FACULTY OF MATHEMATICS AND NATURAL SCIENCE

(1)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

1 FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM

FACULTY OF MATHEMATICS AND NATURAL SCIENCE

Program Studi Department

STATISTIKA

Statistics

Jenjang Pendidikan Programme

Magister Sains

[Master of Science]

Kompetensi Lulusan

x [diisi maksimum 5 kompetensi lulusan yang utama dan ditulis

dengan bullet]

x Mampu melakukan penelitian pengembangan

dan penerapan statistika yang berkontribusi pada penyelesaian masalah riil di masyarakat

Graduate Competence

x [ditulis terjemahan bahasa inggris dengan cetak miring]

x Able to do development research in Statistics methods and to

applly Statistics that have contribution to solve on real problem

STRUKTUR KURIKULUM/

COURSE STRUCTURE

No. Kode MK

Code

Nama Mata Kuliah (MK)

Course Title

Sks

Credits

SEMESTER I

1 SS09 2301 Teori Probabilitas Probability Theory

3

2 Mata Kuliah Pilihan 1

Optional Subjects/Course 1

3

Jumlah sks/Total of credits 12

MATA KULIAH PILIHAN SEMESTER I/ Optional Subjects/Course Semester I

1

SS09 2211 Analisis Statistika

Statistical Analysis

3

2

SS09 2212

Desain Eksperimen

Design of Experiment

(2)

Kurikulum/

SS09 2213

Model Linear

Linear Models

3

4

SS09 2221

Riset Operasi

Operation Research

3

5

SS09 2222

PPIC

Product Planing an Inventory Control

3

6

SS09 2223

Stat. Pros. Control

Statistical Process control

3

SS09 2232

Metode Resampling

Resampling methods

3

9

SS09 2241

Studi Kependudukan

Demographic Study

3

10

SS09 2242

Riset Pemasaran

Marketing Research

3

11

SS09 2243

Statistik Ofisial

Official Statistics

3

SEMESTER II

1 SS09 2302 Statistik Inferensi Inference Statistics

3

MATA KULIAH PILIHAN SEMESTER II/ Optional Subjects/Course Semester II

1. SS09 2214

Analisis Multivariat

Multivariate Analysis

3

2. SS09 2215

An. Data Kualitatif

Qualitative data Analysis

3

3. SS09 2216

_{Statistik Spasial}

Spatial Statistics

3

4. SS09 2217

_{Reg. Nonparametrik}

Nonparametric regression

3

5. SS09 2218

_{An. Deret Waktu}

Time Series Analysis

3

6. SS09 2219

_{Proses Stokastik}

Stocastic Process

(3)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

3

7. SS09 2223

_{Stat. Pros. Control}

Statistical Process control

3

8. SS09 2224

_{Teori Antrian}

Queueing Theory

3

9. SS09 2225

_{Peranc. Kualitas}

Quality design

3

10. SS09 2226

_{Analisis Realibilitas}

Reliability Analysis

3

11. SS09 2233

_{Analisis Bayesian}

Bayesian Analysis

3

12. SS09 2234

Neural Network

3

13. SS09 2235

Data Mining

3

14. SS09 2243

Statistik Ofisial

Official Statistics

3

15. SS09 2244

Ekonometrika

Econometrics

3

16. SS09 2245

Aktuaria

Actuaria

(4)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

4

SEMESTER III

1 SS09 2302 Analisis Data Data Analysis

3

MATA KULIAH PILIHAN SEMESTER III/ Optional Subjects/Course Semester III

1. SS09 2216 Statistik Spasial Spatial Statistics

3

2. SS09 2217 Reg. Nonparametrik Nonparametric regression

3

3. SS09 2218 An. Deret Waktu Time Series Analysis

3

4. SS09 2219 Proses Stokastik Stocastic Process

3

5. SS09 2225 Peranc. Kualitas Quality design

3

6. SS09 2226 Analisis Realibilitas Reliability Analysis

3

7. SS09 2233 Analisis Bayesian Analisis Bayesian

3

8. SS09 2234 Neural Network Neural Network

3

9. SS09 2235 Data Mining Data Mining

3

10.

SS09 2236 Stat. Komp. Intensif

Intensive Computational statistics

3

11. SS09 2244 Ekonometrika Econometrics

3

12. SS09 2245 Aktuaria Actuaria

(5)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

5 SILABUS KURIKULUM/

COURSE SYLLABUS

MATA KULIAH/ COURSE TITLE

SS09 2301: Teori Probabilitas

SS09 2301: Probability Theory

Credits: tiga/three

Semester: I

TUJUAN

PEMBELAJARAN/ LEARNING OBJECTIVES

Memahami konsep percobaan random, variabel random, ruang probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel random, model-model probabilitas, hukum bilangan besar, teorema limit pusat dan fungsi variabel random

[Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable]

KOMPETENSI/ COMPETENCY

x Memahami konsep percobaan random, variabel random, ruang

probabilitas, fungsi distribusi, ekspektasi, konvergensi variabel random, model-model probabilitas, hukum bilangan besar, teorema limit pusat dan fungsi variabel random

x [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable]

POKOK BAHASAN/ SUBJECTS

x Variabel random, ruang probabilitas, fungsi distribusi, ekspektasi

dan momen, konvergensi variabel random, fungsi karakteristik, distribusi bersyarat dan kebebasan stokastik, hukum bilangan besar, distribusi khusus, distribusi fungsi variabel random, distribusi limit. Pengantar teori peluang. Transformasi variabel random dan statistik berurut. Fungsi pembangkit momen x [Random variable, probability space, distribution function,

expectation and moment, convergence of random variables, characteristic function, conditional distribution and stochastic independence, Law of Large Numbers, special distribution, distribution of random variable function, limit distribution. Introduction to probability theory. Transformation of random variables and order statistics. Moment generating function]

PUSTAKA UTAMA/ REFERENCES

1. Bartoszynski, R., 1996, Probability and Statistical Inference, John

Wiley & Sons, New York.

2. Bhat, B.R., 1981, Modern Probability Theory, John Wiley & Sons,

New York.

