Discussion - Practice - QUIZ

(1)

Study Program Name Bachelor, Mathematics Department, FMKSD-ITS Course Name Algorithm and Programming

Course Code KM184202

Semester 2

Sks 4

Supporting Lecturer Dr. Dwi Ratna Sulistyaningrum, MT, Alvida Mustika Rukmi, S.Si, M.Si

Materials

• Algorithm

• Structured Programming

• Recursive

• GUI and Event Driven

Learning

Outcome ^[C2]

Able to explain basic concepts of mathematics that includes the concept of a proof construction both logically and analytically, modeling and solving the simple problems, as well as the basic of computing.

[C3]

Able to solve problems based on theoretical concepts in at least one field of mathematics: analysis and algebra, modeling and system optimization, and computing science.

COURSE LEARNING OUTCOME

1. Be able to understand the basic concepts of algorithms and procedural computer programming.

2. Be able to design algorithms, flow charts, and create computer programs with JAVA language programming to solve mathematical problems, individually or togetherly.

(2)

Meets Sub Course Learning

Outcome Breadth of Materials Learning Methods Time

Estimation Student Learning Experiences

Assessment Criteria and

Indicator

Weighting Assessment

(%) (1,2) Students are able to explain

the programming paradigm as well as to know the

programming languages.

Understanding:

o Definition of programming o Programming paradigm o Types of programming languages

Lecture

Discussion ²x(2x50”) Discussion Accuracy describes the definitions and paradigms of programming and explains the programming language (3,4) Students are able to explain

the definition of the algorithm and know the algorithm criteria and able to make the program flowchart (2,3)

Definition of algorithm definition Explanation of algorithm criteria Explanation creates a program flowchart

Lecture

Group Discussion 2x(2x50”) ^Task-

(Problem &

Solving)

Accuracy describes the definition of the algorithm and knows the algorithm criteria Precision create flowchart program (5,6) Students are able to explain

the definition of pseudo-code based on program flowchart (4)

Definition and manufacture of pseudocode

Lecture

Discussion 2x(2x50”) ^Quiz-1 Precise create

pseudocode based on flowchart

(7,8) Students are able to explain the basic principles of Java programming include data types, keywords, constants, variables

The concept of programming - Data type, keyword

- Definitions of variables, constants - variables in programming - type and casting conversion - Scope the appropriate variables

Lecture

Discussion 2x(2x50”) • TaSks

• Practice

• Accurate explanation of data types, keywords, variables, constants in Java

• Accuracy of type and casting conversion

• Actualization of exemplary examples.

(9,10) Students are able to apply the concept of Input-Output and Operator structure in programming.

I / O operation on java Operator assignment, bitwise on java

Parentheses

operator presedence on java

- Lecture - Discussion - Practice - Assignment

2x(2x50”) - TaSks - Discussion - Practice

Accuracy of using I / O Operation on java Operator assignment, bitwise on java Parentheses

• operator presedence on java

(11,12, 13,14) Students are able to apply the concept of control structure (condition / branching and repetition) in programming.

‐ If Statement , Switch Statement , Break, Exit, dan Continue dalam pemrograman Java

4x(2x50”) - TaSks -

Discussion - Practice

• Accurate use of If Statement, Switch Statement, Break, Exit, and Continue in Java programming

(3)

‐ For Loop Statement, While Loop Statement, Do While Statement dalam pemrograman

- QUIZ • For Loop Statement, While Loop Statement, Do While Statement in programming

• Presentation of taSks

• Encoding skills

(15,16) MIDTERM EXAM

(17,18,19) Students are able to apply the concept of function (method) in programming.

Non argument function Parameters function

3x(2x50”) ‐ Task

‐ Practice

• Accuracy makes functions both non arguments and arguments

• Mastering passing techniques (20,21) Students are able to apply the

concept of data type 1D and 2D arrays in programming.

