contribute to the common knowledge and understanding of the subject area • your project must have aspects that are original. it is generally not enough to repeat the work of others, since it is regarded as a waste of resources (time, money etc), unless, that is, your purpose is to confirm or reject previous findings
  9. 9. Computer Science “Computer science is no more about computers than astronomy is about telescopes.” , Edsger W. Dijkstra
  10. 10. Computer Science Characteristic • An empirical discipline, in which each new artefact, e.g. a program, can be seen as an experiment, the structure and behaviour of which can be studied. • Concerned with a number of different issues seen from a technological perspective, e.g • Theoretical aspects, such as numerical analysis, data structures and algorithms; • The relationship between different pieces of software (i.e. different types of architecture, such as client-server, two-tier, three-tier, distributed system, High Performance Computing); • Techniques and tools for developing software (i.e. software engineering, programming languages and operating systems).
  11. 11. Ilmu dan Rekayasa Komputer • • • • • • • • Combinatorial Graph Algorithm Network Sorting Searhing Learning Artificial Intelligence Perceptual Computing • Recognition System • Information Theory and Digital signal Processing • Physics • Bioinformatics • Big Data • Etc.
  12. 12. Artificial Intelligence • • • • Problem-solving Knowledge and reasoning Planning Uncertain Knowledge (probabilistic reasoning) • Learning
  13. 13. AI • • • • Communicating perceiving Acting what is AI ? – Acting humanly – Thinking humanly – Acting rationally – Thinking rationally
  14. 14. Foundation • • • • • • • Philosophy Mathematics Economics Neuroscience Psychology Computer Engineering Control theory and Cybernetics
  15. 15. AI • • • • Intelligent Agents Searhing strategies Optimal Decisions/Strategies in Game Machine Learning – Information Theory – Probabilistic Model – Graph Model
  16. 16. Applications • • • • • • • • • • • • Speech Recognition/Natural Language Processing Digital Signal Processing Computer Vision Perceptual Computing Social Analytic Prediction Clustering Security Biometrics Knowledge Finder (Datamining) Business Intelligence etc...
  17. 17. Learning • Inductive – proses belajar berdasarkan informasi yang spesifik guna mendapatkan pola dalam menyelesaikan masalah tersebut, dan pola yang didapatkan dari informasi-informasi spesifik tersebut digunakan untuk menyelesaikan masalah yang baru/akan datang (umum). – Machine Learning (future) • Deductive – proses belajar berdasarakan informasi yang general untuk menyelesaikan masalah yang spesifik. – Sistem Pakar (dead)
  18. 18. Machine Learning • Supervised • Unsupervised • Reinforcement Learning
  19. 19. Karakteristik Supervised • Masalah yang diselesaikan biasanya berbentuk klasifikasi, dataset yang dimiliki oleh kasus yang berbentuk klasifikasi biasanya selain memiliki atribut untuk setiap instancesnya, namun juga sudah memiliki kelas yang jelas. • Sehingga task selanjutnya dari hipotesis atau model yang ditemukan adalah melakukan klasifikasi terhadap instance yang baru dan belum memiliki label (belum diklasifikasi). • ID3, C4.5, Artifcial Neural Network, Support Vector Machine dan lain-lain.
  20. 20. Karakteristik Unsupervised • Unsupervised Learning biasanya memiliki kata kunci clustering atau melakukan pengklusteran terhadap sekelompok data atau sekelompok instances yang tidak memiliki label. • Sehingga memiliki informasi bahwa terdapat sekumpulan data yang membentuk cluster. • Contohnya seperti K-Mean, Graph Algorithm, EM (Expectation Maximization) Algorithm.
  21. 21. Karakteristik Reinforcement Learning Biasanya berupa permasalah yang membutuhkan aktifitas eksplorasi, sehingga cukup sesusai jika digunakan untuk membangun suatu intelijen pada suatau game (terutama puzzle).
  22. 22. Fundamentals of Algorithmic Problem Solving • Understanding the Problem • Ascertaining the Capabilities of the Computational Device • Choosing between Exact and Approximate Problem Solving • Algorithm Design Techniques (strategy) • Designing an Algorithm and Data Structures • Methods of Specifying an Algorithm (exp: pseudocode ) • Proving an Algorithm’s Correctness • Analyzing an Algorithm • (efficiency, effectivity, simplycity, generality) • Coding an Algorithm
  23. 23. GOAL Dari CS? • PERFORMANCE.... – EFEKTIVITAS... – EFISIENSI...
  24. 24. Thanks... Good Luck... http://about.me/hendriKarisma

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