3. Hogg, R.V. and Tanis, E.A., 1993, Probability and Statistical

(6)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

6

MATA KULIAH/

COURSE TITLE

SS09 2302: Statistik Inferensi

SS09 2302: Inference Statistics

Semester: II

TUJUAN

Mampu memahami konsep penaksiran, metode penentuan penaksir, sifat-sifat penaksir, fungsi kerugian dan resiko, statistik kecukupan.

Keluarga eksponensial, ketidakbiasan, equivariance, uniformly most

powerfull test, ketidakbiasan untuk uji hipotesis, hipotesis linier [Able to understand concept of estimation, methods of finding estimators, properties of estimators, loss and risk function, sufficiency. Exponential family, Unbiasedness, equivariance, uniformly most powerfull test, unbiasedness for hypothesis test, linier hypothesis]

x Mampu memahami konsep penaksiran, metode penentuan

penaksir, sifat-sifat penaksir, fungsi kerugian dan resiko, statistik kecukupan. Keluarga eksponensial, ketidakbiasan, equivariance,

uniformly most powerfull test, ketidakbiasan untuk uji hipotesis, hipotesis linier

x [Able to understand concept of estimation, methods of finding estimators, properties of estimators, loss and risk function, sufficiency. Exponential family, Unbiasedness, equivariance, uniformly most powerfull test, unbiasedness for hypothesis test, linier hypothesis]

x Penaksiran, meliputi penaksiran titik, penaksiran interval.

Statistik kecukupan, ketakbiasan, penaksir efisien, penguji hipotesis. UMPT. Uji hipotesis pada sampling distribusi normal. Uji Chi-square, hipotesis linear, dan hipotesis multivariate linier x Estimation, covers point estimation, interval estimation.

Sufficiency, unbiasedness, efficient estimators, hypothesis testing, UMPT, hypothesis testing of sampling normal distribution, Chi-square test, linier hypothesis, and linier multivariate hypothesis

1. Bartoszynski, R., 1996, Probability and Statistical Inference, John

2. Hogg, R.V. and Tanis, E.A., 1993, Probability and Statistical

Inference; Macmillan Publishing Co., New York.

3. Lehman, E.L. 1983, Theory of Point Estimation, John Wiley & Sons:

New York.

(7)

Kurikulum/

SS09 2303: Analisis Data

SS09 2303: Data Analysis

Semester: III

TUJUAN

Mampu memahami penggunaan paket program Statistik, khususnya MINITAB, SPSS, SAS, dan R, untuk menyelesaikan permasalahan real, yaitu problem tentang pemodelan regresi, analisis multivariat, analisis data kualitatif, regresi nonparametrik, analisis time series, dan metode resampling. Mampu membuat suatu laporan ilmiah hasil analisis suatu permasalahan real

[Able to understand usage of statistical program packages, especially MINITAB, SPSS, SAS, and R, for solving real problem, which are problems on regression model, multivariate analysis, qualitative data analysis, nonparametric regression, time series analysis, and

resampling method. Able to produce a scientific report based on a real problemsalahan real]

x Mampu memahami penggunaan paket program Statistik,

khususnya MINITAB, SPSS, SAS, dan R, untuk menyelesaikan permasalahan real, yaitu problem tentang pemodelan regresi, analisis multivariat, analisis data kualitatif, regresi nonparametrik, analisis time series, dan metode resampling. Mampu membuat suatu laporan ilmiah hasil analisis suatu permasalahan real

x [Able to understand usage of statistical program packages, especially MINITAB, SPSS, SAS, and R, for solving real problem, which are problems on regression model, multivariate analysis, qualitative data analysis, nonparametric regression, time series analysis, and resampling method. Able to produce a scientific report based on a real problemsalahan real]

x Bahasa pemrograman paket program statistika, yang meliputi

telaah terhadap program-program komputer (khususnya MINITAB, SPSS, SAS, dan R) dan penerapan model-model statistika. Studi kasus real dengan penerapan beberapa metode statistik lanjut,

yaitu analisis mulivariate, analisis data kualitatif, Generalized

Linear Models, regresi nonparametrik, regresi nonlinear (uji nonlinearitas), analisis deret waktu, nonlinear time series, dan resampling methods

(8)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

8

on computer programs (especially MINITAB, SPSS, SAS, and R) and statistical models application. Study of real cases using some advance statistics method application, which are multivariate analysis, qualitative data analysis, Generalized linier models, nonparametric regression, nonlinier regression (nonlinier test), time series analysis, nonlinier time series and resampling method

1. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (2006)

“Multivariate Data Analysis”, 6th edition, Prentice Hall International: UK.

2. Sharma, S. (1996). “Applied Multivariate Techniques”, New-York:

John Wiley & Sons, Inc.

3. Johnson, N. and Wichern, D. (1998). “Applied Multivariate

Statistical Analysis”, Prentice-Hall, Englewood Cliffs, N.J

4. McCullagh P. and Nelder, J.A. (1989) Generalized Linear Models.

London: Chapman and Hall.

5. Hosmer, D.W. and Lemeshow, S. (2000). Applied Logistic

Regression. 2nd Edition, New-York: John Wiley & Sons.

6. Wand, M. P. and Joes, M. C. (1995). Kernel Smoothing. Chapman

and Hall, London .

7. Heckman, N. and Ramsay, J. O. (1996). Spline smoothing with

model based penalties. McGill University, unpublished manuscript.

8. Shumway, R.H. and Stoffer, D.S. (2006). Time Series Analysis and

Its Applications with R Examples. Second edition, Springer: New York, USA.

9. Wei, W.W.S. (2006). Time Series Analysis: Univariate and

Multivariate Methods. Second edition, Addison-Wesley Publishing Co., USA.

10.Box, G.E.P, Jenkins, G.E., and Reinsel, H. (1994). Time Series

Analysis.

11.Ripley, B. D. (1996) Pattern Recognition and Neural Networks.

Cambridge.

12.Tong, H. (1994). Nonlinear Time Series. John Wiley & Sons.

13.Manual SAS, SPSS, MINITAB, dan R.