Use of 1D and 2D array data types

in programming - Lecture

- Discussion - Practice - Assignment

2x(2x50”) ‐ Task

‐ Practice

• Accuracy

• Use of 1D and 2D array data types in programming

• Encryption writing skills with the use of 1D and 2D array data types.

• Accurate use of Data Type string

• Creation of functions that perform simple search and slimming processes

(22,23) Students are able to apply recursive concept and compare with iterative

Students are able to develop a recursive method for

mathematical functions

The recursive concept includes:

Understanding Recursive method for mathematical functions [1]: Chapter 18 [2]: Chapter 20

2x(2x50”) ‐ Task

‐ Practice Appropriateness explains the recursive concept

Clarity develops a recursive method for mathematical functions

(4)

Students are able to solve problems with recursive

(24,25,26) Students are able to apply string manipulation with String class library in JAVA

Use of String class library and method -

3x(2x50”) ‐ Task

‐ Practice

‐ QUIZ

The accuracy of using methods in the Java Class Library String for encoding requires string

manipulation

(27,28) Students are able to apply Java GUI toolkit concept for GUI based programming

The use of components in the Java GUI toolkit includes: AWT, SWT, and Swing

[1]: Chapter 14 Page 550 - 574

[2]: Chapter 12 p. 446 – 474

2x(2x50”) ‐ Task

‐ Practice

The precision of making Java GUI programming

(29,30) Students are able to understand Event- Driven concepts and are able to implement in Matlab

Event-Driven

[1]: Chapter 14 Page 561 - 574 [2]: Chapter 16 Page 600 - 603

2x(2x50”) ‐ Task

‐ Practice

Accuracy of using event driven

Seriousness in working.

Create a simple program that involves event-driven

(31,32) FINAL EXAM

(5)

Reference Main :

1. Java Programming Comprehensive, 10^th edition, Pearson Education, Inc., publishing as Prentice Pagel, 2013 2. Paul Deitel, Harvey Deitel, Java: How to Program, 9^th edition, Prentice Pagel, 2012

Supporting :

1. Abdul Kadir, “Algoritma & Pemrograman Menggunakan Java”, Andi Offset, 2012

(6)

EVALUATION AND ASSESSMENT PLAN

Course : Algorithms And Programming, Code: KM184202, credits: 4 , semester:2

Mee ting

Specific Learning Objective (Sub‐Competency)

Elements of Competency in Assessment Number of Questio

ns

Form of

Assessment %

Cognitive Psychomotor Affective

C1 C2 C3 C4 C5 C6 C7 P1 P2 P3 P4 P5 A1 A2 A3 A4 A5

(1,2) Students able to explain the paradigm of programming and know some programming languages.

√ √ √ Discussions

(3,4) Students able to explain the definition of algorithms and know the criteria of algorithm and able to construct a program flowchart

√ √ √ 4 Assignment-

Problem &

Solving)

10%

(5,6) Students able to explain the definition of pseudo code

according to program flowchart √ √ √ 3 Assignment 7.5%

(7,8) Students able to explain the basic principles of Java programming, including data types, keywords, constants, variables

√ √ √ 2 ‐ Assignment

‐ Practice 5%

(9,10, Students able to apply the concept of input-output

structure and operator in programming. √ √ √ 6 ‐ Assignment

‐ Discussions

‐ Practice

15%

(7)

11)) ‐ QUIZ 1 (12,

13,14)

Students able to apply the concept of control structure

(condition/branching and looping) in programming. √ √ √ 3 ‐ Assignment

‐ Discussions

‐ Practice

‐

7.5%

15,16

ETS

(17,18,1 9)

Students able to explain the concepts of function

(method) in programming. √ √ √ 4 ‐ Assignment

‐ Practice 10%

(20,21) Students able to apply the concept of 1-D and 2-D

array in programming. √ √ √ 4 ‐ Assignment

‐ Practice 10%

(22,23) Students able to apply the concept of recursive and compare it with iterative concept

