Komputasi Cerdas dan Visualisasi

(1)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

1

Program Studi

Department

TEKNIK INFORMATIKA

INFORMATICS

Jenjang Pendidikan

Programme

PASCA SARJANA

POSTGRADUATE

Kompetensi Lulusan

x Memahami dan menguasai prinsip dasar bidang informatika.

x Merancang dan mengimplementasikan sistem serta mengintegrasikan hardware dan software.

x Mendayagunakan, mengevaluasi dan mengidentifikasi pengembangan sistem berbasiskan komputer.

x Menguasai dasar konsep dan keahlian pemrograman komputer .

x Mempunyai keahlian tertentu di topik-topik lanjut komputing.

x Mempunyai keahlian meneliti sesuai dengan metodologi penelitian.

x Mempunyai keahlian komunikasi interpersonal, teamwork serta manajerial

x Mampu menunjukan sikap yang menghargai, melindungi dan meningkatkan etika professional.

Graduate Competence

x Understanding and mastering the basic principles of informatics.

x Designing and implementing systems and to integrate hardware

and software.

x Utilizing, evaluating and identifying the development of

computer-based systems.

x Mastering the basic concepts and computer programming skills.

x Mastering a particular expertise in advanced topics komputing.

x Mastering research skills in accordance with the research

methodology.

x Having interpersonal communication skills, teamwork, and

managerial

x Having attitude of respect, protect and improve the professional

(2)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

2 STRUKTUR KURIKULUM/

COURSE STRUCTURE

No. Kode MK Code

Nama Mata Kuliah (MK) Course Title

sks Credits SEMESTER I

1 KI092301 Kecerdasan Buatan Artificial Intelligence

3 2 KI092302 Jaringan Komputer

Computer Networks

3 3 KI092303 Rekayasa Perangkat Lunak

Software Engineering

3 4 KI092304 Metodologi Penelitian

Research Method

3

Jumlah sks/Total of credits 12

BIDANG KEAHLIAN KOMPUTASI CERDAS DAN VISUALISASI INTELLIGENT COMPUTING AND VISUALIZATION EXPERTISE Semester II

1 KI092311 Topik Dalam Pengenalan Pola Topic in Pattern Recognition

3 2 KI092312 Topik Dalam Kecerdasan Komputasional

Topic in Intelligent Computing

3 3 KI092313 Topik Dalam Simulasi Diskrit

Topic in Discrete Simulation 3 4 KI092314 Topik Dalam Data Mining

Topic in Data Mining 3

Semester III

1 KI092315 Topik Dalam Pemrosesan Citra dan Visi Komputer Topic in Image Processing and Computer Vision

3 2 KI092316 Topik Dalam Sistem Temu Kembali Informasi

Topic in Information Retrieval

3

SEMESTER IV

1 KI092361 Tesis Thesis

6

(3)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

3 SILABUS KURIKULUM/COURSE SYLLABUS

MATA KULIAH/

COURSE TITLE

KI092303: Rekayasa Perangkat Lunak

KI092303: Software Engineering

Credits :

3

Mahasiswa mampu menjelaskan tahapan dan metodologi pembangunan perangkat lunak secara benar baik secara mandiri atau juga dalam kerjasama tim.

Students are capable of explaining the phase and the methodology of software building process either in personal or in group.

KOMPETENSI/

COMPETENCY

x Mahasiswa mampu menjelaskan model pengembangan perangkat lunak.

x Mahasiswa mampu merumuskan dengan teliti berbagai macam metodologi pengembangan perangkat lunak.

x Mahasiswa mampu menganalisis & melakukan perancangan model perangkat lunak .

x Mahasiswa mampu bekerjasama dan berfikir kreatif dalam membuat perangkat lunak serta mempresentasikan hasil karya rancangan.

x Students are able to explain software development.

x Students are able to determine in detail the methodologies of

software development.

x Students are able to analyze and construct software model.

x Students are able to work as a team and think creatively in

constructing and presenting software application

POKOK BAHASAN/

SUBJECTS

(4)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

4

rancangan sistem secara konseptual, perancangan fisik- keluaran, masukan, antarmuka pemakai dan sistem, platform, basis data, modul, kontrol, dokumentasi, pengujian, ataupun rencana konversi; Implementasi sistem, Pemrograman dan pengujian, Instalasi perangkat keras dan perangkat lunak, Pelatihan kepada pemakai,Pembuatan dokumentasi ; Software Testing Strategies, Unit Testing, Integration Testing; Software Testing Techniques, Whitebox testing, Blackbox testing, System Testing; Quality Assurance; Operasi dan pemeliharaan, Perawatan perfektif, Perawatan adaptif, Perawatan korektif, Evaluasi dan pengukuran produk perangkat lunak, Software metric;Software Performance, SQA & Reviews perangkat lunak, Software Reuse, Manajemen Resiko, Specification Configuration Management; Pembiayaan dan Estimasi Perangkat Lunak, Cocomo, Delphi, Activity Base costing.