14.Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics

with S. Fourth edition. Springer

SS09 2304: Tesis

SS09 2304: Thesis

Credits: enam/six

(9)

Kurikulum/

Mampu menyelesaikan persoalan Statistika Industri, Bisnis-Sosial, atau Komputasi, dengan metode statistika terbaru dan membandingkan dengan metode sebelumnya, serta dapat mempublikasikan hasil kajiannya dalam suatu forum ilmiah nasional atau internasional (seminar dan/atau jurnal)

[Able to solve statistical problems on industry, social-business, or computation, using the newest statistical method and compare it with the previous method, and also can publish the study result in a national or international scientific forum (seminar and/or journal)

x Mampu menyelesaikan persoalan Statistika Industri, Bisnis-Sosial,

atau Komputasi, dengan metode statistika terbaru dan membandingkan dengan metode sebelumnya, serta dapat mempublikasikan hasil kajiannya dalam suatu forum ilmiah nasional atau internasional (seminar dan/atau jurnal)

x [Able to solve statistical problems on industry, social-business, or computation, using the newest statistical method and compare it with the previous method, and also can publish the study result in a national or international scientific forum (seminar and/or journal)]

x Kegiatan penelitian mandiri dimulai dari pembuatan proposal

penelitian, seminar proposal dan pelaksanaan penelitian. Hasil penelitian harus diseminarkan dan dipertanggungjawabkan dihadapan penguji dalam ujian tesis, serta dipublikasikan dalam suatu forum ilmiah nasional atau internasional (seminar dan/atau jurnal)

x [Independent research activities starting from producing a research proposal, proposal seminar, and research

implementation. Result of the research should be presented in a seminar and can be accountabled in front of examiners during thesis examination, also should be published in a national or international scientific forum (seminar and/or journal)] PUSTAKA

UTAMA/ REFERENCES

Manual of how to write proposal, thesis and dissertation report based on quality standard of PPS-ITS

SS09 2211: Analisis Statistika

SS09 2211: Statistical Analysis

(10)

Kurikulum/

Mampu memahami teori dan metode statistika dasar. Mampu menganalisis hasil metode statisika dasar, dan memberikan interpretasi hasil suatu analisis data dengan metode statistika dasar

[Able to understand theory and method of basic statistics. Able to analyse result of basic statistical method and give interpretation of the result of data analysis using basic statistical method]

x Mampu memahami teori dan metode statistika dasar. Mampu

menganalisis hasil metode statisika dasar, dan memberikan interpretasi hasil suatu analisis data dengan metode statistika dasar

x [Able to understand theory and method of basic statistics. Able to analyse result of basic statistical method and give interpretation of the result of data analysis using basic statistical method]

x Pengantar Probabilitas. Estimasi parameter, meliputi estimasi titik

dan interval. Uji hipotesis tentang rata-rata, proporsi, dan varians pada satu dan dua populasi. Analisis korelasi, regresi sederhana dan berganda. Uji independensi dan analisis nonparametrik dasar x Introduction to probability. Parameter estimation, covers point and

interval estimation. Hypothesis testing of mean, proportion, and varians of one and two populations. Correlation analysis, simple regression and multiregression. Independent test and basic nonparametric analysis

1. Dowdy, S., Weardon, S., and Chilko, D., 2004, Statistics for

Research, 3rd Edition, John Wiley & Sons: New York.

2. Lefebvre, M., 2006, Applied Probability and Statistics, Springer

Verlag: New York.

SS09 2212: Desain Eksperimen

SS09 2212: Design of Experiment

Semester: I

TUJUAN

Memahami berbagai konsep rancangan percobaan, yang meliputi faktorial design, nested design, fraksional faktorial design, split-plot design, confounding, blok tak lengkap, analisis kovariansi, dan metode Taguchi

(11)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

11

Taguchi method]

x Memahami berbagai konsep rancangan percobaan, yang meliputi

faktorial design, nested design, fraksional faktorial design, split-plot design, confounding, blok tak lengkap, analisis kovariansi, dan metode Taguchi

x [Understanding various concepts of experiment design, which covers factorial design, nested design, fractional factorial design, split-plot design, confounding, incomplete blocks, covariance analysis, and Taguchi method]

x Konsep dasar perancangan percobaan, justifikasi model linier,

pengacakan, pengelompokan dan penggunaan pengamatan

penyerta. Pembahasan mengenai Faktorial design, Nested design,

Fraksional faktorial design, rancangan petak terbagi (split-splot

design), pembauran (confounding), analisis kovarians, dan metode Taguchi

x Basic concept of experiment design, linier model justification, randomization, clustering and penyerta observation usage. Discussion on factorial design, Nested design, Fractional factorial design, split-splot design, confounding, covarians analysis, and Taguchi method

1. Hinkelmann, K. and Kemptkarne, O., 1994, Design and Analysis of

Experiments, John Wiley & Sons, New York.

2. Bagchi, T., 1994, Taguchi Methods Explained Practical Steps to

Robust Design, John Wiley & Sons, New York.

3. Montgomery, D.C., 1997, Design and Analysis of Experiment, John

4. Gardiner, W.P. Gettinby, 1998, Experimental Design Techniques

in Statistical Practice : A Practical Software-base approach, Horwood Publishing Limited.

SS09 2213: Model Linier

SS09 2213: Linear Model

Semester: I

TUJUAN

PEMBELAJARAN/ LEARNING

Mengerti dan memahami bentuk-bentuk sebaran kuadratik, model dasar, penggolongan silang, dwi arah, komponen ragam. Mampu mengem-bangkan model-model linier untuk regresi, baik dengan rank

(12)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

12

OBJECTIVES [Understanding various distributions of quadratic forms, basic model,

cross classification, two way, component style. Able to develop linier models for regression, both by full rank or not of full rank]

x Mengerti dan memahami bentuk-bentuk sebaran kuadratik, model

dasar, penggolongan silang, dwi arah, komponen ragam. Mampu mengem-bangkan model-model linier untuk regresi, baik dengan

rank penuh ataupun tidak

x [Understanding various distributions of quadratic forms, basic model, cross classification, two way, component style. Able to develop linier models for regression, both by full rank or not of full rank]

x Pendugaan dan pengujian hipotesis beberapa model linear. Model

klasifikasi satu-arah dan dwi-arah. Perluasan model-model sel rataan. Model dengan peubah penyerta. Model pengaruh-pengaruh campuran dan pendugaan komponen ragam, serta fungsi estimabel

x Estimation and hypothesis test for some linier models. One way and two way classification models. Extension of means cell models. Models with dependent variables. Mixed influence models and variance component estimation and also estimabel functions

1. Bowerman, B.L. and R.T. O’Connel, 1990, Linear Statistical Models

an Applied Approach, PWS-KENT Publication Company, Boston.

