1. Data Mining: Penelitian Data Mining
Romi Satria Wahono

2. Romi Satria Wahono SD Sompok Semarang (1987) SMPN 8 Semarang (1990)
SMA Taruna Nusantara, Magelang (1993)
S1, S2 dan S3 (on-leave) Department of Computer Sciences Saitama University, Japan ( )
Research Interests: Software Engineering and Intelligent Systems
Founder IlmuKomputer.Com
Peneliti LIPI ( )
Founder dan CEO PT Brainmatics Cipta Informatika

3. Course Outline Pengenalan Data Mining Proses Data Mining
Object-Oriented Programming
Course Outline
Pengenalan Data Mining
Proses Data Mining
Evaluasi dan Validasi pada Data Mining
Metode dan Algoritma Data Mining
Penelitian Data Mining

4. Penelitian Data Mining

5. Penelitian Data Mining
Standard Proses Penelitian pada Data Mining
Journal Publications on Data Mining
Research on Classification
Research on Clustering
Research on Prediction
Research on Association Rule

6. Standard Proses Penelitian pada Data Mining
Object-Oriented Programming
Standard Proses Penelitian pada Data Mining

7. Data Mining Standard Process (CRISP–DM)
Object-Oriented Programming
Data Mining Standard Process (CRISP–DM)
A cross-industry standard was clearly required that is industry neutral, tool-neutral, and application-neutral
The Cross-Industry Standard Process for Data Mining (CRISP–DM) was developed in 1996 (Chapman, 2000)
CRISP-DM provides a nonproprietary and freely available standard process for fitting data mining into the general problem-solving strategy of a business or research unit

8. CRISP-DM romi@romisatriawahono.net Object-Oriented Programming

9. 1. Business Understanding Phase
Object-Oriented Programming
1. Business Understanding Phase
Enunciate the project objectives and requirements clearly in terms of the business or research unit as a whole
Translate these goals and restrictions into the formulation of a data mining problem definition
Prepare a preliminary strategy for achieving these objectives

10. 2. Data Understanding Phase
Object-Oriented Programming
2. Data Understanding Phase
Collect the data
Use exploratory data analysis to familiarize yourself with the data and discover initial insights
Evaluate the quality of the data
If desired, select interesting subsets that may contain actionable patterns

11. 3. Data Preparation Phase
Object-Oriented Programming
3. Data Preparation Phase
Prepare from the initial raw data the final data set that is to be used for all subsequent phases. This phase is very labor intensive
Select the cases and variables you want to analyze and that are appropriate for your analysis
Perform transformations on certain variables, if needed
Clean the raw data so that it is ready for the modeling tools

12. 4. Modeling phase Select and apply appropriate modeling techniques
Object-Oriented Programming
4. Modeling phase
Select and apply appropriate modeling techniques
Calibrate model settings to optimize results
Remember that often, several different techniques may be used for the same data mining problem
If necessary, loop back to the data preparation phase to bring the form of the data into line with the specific requirements of a particular data mining technique

13.
Object-Oriented Programming
5. Evaluation phase
Evaluate the one or more models delivered in the modeling phase for quality and effectiveness before deploying them for use in the field
Determine whether the model in fact achieves the objectives set for it in the first phase
Establish whether some important facet of the business or research problem has not been accounted for sufficiently
Come to a decision regarding use of the data mining results

14.
Object-Oriented Programming
6. Deployment phase
Make use of the models created: Model creation does not signify the completion of a project
Example of a simple deployment: Generate a report
Example of a more complex deployment: Implement a parallel data mining process in another department
For businesses, the customer often carries out the deployment based on your model

15.
Object-Oriented Programming
Latihan
Pelajari dan pahami Case Study 1-5 dari buku Larose (2005) Chapter 1
Pelajari dan pahami bagaimana menerapkan CRISP-DM pada tesis Firmansyah (2011) tentang penerapan algoritma C4.5 untuk penentuan kelayakan kredit

16. Journal Publications on Data Mining

17. Transactions and Journals
Review Paper (survey and state-of-the-art):
ACM Computing Surveys (CSUR)
Research Paper (technical):
ACM Transactions on Knowledge Discovery from Data (TKDD)
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Springer Data Mining and Knowledge Discovery
International Journal of Business Intelligence and Data Mining (IJBIDM)

18. Cognitive Assignment III
Object-Oriented Programming
Cognitive Assignment III
Baca 1 paper ilmiah yang diterbitkan di journal yang berhubungan dengan metode data mining yang sudah kita pelajari
Rangkumkan masing-masing dalam bentuk slide dengan struktur:
Latar Belakang Masalah (Research Background)
Pernyataan Masalah (Problem Statements)
Pertanyaan Penelitian (Research Questions)
Tujuan Penelitian (Research Objective)
Metode-Metode yang Sudah Ada (Existing Methods)
Metode yang Diusulkan (Proposed Method)
Hasil (Results)
Kesimpulan (Conclusion)
Presentasikan di depan kelas pada mata kuliah berikutnya

19.
Object-Oriented Programming
Referensi
Ian H. Witten, Frank Eibe, Mark A. Hall, Data mining: Practical Machine Learning Tools and Techniques 3rd Edition, Elsevier, 2011
Daniel T. Larose, Discovering Knowledge in Data: an Introduction to Data Mining, John Wiley & Sons, 2005
Florin Gorunescu, Data Mining: Concepts, Models and Techniques, Springer, 2011
Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques Second Edition, Elsevier, 2006
Oded Maimon and Lior Rokach, Data Mining and Knowledge Discovery Handbook Second Edition, Springer, 2010
Warren Liao and Evangelos Triantaphyllou (eds.), Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications, World Scientific, 2007