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Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
Slide Presentasi Kelompok Keilmuan E
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Slide Presentasi Kelompok Keilmuan E

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Slide Presentasi Kelompok Keilmuan E

Slide Presentasi Kelompok Keilmuan E

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  • 1. Artificial Intelligence Kelompok Keilmuan E (Game Tech dan Artificial Intelligence) Program Studi Teknik Informatika Universitas Komputer Indonesia 2014
  • 2. Kelompok Keilmuan E • • • • • Nelly Indriani W Irfan Maliki Ednawati Rainarli Hendri Karisma Ken Kinanti Purnamasari
  • 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. 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. Kurikulum berdasarkan ACM/IEEE 2005 • • • • • Computer engineering Computer science Information systems Information technology Software engineering
  • 6. Tujuan • Educational • Research
  • 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. 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. Computer Science “Computer science is no more about computers than astronomy is about telescopes.” , Edsger W. Dijkstra
  • 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. 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. Artificial Intelligence • • • • Problem-solving Knowledge and reasoning Planning Uncertain Knowledge (probabilistic reasoning) • Learning
  • 13. AI • • • • Communicating perceiving Acting what is AI ? – Acting humanly – Thinking humanly – Acting rationally – Thinking rationally
  • 14. Foundation • • • • • • • Philosophy Mathematics Economics Neuroscience Psychology Computer Engineering Control theory and Cybernetics
  • 15. AI • • • • Intelligent Agents Searhing strategies Optimal Decisions/Strategies in Game Machine Learning – Information Theory – Probabilistic Model – Graph Model
  • 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. 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. Machine Learning • Supervised • Unsupervised • Reinforcement Learning
  • 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. 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. 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. 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. GOAL Dari CS? • PERFORMANCE.... – EFEKTIVITAS... – EFISIENSI...
  • 24. Thanks... Good Luck... http://about.me/hendriKarisma

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