2. Hocking, R.R., 1996, Methods and Applications of Linear Models

Regression and analysis of Variance, John Willey & Sons Inc., New York.

3. Rao, C.R., 1973, Linear Statistical Inference and Its Applications,

2nd Edition, Eastern Private Limited, New Delhi.

4. Searle, S.R., 1987, Linear Models for Unbalanced data, John Wiley

& Sons Inc., New York.

5. Myers, R.H. and Milton, J.S., 1991, A First Subjects/Course in the

Theory of Linear Statistical Models, PWS-KENT Publication Company, Boston.

SS09 2214: Analisis Multivariat

SS09 2214: Multivariate Analysis

Semester: II

(13)

Kurikulum/

multivariat, analisis eksplorasi, pereduksi dimensi, pengujian hipotesis data multivariat, metode multisampel dan analisis diskriminan

[Able to differentiate and interpret univariate data, multivariate data, exploration analysis, reduction dimension techniques, test of hypothesis of multivariate data, multisample method, and discriminant analysis]

x Mampu membedakan dan menginterpretasikan data univariat,

data multivariat, analisis eksplorasi, pereduksi dimensi, pengujian hipotesis data multivariat, metode multisampel dan analisis diskriminan

x [Able to differentiate and interpret univariate data, multivariate data, exploration analysis, reduction dimension techniques, test of hypothesis of multivariate data, multisample method, and discriminant analysis]

x Review tentang aljabar linier, dan fungsi distribusi multivariat,

yaitu distribusi Multinormal, Wishart, dan T2 Hotelling. Analisis

eksplorasi yang meliputi Biplot, analisis korespondensi, PCA, analisis faktor, analisis cluster, multidimensional scaling dan analisis konjoin. Analisis konfirmasi, terdiri atas pengujian satu mean dan taksiran interval, serta pengujian dua mean dan taksiran interval. MANOVA, meliputi one-way, two-way, dan faktorial diskriminan linier

x Reviewing linier algebra, and function of multivariate distributions which are Multinormal, Wishart, and T2 Hotelling. Exploration analysis which covers Biplot, corespondence analysis, PCA, factor analysis, multidimensional scaling and conjoint analysis. Confirmatory analysis, consists of one mean test and interval estimation. MANOVA, consist of one-way, two-way, and linier discriminant factorial

1. Timm, N.H., 2002, Applied Multivariate Analysis, Springer-Verlag:

New York.

2. Rencher, A.C., 2002, Method of Multivariate Analysis, John Wiley

& Sons : Canada.

3. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C., 2006,

Multivariate Data Analysis, 6th edition, Prentice Hall International: UK.

4. Sharma, S., 1996, Applied Multivariate Techniques, New-York:

John Wiley & Sons, Inc.

5. Dillon, W.K. and Matthew, G., 1984, Multivariate Analysis,

(14)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

14

MATA KULIAH/

COURSE TITLE

SS09 2215: Analisis Data Kualitatif

SS09 2215: Qualitative Data Analysis

Semester: II

TUJUAN

Memahami inferensi dalam tabel kontingensi 2x2, L2x2, rxk, Lrxk, rxkxl, model Log linier tabel rxk, rxkxl yang berkategori, model logistik regresi, dan model logistik regresi dengan strata

[Understanding inference in contingency tables 2x2, L2x2, rxk, Lrxk, rxkxl, Log linier model, rxk, rxkxl category tables, logistic regression model, and logistic regression model with stratum]

x Memahami inferensi dalam tabel kontingensi 2x2, L2x2, rxk, Lrxk,

rxkxl, model Log linier tabel rxk, rxkxl yang berkategori, model logistik regresi, dan model logistik regresi dengan strata

x [Understanding inference in contingency tables 2x2, L2x2, rxk, Lrxk, rxkxl, Log linier model, rxk, rxkxl category tables, logistic

regression model, and logistic regression model with stratum]

x Metode-metode analisis tabel kontingensi berdimensi banyak.

Metode jumlah kuadrat tertimbang, model log-linier dan pendekatan regresi logistik untuk analisis data kategori.

Pendugaan parameter dan besaran asosiasi, pemilihan model, dan pengujian kesesuaian model. Penerapan praktis untuk

penyelesaian permasalahan real dengan penggunaan paket komputer statistik, khususnya SPSS dan R

x Some methods of multidimension contingency tables. Weighted sum square method, log-linier model and logistic regression approach for categoric data analysis. Parameter estimation and association value, model selection, and fitting model test. Practical application for solving real problem using statistical computer package, especially SPSS and R

1. Agresti, A., 2002, Categorical Data Analysis, 2nd Edition, John

Wiley & Sons: New York.

2. Bishop, Y.M.M., Fienberg, S.E. and Holland, P.W., 2007, Discrete

Multivariate Analysis: Theory and Practice, Springer: New York.

3. Greenacre, M.J., 1984, Theory and Applications of Correspondence

(15)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

15

MATA KULIAH/

COURSE TITLE

SS09 2216

:

Statistik Spasial

SS09 2216

:

Spatial Statistics

Credits: tiga/

three

Semester: III

TUJUAN

Memahami konsep dasar data spasial, struktur data

spasial, pendugaan dan pemodelan korelasi spasial,

prediksi dan interpolasi, mapping pola, regresi spasial dan

pemodelan spatio-temporal

[

Understanding the basical concept of spatial data, spatial

data structure, estimating and modelling spatial

correlation, prediction and interpolation, pattern

mapping,spatial regression, Spatial-temporal modelling

.]

x

Memahami konsep percobaan random, variabel

random, ruang probabilitas, fungsi distribusi,

ekspektasi, konvergensi variabel random,

model-model probabilitas, hukum bilangan besar, teorema

limit pusat dan fungsi variabel random

x [Understanding concept of random experiment, random variable, probability space, distribution function, expectation, convergence of random variables, probability models, Law of Large Numbers, Central Limit Theorem, and function of random variable.]

Pengertian statistik spasial, Struktur data spasial (titik,

area (lattices), dan spasial), isotropic dan stasioner.