x Introduction of Software Model, Software Concept, Case Study:

Adventures Works Cycle Application, Software Product Perspective, Software Process Model, MSF Model, Unified Process, Agile Model, Integrated Activities, System Engineering, Requirement Engineering, Analysis Model; Software Design Model, System

Design: Conceptual and Physic Model, Conceptual Design, Design

Alternative Evaluation, Design Specification Preparation, Conceptual System Design Report Preparation, Physical Output Design, Input, User Interface and System, Platform, Database, module, Control, Documentation, Testing and Conversion Plan, System Implementation, Programming and Testing, Hardware and Software Installation, User Training, Documentation, Software Testing Strategies, Unit Testing, Integration Testing, Software Testing Techniques, Whitebox Testing, Blackbox Testing, System Testing, Quality Assurance, Operation and Maintenance, Perfective Maintenance, Adaptive Maintenance, Corrective Maintenance, Evaluation and Measurement of Software Product, Software Metric, Software Performance, SQA & Reviews, Software Reuse, Risk Management, Specification Configuration Management, Software Cost & Estimation, Cocomo, Delphi, Activity Base Costing.

PUSTAKA

UTAMA/

(5)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

5

REFERENCES _x Sommerville, I., Software Engineering 8th edition, Addison-Wesley,

2007.

x Stephen R. Schach: Object-Oriented and Classical Software Engineering, 7th Edition, 2007

(6)

Kurikulum/

KI092311: Topik Dalam Pengenalan Pola

KI092311: Advance Pattern Recognition

Credits :

3

Mahasiswa mampu melakukan perancangan model dengan menganalisis permasalahan nyata dalam lingkup pengenalan pola dan kemudian mengimplementasikannya baik secara mandiri atau juga dalam kerjasama tim.

Students are able to design pattern recognition model from real problem analysis then implement it in personal or in team works.

KOMPETENSI/

COMPETENCY

x Mahasiswa mampu memahami penggunaan ilmu-ilmu dasar statistika dengan teknik-teknik yang diperkenalkan dalam lingkup pengenalan pola agar dapat menerapkan pemakaiannya untuk permasalahan nyata,

x Mahasiswa mampu menganalisis serta berfikir analitis dengan pemodelan kalkulus dan melakukan perancangan dari permasalahan dengan metode yang paling sesuai,

x Mahasiswa mampu mengimplementasikan solusi pemodelan kedalam bentuk aljabar linear dengan bantuan tool komputasi numerik serta kemudian mempresentasikan hasil akhir,

x Mahasiswa mampu bekerjasama dalam memecahkan permasalahan nyata melalui pengenalan pola mulai dari tahap analisa sampai implementasi.

x Students are able to understand and implement basic statistic

knowledge and pattern recognition techniques in real problems.

x Students are able to analyze calculus model as a foundation to

construct problem model with suitable method.

x Students are able to implement solution model into linear algebra

using numeric computation tools to yield an final result.

x Students are able to cooperate to solve some real problem using

pattern recognition techniques from analytic to implementation phase.

(7)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

7

SUBJECTS dengan fungsi logistik, Pembahasan makalah dengan topik terkait.

Pemilihan fitur; Deteksi outlier, Pemilihan berdasarkan statistik, Metrik untuk mengukur pemisahan antar klas dalam klasifikasi, Pembahasan makalah dengan topik terkait. Pembangkitan fitur; Penggunaan teknik wavelet, Penggunaan teknik independent dan principal component analysis, Penggunaan teknik fractal, Pembahasan makalah dengan topik terkait. Klasifikasi non-linear; Support Vector Machine sebagai pengklasifikasi, Pembahasan makalah dengan topik terkait. Clustering; Analisa klaster secara partitional dan hierarchical, Analisa klaster berdasarkan densitas, Pembahasan makalah dengan topik terkait.

x Pattern Recognition Basic Concept; Introduction of Classification

Problems, Bayesian Theory. Linear Classification; Estimation Parameter, Discrimination Function, Least Square Method, Discrimination with Logistic Function, Paper Review in Related Fields, Feature Option, Outliner Detection, Statistic Determination, Cluster Metrics for Classification, Paper Review in Related Fields ,Non Linear Classification, Classification using Support Vector Machine, Paper Review in Related Fields, Clustering, Partition and Hierarchy Cluster Analysis, Density Based Cluster Analysis, Paper Review in Related Fields.

