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Dr. Andi Sunyoto M.Kom

Lecturer of Universitas Amikom Yogyakarta
  • Computer Vision
  • Computer science
  • Artificial Intelligence
  • Digital Image Processing
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Academic

Lecturer

Artificial

Intelligence

Lecture Slides

  1. 01. Introduction AI
  2. 02. K-Nearest Neighbor
  3. 03. Naive Bayes
  4. 04. Linear Regression
  5. 05. K-Means
  6. 06. Introduction AI
  7. 07. K-Nearest Neighbor
  8. 08. Ujian Tengah Semester
  9. 09. Naive Bayes
  10. 10. Linear Regression
  11. 11. K-Means
  12. 12. Introduction AI
  13. 13. K-Nearest Neighbor
  14. 14. Naive Bayes
  15. 15. Linear Regression
  16. 16. Ujian Akhir Semester (UAS)

Mata Kuliah Artificial Intelligence melatih mahasiswa untuk memahami ide dasar, intuisi, konsep, algoritma dan teknik untuk membuat komputer menjadi lebih cerdas melalui proses learning from data. Materi yang disampaikan fokus pada konsep AI yang paling populer yaitu Machine Learning.

Statistic

Basic

Lecture Slides

  1. 01. Intro Statistics
  2. 02. Frequency Distribution
  3. 03. Central Tendency
  4. 04. Measurement of Position
  5. 05. Measurement of Dispersion
  6. 06. Skewness of Data
  7. 07. Measurement of Kurtosis
  8. 08. Ujian Tengah Semester
  9. 09. Liner Regression
  10. 10. Performance of Regression
  11. 11. Multivariate Linear Regression
  12. 12. Moving Averages
  13. 13. Simple Exponential Smoothing
  14. 14. Correlation Analysis
  15. 15. Research Trends of Forecasting
  16. 16. Ujian Akhir Semester (UAS)

Mata Kuliah Statistic melatih mahasiswa untuk memahami ide dasar, intuisi, konsep statistik, dan penggunaan ilmu statistik untuk penyelesaian masalah dibidang komputer. Materi yang disampaikan fokus pada konsep statistik dan penggunaan statistik untuk peramalan (forecasting).

Research

Methodology

Lecture Slides

  1. 01. Konsep Dasar Penelitian
  2. 02. Klasifikasi Penelitian
  3. 03. Tema, Trend Penelitian Populer
  4. 04. Teknik, Langkah, dan Organisasi Referensi Penelitian
  5. 05. Literatur Review
  6. 06. Identifikasi Masalah
  7. 07. Desain, Teknik Penulisan Bab 1-3, Sample Penelitian)
  8. 08. Ujian Tengah Semester (Proposal Penelitian (Bab 1-3))
  9. 09. Teknik Pengumpulan Data, Data Understanding
  10. 10. Desain Eksperimen
  11. 11. Teknik hasil dan temuan penelitian (Sample penelitian)
  12. 12. Teknik penulisan Paper (Hasil, diskusi dan kesimpulan)
  13. 13. Presentasi draft paper #1
  14. 14. Presentasi draft paper #2
  15. 15. Review hasil penelitian, Publication ethic
  16. 16. Ujian Akhir Semester (Draft Paper Hasil Penelitian (Bab 4-5))

Mata kuliah metodologi penelitian memberikan pemahaman mahasiswa untuk mampu menerapkan konsep dasae penelitian. Pada akhir kuliah, mahasiswa dapat menghasilkan draf proposal penelitian yang akan dikerjakan

Business

Intelligence

Lecture Slides

  1. 01. Intro Business Intelligence
  2. 02. Data Warehousing and Data Mining
  3. 03. Business Reporting, Visual Analytic, and Business Performance
  4. 04. Data Mining
  5. 05. Technique for Predictive Modelling
  6. 06. Presentation Interactive Dashboard Week #1
  7. 07. Presentation Interactive Dashboard Week #2
  8. 08. Ujian Tengah Semester
  9. 09. Discuss for Final Project Predictive Analytic
  10. 10. Text Analytic, Text Mining, and Sentiment Analysis
  11. 11. Web Analytic, Web Mining, and Social Analysis
  12. 12. Big Data and Analytic | How to Write the Research
  13. 13. Emerging Trends and Future Impacts
  14. 14. Presentation Predictive Analysis Week #1
  15. 15. Presentation Predictive Analysis Week #2
  16. 16. Ujian Akhir Semester

Business Intelligence (BI) refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. The purpose of business intelligence is to support better business decision-making. This course provides an overview of the technology of BI and the application of BI to an organization’s strategies and goals.

Computer

Vision

Lecture Slides

  1. 01. Introduction to Computer Vision
  2. 02. Image Representation and Analysis
  3. 03. Color Space
  4. 04. Project and Paper
  5. 05. Image Filtering
  6. 06. Edge Detection and Template Matching
  7. 07. Image Matching (Corners and Features)
  8. 08. Ujian Tengah Semester
  9. 09. Image Segmentation
  10. 10. How to Write a Paper
  11. 11. Image Future Extraction
  12. 12. Image Classification (Recognition)
  13. 13. Convolutional Neural Network (CNN)
  14. 14. Object Detection & Recognition
  15. 15. Project and Presentation
  16. 16. Ujian Akhir Semester

Apply techniques to extract useful features from an image. Apply techniques to recognize patterns and objects. Understand and apply theoretical and practical capabilities of Computer Vision. Formulate solutions to problems in Computer Vision. Describe the foundation of image formation and image analysis. Understand the basics of 2D and 3D Computer Vision. Get an exposure to advanced concepts, including state of the art deep learning architectures, in all aspects of computer vision.

Video

Collection