Pendugaan dan pemodelan korelasi spasial (estimasi

variogram, MLE, fitting parametric models). Prediksi dan

interpolasi (ordinary kriging, cokriging). Mapping pola

titik, Regresi spasial (SAR, GWR) dan neighborhood

analysis. Pemodelan spatio-temporal.

(16)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

16 and modelling spatial correlation (variogram estimation,

MLE, fitting parametric models). Prediction and

interpolation (ordinary kriging, cokriging). Point pattern

Mapping, Spatial Regression (SAR, GWR) and

neighborhood analysis. Spatio-temporal modelling.

]

1. Cressie, N., 1993,

Statistics for Spatial Data

, John

Wiley & Sons.

2. Wackernagel, H., 1995,

Multivariate Geostatistics, An

Introduction with Applications

, Springer-Verlag.

3. Sandra L.A., 1996,

Practical handbook of Spatial

Statistics

. CRC Press. Inc. USA. Isaaks, E.H. and

Srivastava, R.H., 1989,

Applied Geostatistics

, Oxford

University Press.

4. Isaaks, E.H. and Srivastava, R.H., 1989,

Applied

Geostatistics

, Oxford University Press

SS09 2217

:

Regresi Nonparametrik

SS09 2217

:

Nonparametrics Regression

Credits: tiga/

three

Semester: III

TUJUAN

Mengetahui beberapa model regresi nonparametrik,

khususnya peran dan sifat-sifatnya. Dapat memodelkan

perilaku data berdasarkan pendekatan regresi

nonparametrik.

[

To know and understand various nonparametric

regression models, especially the uses and its

characteristics. Capable to modelling data behaviours

based on nonparametric regression approach

.]

(17)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

17

COMPETENCY

_{ekspektasi, konvergensi variabel random,}

model-model probabilitas, hukum bilangan besar, teorema

limit pusat dan fungsi variabel random

x

Konsep dasar regresi nonparametrik dan perbedaan

dengan regresi parametrik. Estimasi densitas dengan

pendekatan histogram dan kernel. Estimasi kurva

regresi nonparametrik dengan pendekatan kernel,

deret ortogonal, spline, deret Fourier dan Wavelets.

Pemilihan bandwith dalam regresi kernel, dan knot

pada regresi spine.

x [

Basical concept of nonparametric regression and the

differences betwen nonparametric and parmetric

regression. Density estimation problems with

histogram and kernel approach, orthogonal series,

spline, Fourier series and wavelets. Bandwich

selection in kernel regression, knot in spline

regression

]

1. Enbank, R.L., 1988,

Spline Smoothing and

Nonparametric Regression

, Marcel Dekker Ins, New

York.

2. Green, P.J. and Silverman, B.W., 1994,

Nonparametric Regression and Generalized Linear

Models

, Chapman and Hall, London.

3.

Hardle, W., 1990,

Applied Nonparametric

Regression

, Cambridge University Press, New York.

4.

Hardle, W., 1991,

Smoothing Techniques With

Implementation in S

, Spinger Verlag, New York.

5.

Prenter, P.M., 1975,

Spline and Variational Methods

,

John Wiley and Sons, New York

_.

6.

Schumaker, L.L., 1981,

Spline Functions: Basic Theory

,

John Wiley and sons, new York.

(18)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

18 SIAM: Philadelpia.

8. Wahka, G., 1990,

Spline Models for Observational

Data

, SIAM: Pensylvania.

SS09 2217

:

Analisis Deret Waktu

SS09 2217

:

Time Series Analysis

Credits: tiga/

three

Semester: III

TUJUAN

Memahami konsep-konsep statistika dalam model time

series univariat (ARIMA), time series multivariat (Model

Intervensi, Fungsi Transfer, dan VARIMA), dan Nonlinear

time series. Dapat memodelkan time series univariat,

multivariat, dan nonlinear time series.

[

To understand the statistical concepts used in univariate

time series models (ARIMA), Multivariate time series

models (Intervention models, Transfer funtion and

VARIMA), non linear time series. Able to model univariate,

multivariate and nonlinear time series

.]

x

Memahami konsep percobaan random, variabel

random, ruang probabilitas, fungsi distribusi,

ekspektasi, konvergensi variabel random,

model-model probabilitas, hukum bilangan besar, teorema

limit pusat dan fungsi variabel random

x

Konsep proses stasioner, autokorelasi dan

autokorelasi parsial. Regresi dengan error

(19)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

19 deteksi outlier. Fungsi transfer input tunggal dan input

ganda. Model GARCH, VARIMA, dan model time series

nonlinear.

x [

Stationarity concept, autocorrelation and partial

autocorrelation, Regression with autocrrelated error

(time series regression), ARMA, ARIMA, and Seasonal

ARIMA. Intervantion Model and otlier detection.

Single and Multiple input transfer function. GARCH,

VARIMA and nonlinear time series models.

]

1.

Brockwell, P.J. and Davis, R.A., 1991

, Time Series:

Theory and Methods

, 2nd Edition, Springer-Verlag:

New York.

2.

Box, G.E.P., Jenkins, G.M., and Reinsel, D., 1994,

Time

Series Analysis : Forecasting and Control

; 2nd Edition,

Holden Day: San Fransisco

.

3.

Christensen, R., 1991,

Linear Models for Multivariate,

Time Series and Spatial Data

, Springer-Verlag, New

York.

4. Priestley, M.B., 1981,

Spectral Analysis and Time

Series

, Academic Press: London.

SS09 2219

:

Proses Stokastik

SS09 2219

:

Stochastics Process

Credits: tiga/

three

Semester: III

TUJUAN

Memahami konsep-konsep probabilitas yang banyak

digunakan dalam proses stokastik, rantai markov, proses

output, perbedaan proses renewal dengan

input-output, dan Brownian motion.

(20)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

20 process difference with input-output, Brownian motion

.]

x

Memahami konsep percobaan random, variabel

random, ruang probabilitas, fungsi distribusi,

ekspektasi, konvergensi variabel random,

model-model probabilitas, hukum bilangan besar, teorema

limit pusat dan fungsi variabel random

x

Review probabilitas bersyarat dan hukum probabilitas

total. Klasifikasi proses stokastik, rantai Markov,

probabilitas transisi, klasifikasi ruang keadaan, dan

distribusi seimbang. Proses Poisson, sifat-sifat proses

Poisson, dan proses Poisson nonhomogen. Proses

input-output (birth-death processes), proses renewal,

martingales, random walk, Browman motion, proses

difusi, dan penerapannya.

x [

Reviewing conditional probability and total

probability law. Classification of stochastic process.