PUSTAKA UTAMA/

REFERENCES

x Theodoridis, S., Koutroumbas, K., “Pattern Classification”, 3rd ed., Academic Press, 2006.

(8)

Kurikulum/

KI092312: Topik Dalam Kecerdasan Komputasional

KI092312: Advance Computational Intelligent

Credits :

3

Peserta mata kuliah mampu memahami karakteristik dan teknik pembelajaran berbagai tipe metode kecerdasan komputasional serta dapat mengaplikasikan metode kecerdasan komputasional tersebut pada persoalan dunia nyata berdasarkan referensi makalah dari Jurnal yang relevan.

Students are able to understand characteristic and learning techniques several computational intelligent methods and also able to implement it to solve real problems.

KOMPETENSI/

COMPETENCY

x Peserta mata kuliah mampu memahami karakteristik dan teknik pembelajaran tipe-tipe metode kecerdasan komputasional, yang meliputi ; Jaringan Saraf Tiruan dan variannya, Komputasi Evolusioner, Swarm Intelligence, Support Vector Machine dan Kernel.

x Peserta mata kuliah dapat mengaplikasikan metode kecerdasan komputasional pada persoalan dunia nyata, yang meliputi: optimasi, identifikasi sistem dinamis dan klasifikasi pola berdasarkan referensi makalah dari Jurnal yang relevan.

x Students are able to understand characteristic and learning

techniques several computational intelligent methods such as Artificial Neural Networks, Evolutionary Computation, Swarm Intelligence, Support Vector Machine and Kernel.

x Students are able to implement computational intelligent method

to solve real problems such as Optimization, Dynamic System Identification, and Pattern Classification.

SUBJECTS

(9)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

9

penerapan JST pada permasalahan klasifikasi terawasi. JST Radial Basis Function; Arsitektur JST RBF, Teori Regularisasi dan Jaringan Regularisasi, Algoritma Pembelajaran RBF serta teknik optimasi pembelajaran RBF lanjut, review makalah tentang JST Runge-Kutta RBF dan aplikasinya pada permasalahan Identifikasi Sistem Dinamik. Jaringan Saraf Rekuren; Arsitektur RNN, review metode numerik problem Ordinary Differential Equations, Pembelajaran RNN, review makalah aplikasi RNN pada Optimasi Convex dan Fusi Citra, review makalah aplikasi RNN pada Permasalahan Identifikasi Sistem Dinamik dengan Algoritma Optimal Bounded Ellipsoid. Algoritma Genetika; Struktur Algoritma Genetika dan Metoda Search Berbasis Populasi, Rancangan Kromosom dan Fungsi Fitness, Operator Dasar GA : CrossOver, Mutasi dan Seleksi, review makalah aplikasi GA pada Steganography dan Problem Transportasi. Ant Colony; Komponen Metode Ant Colony : tabel jejak pheromone, fungsi evaporasi, varian dengan struktur clan, modifikasi Bee Colony, review makalah aplikasi Modifikasi Ant Colony pada TSP dan Optimasi Penjadwalan Produksi. Particle Swarm Optimization; Komponen PSO : Particle, Fungsi Update Posisi, Fungsi Update Velocity, Momen Inersia, review makalah Aplikasi CL-PSO pada Optimasi Nonlinear. Support Vector Machine; Structural Risk Minimization dan Dimensi VC, Algoritma Pembelajaran SVM, variasi Metode Pembelajaran : Least Squares-SVM, SMO, aplikasi Metode Kernel, review makalah aplikasi pembobotan spektral pada SVM untuk klasifikasi citra Hyperspectral. Metode Hybrid; Arsitektur Neuro-Fuzzy, pembelajaran Jaringan Neuro-Fuzzy, review makalah aplikasi Jaringan Neuro-Fuzzy pada Proses Denoising Citra, Metode GA-Fuzzy, aplikasi GA-Fuzzy pada optimasi produksi dan distribusi rantai pasok.

x Learning Proccess; Learning Methods, Learning Component: Task,

(10)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

10

Genetic Algorithm; Genetic Algorithm Structure and Population Based Searching Method, Chromosome and Fitness Construction, Basic GA Operator: Crossover, Mutation and Selection, Paper Review GA in Steganography and Transportation Problem. Ant