Markov chain,transition probability, classification of

condition space and balanced distribution. Poisson

process and its properties. Non homogeny Poisson

process. Input-Output process (birth-death processes),

renewal process, martingales, random walk,

Browman motion, diffusion process ant its

application.

]

1.

Heyman, D.D. and Sobel, M.J., 1996,

Stochastic

Models in Operations Research

, Vol. 1, McGrraw-Hill,

New York

_.

2.

Kulkarni, V.G., 1998,

Modeling, Analysis, Design, and

Control of Stochastic System,

Springer

.

3.

Lawler, G.F., 2006,

Introduction to Stochastic Process,

Chapman and Hall.

(21)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

21 Discrete Stochastic Dynamic Programming,

John

Wiley & Sons.

5.

Ross, S.N., 1996,

Stochastic Processes

, John Wiley &

Sons, New York.

6.

Rolsky, T., Schmidt, H., Schmidt, V., and Tengels, J.,

1999,

Stochastic Process for Insurance and Finance,

John Wiley & Sons

_.

7.

Lyuer, Y.D., 2002,

Financial Engineering and

Computation,

Cambridge Univ. Press.

SS09 2221

:

Riset Operasi

SS09 2221

:

Operation Research

Credits: tiga/

three

Semester: I

TUJUAN

Memahami berbagai metode kuantitatif dalam riset

operasi dan memiliki ketrampilan menerapkannya dalam

dunia praktis.

[

To understand miscelanous Quantitative methodsin

Operation research and have an ability to apply it on

pratice

.].

x

Memahami konsep percobaan random, variabel

random, ruang probabilitas, fungsi distribusi,

ekspektasi, konvergensi variabel random,

model-model probabilitas, hukum bilangan besar, teorema

limit pusat dan fungsi variabel random

POKOK BAHASAN/

(22)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

22

SUBJECTS

_{ekonomik. Program bilangan bulat: teknik}

penca-bangan dan pembatasan, program bilangan campuran,

program bilangan biner. Program dinamik:

deterministik, probabilistik. Goal programing: single

dan multiple. Teori permainan: strategi murni,

campuran. Sistem antrian: antrian non poisson,

antrian dengan disiplin prioritas, antrian dua phase.

Program Nonlinear.

x [

Network Analysis, Coverage of operation Research,

Linear programming: problems formulation, Prime

Simlex Methods, Dual, Revition, Pascal Opyimum

analysis, sensitivity, economic’s interpretation.

Integer Number’s program: Boundary and Branchery

problems, mixed numbers program, binary numbers

program. Dynamic program: deterministic and

probabilistic. Goal programing: single dan multiple.

Game Theory: Pure Strategy, mixed. Queueing

System: Non Poisson Queueing, Queueing with

Disipline Priority, Two Phase queuein. Nonlinar

programming.

]

8.

Hiller, F. and Lieberman, G.J., 1990,

Introduction to

Operation Research

, 5

th

edition, McGrraw-Hill, New

York

_.

9.

Taha,

H.A.,

1973,

Operation Research: An

Introduction,

Prentice Hall

.

SS09 2223

:

Statistik Proses Kontrol

SS09 2223

:

Statistical Process Control

Credits: tiga/

three

Semester: II

TUJUAN

PEMBELAJARAN/ LEARNING

(23)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

23

OBJECTIVES

_{dalam pengontrolan proses.}

[

Capable to control multivariate process and have

potential capability to develop a new methods on

controlling process

.].

x

Memahami konsep percobaan random, variabel

random, ruang probabilitas, fungsi distribusi,

ekspektasi, konvergensi variabel random,

model-model probabilitas, hukum bilangan besar, teorema

limit pusat dan fungsi variabel random

x

Pengantar

Statistical Process Control

. Diagram kontrol

sederhana untuk atribut dan variabel. Diagram

kontrol multivariat untuk atribut, target, dan

variabilitas. Indeks kemampuan proses: univariat dan

multivariat. Diagram kontrol lain: CuSum, EWMA,

Multiple Stream,

Short Run

, MCuSum, MEWMA,

Systematic Pattern

.

x [

Introduction to Statistical Process Control. Simple

control chart for atributes and variables. Multivariate

control chart for attributes, targets and variability.

Proces capacity Index: univariate and multivariate.

Ohers control charts suh as: CuSum, EWMA , Multiple

Stream, Short Run, MCuSum, MEWMA, Systematic

Pattern.

]

1.

Montgomery, D.C., 2005,

Introduction to Statistical

Quality Control

5

ed

, John Wiley and Sons, USA

.

2.

Fuch, C., Kennet, S.R., 1998,

Multivariate Quality

Control, Theory and Application

, Marcel Dekker Inc.,

New York

3.

Lenz, H.J., Wilrich, P.T., 2004,

Frontier in Statistical

(24)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

24

4.

Keats, J.B., Hubele, N.F., 1989,

Statistical Process

Control in Automated Manufacturing

, Marcel Dekker

Inc., New York

.

5.

Quesenberry, C.P., 1997,

SPC Methods For Quality

Improvement

, John Wiley and Sons, USA

_.

6.

Journal of Quality Technology, Journal of Quality

Engineering, Tecnometrics.

SS09 2224

:

Teori Antrian

SS09 2224

:

Queueing Theory

Credits: tiga/

three

Semester: II

TUJUAN

Memahami konsep proses Markov dan kaitannya dengan

teori antrian, sistem antrian, sistem antrian Markov,

sistem antrian Semi Markov, sistem antrian jaringan

terbuka, sistem antrian jaringan tertutup, dan Markov

Modulated Arrival Process.

[

To understand the concept of Markov’s process and its

relationship with Queueing Theory , Queueing System,

Markov’s queueing system, Semi markov’s queueing

system, Opened network’s queueing system, closed

network’s queueing system, Markov Modulated Arrival

Process

.]

x

Memahami konsep percobaan random, variabel

random, ruang probabilitas, fungsi distribusi,

ekspektasi, konvergensi variabel random,

model-model probabilitas, hukum bilangan besar, teorema

limit pusat dan fungsi variabel random

(25)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

25

Central Limit Theorem, and function of random variable.]

x

Review proses Markov diskrit dan kontinyu. Momen

klaster sistem antrian, notasi Kendall, teorema little,

traffic intensity, dan hukum aliran konservasi. Sistem

antrian Markov jalur tunggal dan ganda. Sistem

antrian semi-Markov. Sistem antrian dengan

prioritas. Sistem antrian M/G/I, dan G/M/I. Sistem

antrian jaringan terbuka, teorema Burke, antrian

jaringan Jackson, antrian jaringan tertutup, algoritma

konvalensi, mean value analysis, Markov-modulated

Poisson Process, Markov-modulated Bernoulli

Process, dan Markov-modulated Fluid Flow

x [

Reviewing continue and discrete, Clustered moment

of queing system, Kendall’s notation, Little Theorem,

Traffic intensity, Law of conservation flow. Single and

Multiple tracks of Markov Queueing system. Semi

markov queueing system. Queueing system with

priority. M/G/I and G/M/I queueing system . Opened

network’s queueing system, Burke’s theorem.

Jackson’s network’s queueing system. closed

network’s queueing system. Convalention

Algorithmmean value analysis, Markov-modulated

Poisson Process, Markov-modulated Bernoulli

Process, dan Markov-modulated Fluid Flow.

]

7.

Breuer, L. And Baum, D., 2005,

An Introduction to

Queueing Theory and Matrix-Analytic Methods,

Springer: Netherlands.

8.

Tijms, H.C., 2003,

A First Subjects/Course in

Stochastic Models

, John Wiley & Sons: England.

SS09 2225

:

Perancangan Kualitas

SS09 2225

:

Quality Design

Credits: tiga/

three

(26)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

26

TUJUAN

Mampu mendesain kualitas yang kokoh dan

mengoptimumkan respon.

[

Orthogonal Arrays, Loss function, S/N ratio optimization

for static and dynamic quality characteristic, optimization

of single and multiple respons

.]

x

Memahami konsep percobaan random, variabel

random, ruang probabilitas, fungsi distribusi,

ekspektasi, konvergensi variabel random,

model-model probabilitas, hukum bilangan besar, teorema

limit pusat dan fungsi variabel random

x

Orthogonal

Arrays

,

Loss function

,

Optimasi S/N

ratio

untuk karakteristik kualitas yang statis dan dinamis

,

Optimasi respon tunggal dan ganda

x [

Orthogonal Arrays, Loss function, S/N ratio

optimization for static and dynamic quality

characteristic, optimization of single and multiple

respons.

]

1.

Park, S.H., 1996,

Robust Design and analysis for

Quality Engineering

, Chapman Hall.

2.

Peace, G.S., 1993,

Taguchi Methods

, Addison Wesley.

SS09 2226

:

Analisis Realibilitas

SS09 2226

:

Reliability Analysis

Credits: tiga/

three

Semester: III

TUJUAN

PEMBELAJARAN/

(27)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

27

LEARNING

OBJECTIVES

digunakan dalam analisis reliabilitas, distribusi

probabilitas dalam analisis reliabilitas, model regresi

untuk data reliabilitas, proportional Hazard Model, dan

model Bayes.

[

To understand statistical concepts that have been used in

reliability analysis, pobability density in reliability analysis,

regression models for reability data, proportional Hazard

Model, and Bayes Models

.]

x

Memahami konsep percobaan random, variabel

random, ruang probabilitas, fungsi distribusi,

ekspektasi, konvergensi variabel random,

model-model probabilitas, hukum bilangan besar, teorema

limit pusat dan fungsi variabel random

x

Konsep laju kerusakan dan reliabilitas. Model

eksponensial, gamma, weibull, normal, log normal,

nilai ekstrim, dan model gabungan. Penaksiran

parameter dan fungsi reliabilitas untuk sampel

lengkap dan tersensor. Uji hipotesis, plot q-q,

reliabilitas sistem pendekatan proses Markov, dan

availiabilitas. Model regresi parametrik dan non

parametrik, model multivariate dan stokastik, serta

metode Bayes.

(28)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

28

PUSTAKA

UTAMA/ REFERENCES

1.

Gertzbalck, I.B., 1989,

Statistical Reliability Theory

,

Marcell Decker, New York.

2.

Lawless, J.F., 1982,

Statistical Models and Methods

for Life Time Data

, John Wiley & Sons: New York.

3.

Sinha, S.K. and Kale, B.K., 1980,

Life Testing and

Reliability Estimation

, Wiley Eastern LTD: New Delhi.

SS09 2231

:

Teknik Simulasi

SS09 2231

:

Simulation Techniques

Credits: tiga/

three

Semester: I

TUJUAN

Mampu membangun algoritma pembangkit data statistik

yang berdistribusi univariat maupun multivariat dan

model statistika secara simulasi stokastik. Mampu

menggunakan simulasi stokastik untuk estimasi densitas

maupun model statistika.

[

Capable to construct the algorithm of generating

statistical data which heve univariate or multivariate

distribution and statistical models using stocasticcaly

simulation. Capable to use stocastic simulation to

estimate the density or statistical models

.]

x

Memahami konsep percobaan random, variabel

random, ruang probabilitas, fungsi distribusi,

ekspektasi, konvergensi variabel random,

model-model probabilitas, hukum bilangan besar, teorema

limit pusat dan fungsi variabel random

(29)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

29

Central Limit Theorem, and function of random variable.]

x

Pembangkit bilangan acak dan variabel acak

berdistribusi. Simulasi steady-state, Integrasi Monte

Carlo, Simulasi Markov Chain, Markov Chain Monte

Carlo (Algoritma Gibbs sampler dan

Metropolis-Hastings). Teknik reduksi varians.

x [

Random number and distributed random variable

generator. Steady-state simulation, Monte Carlo

Integration, Markov Chain Simulation, Markov Chain

Monte Carlo (Gibbs sampler algorithm dan

Metropolis-Hastings). Variance reduction technique.

]

1.

Asmussen, S. and Glynn, P.W., 2007,

Stochastic

Simulation: Algorthms and Analysis

.

2.