Colony, Ant Colony Component Method: Pheromone Track Table,

Evaporation Function, Clan Structure Variation, Modification Bee Colony, Paper Review Ant Colony Modification in TSP Application and Product Scheduling Optimization. Particle Swarm Optimization; PSO Component: Particle, Position Update Function, Velocity Update Function, Inertia Moment, Paper Review CL-PSO Application in Non-Linear Optimization. Support Vector Machine; Structural Risk Minimization and VC Dimension, SVM Learning Algorithm, Learning Variation Method: Least Square-SVM, SMO, Kernel Method Application, Paper Review Spectral Weighted in

SVM for Hyperspectral Image Classification. Hybrid Method;

Neuron-Fuzzy Architecture, Neuron-Fuzzy Network Studying, Paper Review Neuron-Fuzzy Network Application for Image De-noising Process, GA-Fuzzy Method, GA-Fuzzy Application in Supply Chain and Production Optimization.

REFERENCES

x Amit Konar, Computational Intelligence, Springer, 2005.

x C. H. Bishop, Pattern Recognition and Machine Learning, Springer Science, 2006.

x Simon Haykin, Neural Networks: A Comprehensive Foundation (2nd Edition), Prentice Hall, 1998.

x Christian Blum, Daniel Merkle, Swarm Intelligence : Introduction and Applications, Springer-Verlag 2008.

(11)

Kurikulum/

KI092313: Topik Dalam Simulasi Diskrit

KI092313: Advance Discrete Simulation

Credits :

3

Mahasiswa mengenal topik-topik penelitian mengenai simulasi dan mampu membuat proposal penelitian tesis dengan topik simulasi.

Students recognize research topics in simulation and capable of writing research proposal thesis in simulation topics

KOMPETENSI/

COMPETENCY

x Mahasiswa mampu membangkitkan bilangan acak bivariat

x Mahasiswa mampu menggunakan prinsip simulasi berorientasi obyek

x Mahasiswa mampu membuat model simulasi Monte Carlo dan aplikasinya

x Mahasiswa mengerti konsep simulasi terdistribusi

x Mahasiswa mampu membuat review paper mengenai simulasi

x Mahasiswa mampu melakukan analisis data dengan output majemuk (multiple outputs)

x Mahasiswa mampu membuat proposal penelitian tesis dengan topik simulasi.

x Students are able to generate bi-variant number.

x Students are able to utilize object oriented simulation concept .

x Students are able to design Monte Carlo simulation model and its

applications.

x Students understand distributed simulation concept

x Students are able to write paper review in simulation topics

x Students are able to analyze data for multiple output

x Students are able to write research proposal for thesis in simulation topics

SUBJECTS

(12)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

12

sistem alternatif Mengevaluasi hasil analisis. Bilangan acak bivariat dam pembangkitannya. Simulasi berorientasi obyek. Simulasi Monte Carlo dan aplikasinya. Simulasi terdistribusi. Analisis data dengan output majemuk (multiple outputs): analisis korelasi, test. Simulasi jaringan (Network Simulation). Pembuatan proposal penelitian / review paper mengenai simulasi.

x Basic Simulation Review Component Material: Probability

Distribution, Fitting Distribution, Random Number Generalization: LCG (mixed, multiplicative) Method, Tausworte Method, Random Number Evaluation, Simulation Model Building, Mathematic Analysis Based for Queue, Parameter Determination for System Productivity Evaluation. Simulation Data Output Retrieval, Simulation Output Analysis. Alternative System Design. Analysis Output Evaluation. Bi-Variants Random Number Generalization, Object Oriented Simulation. Monte Carlo Simulation and its Application, Distributed Simulation, Data Analysis for Multiple Outputs: Correlation Analysis, Test, Network Simulation. Research Proposal or Paper Review in Simulation Topics.

REFERENCES

x Banks, Jerry, et. al., ”Discrete-Event System Simulation", 3rd ed., PrenticeHall, New Jersey, 2001.

x Law, Averill M., et. al., "Simulation Modeling and Analysis", McGraw-Hill, 1990.

(13)

Kurikulum/

KS091202 : Topik Dalam Pemrosesan Citra dan Visi

Komputer

KS091202 : Advanced Topic in Image Processing and

Computer Vision

Mahasiswa mampu menerapkan pemrosesan citra baik citra tunggal maupun kumpulan citra dan menghasilkan informasi berdasarkan data citra tersebut.

Students can apply image processing either a single image or set of images and generate information based on image data.

KOMPETENSI/

COMPETENCY

x Mahasiswa mampu menjelaskan dengan benar konsep pembentukan citra beserta teori tentang sumber cahaya, bayangan, dan warna, serta konsep konvolusi, filtering linier dan non-linier, serta tekstur.

x Mahasiswa mampu menganalisis & melakukan perancangan sistem rekonstruksi bentuk dan motion berdasarkan kumpulan citra,

x Mahasiswa mampu menganalisis & menerapkan metode-metode segmentasi citra

x Mahasiswa mampu menganalisis & membuat aplikasi sebagai penerapan metode-metode tracking objek

x Mahasiswa mampu menganalisis & membuat aplikasi sebagai penerapan metode-metode pengenalan objek

x Mahasiswa mampu bekerjasama dan berfikir kreatif dalam membuat aplikasi pemrosesan citra dan visi komputer.

x Students are able to explain the true concept of image formation

and the theory of light sources, shadows, and color, and the concept of convolution, linear filtering and non-linear, and texture.

x Students can analyze & perform reconstruction of system design

based on shape and motion imagery collection,

x Students are able to analyze & apply the methods of image

segmentation

x Students can analyze & make the application as the application of

methods of object tracking

x Students can analyze & make the application as the application of

methods of object recognition

x Students can work and creative thinking in making the image

processing applications and computer vision.

(14)

Kurikulum/

Lambertian dan Albedo, Permukaan Spekular, Model-model Shading Lokal, Photometric Stereo,

x Warna dan Fitur Citra: Representasi Warna, Warna Permukaan berdasarkan Warna Citra, Fitur-fitur Geometris, Fitur-fitur Analitis,

x Filtering dan Deteksi Tepi: Konvolusi, Smoothing, Median Filter, Morfologi Matematika, Noise, Filter DoG,

x Tekstur: Representasi Tekstur, Shape from Texture,

x Rekonstruksi Bentuk: Geometri dari Kumpulan View, Stereopsis, Affine Structure from Motion, Projective Structure from Motion,

x Segmentasi: Metode Clustering Sederhana, Segmentasi menggunakan K-means, Segmentasi menggunakan Eigenvector, Segmentasi menggunakan Algoritma EM,

x Tracking: Kalman Filter, Particle Filtering,

x Pengenalan Objek: Classifiers, Pemilihan Fitur, Jaringan Syaraf, Support Vector Machine, Hidden Markov Models. Pembahasan makalah dengan topik terkait.

x Image Formation Theory: The concept of light, BRDF, and Albedo

Lambertian Surface, Surface Spekular, Shading models Local, photometric stereo,

x Color and Feature Image: Color representation, based on Surface

Color Image Color, geometric features, analytical features,

x Filtering and edge detection: convolution, Smoothing, Median

Filters, Morphology Mathematics, Noise, Filter DoG,

x Texture: Texture Representation, Shape from Texture,

x Reconstruction Form: Geometry of the collection View, Stereopsis,

Structure from Motion affine, projective Structure from Motion,

x Segmentation: Simple Clustering Methods, Segmentation using

K-means, segmentation using Eigenvector, Segmentation using EM algorithm,

x Tracking: Kalman Filter, Particle Filtering,

x Introduction to Object: Classifiers, Feature Selection, Neurosurgery

Network, Support Vector Machine, Hidden Markov Models. The discussion paper with related topics.

REFERENCES

x Forsyth and Ponce , “Computer Vision A Modern Approach”, Prentice-Hall, 2003

(15)

Kurikulum/

KI092316 : Topik dalam Sistem Temu Kembali Informasi

KI092316 : Advanced Topic on Information Retrieval

Credits :

3

Mahasiswa mampu memahami karakteristik dan teknik - teknik pada sistem temu kembali informasi (STKI) untuk mengekplorasi keterbaharuan dari topik-topik terkait, dan kemudian mampu menulis karya ilmiah berdasarkan hasil eksplorasi.

Students can analyze real-world problems and design solution model based on information retrieval techniques, and then implement the model independently as well as in teamwork.