Law, A. And Kelton, C., 2000,

Simulation Modelling

and Analysis

, McGraw-Hill.

3.

Trivedi, K.S., 1982,

Probability and Statistics with

Reliability, Queueing and Computer Science

Application

, Addison Wesley.

SS09 2232: Metode Resampling

SS09 2232: Resampling Methods

Semester: I

TUJUAN

Mampu membangun algoritma perbanyakan data yang terbatas dengan resampling, baik data univariat maupun multivariat, serta baik secara uniform maupun secara terbobot dengan suatu densitas. [Capable to construct the algorithm of generating a finite numbers of data using resampling, univariatly or multivariatly, uniformly or weightly with a fixed density]

x Mampu membangun algoritma perbanyakan data yang terbatas

(30)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

30

x [Capable to construct the algorithm of generating a finite numbers

of data using resampling, univariatly or multivariatly, uniformly or weightly with a fixed density]

x Jacknife, Bootstrap, Generalized Bootstrap, Adaptive-Acceptance

Rejection, Iterasi Full Conditional Distribution, Algoritma Ekspektasi-Maksimisasi (EM).

x [Jacknife, Bootstrap, Generalized Bootstrap, Adaptive-Acceptance Rejection, Full Conditional Distribution Iteration, Expectation-maximisation Algorithm (EM)]

1. Efron, B. and Tibhsirani, C., 1993, Bootstrap and Jacknife Method,

John Wiley & Sons: New York.

2. Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B., 1995,

Bayesian Data Analysis, Chapman & Hall, London.

3. Tanner, M.A., 1996, Tools for Statistical Inference: Methods for

the Exploration of Posterior Distributions and Likelihood Functions, 3rd Edition, Springer-Verlag: New York.

SS09 2233: Analisis Bayesian

SS09 2233: Bayesian Analysis

Semester: II/III

TUJUAN

Mahasiswa mengerti, memahami dan menguasai teori Bayesian dan Empirical Bayes serta mampu mengaplikasikannya ke dalam permasa-lahan real.

[The student can understand and menguasai the Bayesian theory and Empirical Bayes and capable to apply in to real-life problems.]

x Mahasiswa mengerti, memahami dan menguasai teori Bayesian

dan Empirical Bayes serta mampu mengaplikasikannya ke dalam permasa-lahan real.

x [The student can understand the Bayesian theory and Empirical Bayes and capable to apply in to real-life problems]

x Teorema Bayes, Bayesian inference, Data augmentation,

Single-parameter model, Multi-Single-parameter model, Hirarchical model, Jenis prior, prior odds, posterior, posterior odds, Bayes faktor, Bayesian non-Normal dan neo-Normal model, Bayesian Reliability, Mixture densitas, Mixture regresi, Mixture of mixture, Pemilihan model terbaik dengan Bayesian, Struktur Perkalian Distribusi, MCMC.

(31)

Kurikulum/

Cu

rriculum

ITS : 2009-2014

31

faktor, Bayesian non-Normal dan neo-Normal model, Bayesian Reliability, Mixture density, Mixture regressioni, Mixture of mixture, Best model selection using bayesian, Distribution multiplicative structure , MCMC]

1. Box, G. E. P. and Tiao, G. C., 1973, Bayesian Inference in Statistical

Analysis, Reading, MA, Addison-Wesley.

2. Carlin, B. P. and Louis, T. A., 1996, Bayes and Empirical Bayes

Methods for Data Analysis, Chapman & Hall, London.

3. Casella, G. and Berger, R. L., 1990, Statistical Inference,

Duxbury, Bellmont California, USA.

4. Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B., 1995,

Bayesian Data Analysis, Chapman & Hall, London.

5. Martz, H.F. and Waller, R. A., 1982, Bayesian Reliability

Analysis, John Wiley & Sons, New York.

6. McLachlan, G. and Basford, K., 1988, Mixture models: inference

and application to clustering, Marcel and Decker Inc.

7. Tanner, M. A., 1996, Tools for Statistical Inference: Methods for

the Exploration of Posterior Distributions and Likelihood Functions, 3rd Edition, Springer-Verlag: New York.

8. Titterington, M., Makov, G., and Smith A.F.M., 1985, Statistical

analysis of finite mixtures, John Wiley & Sons, UK.

9. Zellner, A., 1971, An Introduction to Bayesian Inference in

Econometrics, Wiley, New York.

10. Software: WinBUGS 1.4, Weibull++6, MixS.

SS09 2235: Data Mining

(32)

Kurikulum/

Mampu membangun algoritma pembangkit data statistik yang ber-distribusi univariat maupun multivariat dan model statistika secara simulasi stokastik. Mampu menggunakan simulasi stokastik untuk estimasi densitas maupun model statistika.

[Capable to develop the algorithm for generating statistical data whic have uivariate and also multivariate distribution and statistical models with stocastically simulation. Capable to apply stocastic simulation to estimate the density and statsitical model]

x Mampu membangun algoritma pembangkit data statistik yang

ber-distribusi univariat maupun multivariat dan model statistika secara simulasi stokastik. Mampu menggunakan simulasi stokastik untuk estimasi densitas maupun model statistika.

x [Capable to develop the algorithm for generating statistical data whic have uivariate and also multivariate distribution and statistical models with stocastically simulation. Capable to apply stocastic simulation to estimate the density and statsitical model]

POKOK BAHASAN/ SUBJECTS

x Machine Learning dan Data Mining, Knowledge Preparation and

Representation, Clustering dan Classification (Basic methods, Decision Trees, CART), Targeted Marketing and Customer Modeling, Genomic Microarray Data Analysis, web mining, text mining, multi-media data mining.

x [Machine Learning and Data Mining, Knowledge Preparation and Representation, Clustering dan Classification (Basic methods, Decision Trees, CART), Targeted Marketing and Customer Modeling, Genomic Microarray Data Analysis, web mining, text mining, multi-media data mining]

PUSTAKA UTAMA/ REFERENCES

1. Witten, I. and Frank, E., 1999, Data Mining, Practical

Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann.

2. Perner, P. And Rosenfeld, A., 2003, Machine Learning and

Data Mining in Pattern Recognition, Springer: Berlin, Germany.

SS09 2241: Studi Kependudukan

SS09 2241: Demography Study