KOMPETENSI/

COMPETENCY

x Kemampuan untuk memahami konsep, teori, istilah dalam berbagai macam model STKI.

x Kemampuan untuk menganalisa, melakukan perancangan dan implementasi solusi dari permasalahan nyata pada topik tertentu dengan metode STKI yang sesuai.

x Kemampuan untuk telaah artikel dari beberapa referensi publikasi internasional dalam topik STKI yang dilanjutkan dengan menyusun makalah hasil studi kepustakaan;

x Kemampuan membuat sebuah proposal penelitian dalam topik STKI yang jika dimungkinkan dapat dilanjutkan untuk dijadikan sebagai proposal tesis.

x The ability to understand concepts and theories within scope of

information retrieval (IR) techniques.

x The ability to analyze, design, and implement some solutions from

real-world problems within selected topics using appropriate IR approaches.

x The ability to explore articles from international publications

related to IR topics and then make some review papers based on literature studies.

x The ability to make a research proposal within IR topics and if

possible can extend it as thesis proposal.

SUBJECTS

(16)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

16

space classification, Flat clustering, Hierarchical clustering. Topic Analysis on Information: Matrix decomposition, Latent semantic indexing. Web Search Basics: Web crawling,Content analysis, Link analysis, Semantic web. Writing Papers.

REFERENCES

x Christopher D. Manning, Prabhakar Raghavan, Hinrich Schutze, Introduction to Information Retrieval, Cambridge University Press, 2008.

x Ricardo Baeza-Yates, Berthier Ribeiro-Neto, Modern Information Retrieval, ACM Press New York, Addition Wesley, 1999.

x Ronen Feldman, James Sanger, The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data, Cambridge University Press, 2006.

x Articles on Science Direct: Information Processing & Management.

(17)

Kurikulum/

KI092314: Topik Dalam Data Mining

KI092314: Advance Data Mining

Credits :

3

Mahasiswa mampu memahami, menelaah, membuat makalah hasil studi kepustakaan, dan membuat proposal penelitian untuk sebuah topik tertentu dalam data mining.

Students are able to understand, recognize and write report of reference study or research proposal in data mining topics

KOMPETENSI/

COMPETENCY

x Mahasiswa mampu memahami teknik-teknik dasar dan umum dalam data mining;

x Mahasiswa mampu menelaah beberapa artikel yang diterbaitkan dalam publikasi internasional dalam data mining;

x Mahasiswa mampu menyusun sebuah makalah hasil studi kepustakaan dari beberapa referensi publikasi dalam data mining;

x Mahasiswa mampu membuat sebuah proposal penelitian untuk topik tertentu dalam data mining, yang jika dimungkinkan dapat dilanjutkan untuk dijadikan sebagai proposal Tesis.

x Students understand basic and general techniques in data mining.

x Students are able to understand international published articles in

data mining.

x Students are able to write reference study reports in data mining.

x Students are able to write research proposal for thesis in data

mining

SUBJECTS

x Pendahuluan tentang teknik-teknik dasar dalam data mining: Pengertian dasar, Data Warehouse dan teknologi OLAP, Praproses data, Eksplorasi data. Klasifikasi: Model Decision Tree sebagai teknik dasar, Alternatif teknik-teknik lain untuk klasifikasi. Klasterisasi: Analisa klaster, Penggunaan teknik-teknik untuk proses klaster. Analisis asosiasi: Deskripsi konsep, Frequent Itemsets Mining, Closed dan Maximal Frequent Itemsets Mining, Sequential Patterns Mining. Deteksi Anomali: Identifikasi anomali dengan pendekatan statistik, Deteksi Outlier. Diskusi beberapa makalah terkait dengan keterbaruan penelitian dalam data mining.

(18)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

18

Data Warehouse and OLAP Technology, Data Pre-Process, Data Exploration. Classification: Decision Tree Model as a Basic Technique, Classification Alternative Techniques. Clustering: Cluster Analysis, Cluster Process Techniques, Association Analysis: Description Concept, Frequent Item-sets Mining, Closed and Maximal Frequent Item-sets Mining, Sequential Patterns Mining. Anomaly Detection: Anomaly Identification using Statistic Approach, Outlier Detection. Research Discussion Forum in Data Mining.

REFERENCES

x Tan, Pang-Ning, Steinbach, M., Kumar, V., “Introduction to Data Mining”, Pearson International Edition, 2006.

x Han, Jiawei, Kamber, M., “Data Mining: Concepts and Techniques”, 2nd ed., Morgan Kaufmann, 2005.

x Journal of IEEE Trans. on Knowledge and Data Eng., IEEE Comp. Society.

x Proceeding of IEEE Intl. Workshop on Data Minings IEEE Comp. Society.

x Journal of ACM Transactions of Database Systems, ACM Society.

x Proceeding of ACM Intl. Conference on Digital Libraries, ACM Society.