 Students able to develop recursive method for mathematical functions

 Students able to solve problem using recursive approaches

√ √ √ 3 ‐ Assignment

‐ Practice 7.5%

(24,25, 26,)

Students able to apply string manipulation using class

library String in JAVA √ √ √ 6 ‐ Assignment

‐ Practice

‐ QUIZ 2

15%

(27, 28)Students able to apply the concept of GUI toolkit in

Java to create a program based on GUI √ √ √ 3 ‐ Assignment

‐ Practice 7.5%

(29,30) Students able to understand the event-driven concept

and able to implement it √ √ √ 2 ‐ Assignment

‐ Practice 5%

(8)

31, 32 EAS

Number

Questions 40

Percentage 100%

Description :

C1 : Knowledge P1 : Imitation A1 : Receiving

C2 : Comprehension P2 : Manipulation A2 : Responding

C3 : Application P3 : Precision A3 : Valuing

C4 : Analysis P4 : Articulation A4 : Organization

C5 : Syntesis & Evaluation P5 : Naturalisation A5 : Characterization C6 : Creative

Scoring Criteria

1. Assignment (10%)

After each chapter, there will be some exercises 2. Practice (20%)

Practice is conducted 8 times 3. Quiz I (10%)

Quiz I is conducted in the 6^th week, to test the material from the beginning until 5^th week with 4 questions of the same weight 4. Mid‐term exam (25%)

Mid‐term exam is conducted in 8^th week, to test the material from the beginning until 7^th week 5. Quiz II (10%)

Quiz II is conducted on 11^th week, to test the material after the mid‐term exam until 12^th week with 4 questions of the same weight

(9)

6. Final exam (25%)

Final exam is conducted in 16^th week, to test the material from after the mid‐term exam until 15^th week

(10)

Assessment Design

Week number‐ : 4 Assignment : 2

1. Assignment Objective :

Students able to understand algorithm and able to construct algorithm from a real problem to a program flowchart (2,3)

2. Assignment Description a. Objects studied :

Creating flowchart from a real problem.

b. What needs to be done and its constraints :

Creating a flowchart according to the given problem c. Method to complete the assignment :

The assignment is written on a paper d. Outcome :

Understand the method to create flowchart

3. Question example:

A university offers credits for its courses with the following criteria:

 Theory : one credit for 25 hours

 Laboratories : one credit for 10 hours

Create a flowchart that reads the number of hours in theory and the number of hours in laboratory that is taken by a student then compute the total credits

4. Scoring criteria

No. Aspect / Assessed Concept Score

1 Able to define the initial conditions and final conditions of the algorithm :

Input : Number of theoretical hours and number of laboratory hours Output : Total Credits

20

2 Able to draw the input and output processes 20

3 Able to draw the computation process 20

3 Able to create the complete flowchart completely and correctly

40

(11)

(12)

Study Program Name Bachelor, Mathematics Department, FMKSD-ITS Course Name Algorithm and Programming

Semester 2

Sks 4

Supporting Lecturer Dr. Dwi Ratna Sulistyaningrum, MT, Alvida Mustika Rukmi, S.Si, M.Si

Materials

• Algorithm

• Structured Programming

• Recursive

• GUI and Event Driven Learning

Outcome

[C2]

[C3]

(13)

Meets Sub Course Learning Outcome

Breadth of Materials Learning Methods Time Estimation

Student Learning Experiences

Assessment Criteria and

Indicator

Weighting Assessment

(%) (1,2) Students are able to

explain the programming paradigm as well as to know the programming languages.

Understanding:

o Definition of programming o Programming paradigm o Types of programming languages

Lecture

Discussion ²x(2x50”) Discussion Accuracy describes the definitions and paradigms of programming and explains the programming language (3,4) Students are able to

explain the definition of the algorithm and know the algorithm criteria and able to make the program flowchart (2,3)

Definition of algorithm definition

Explanation of algorithm criteria

Explanation creates a program flowchart

Lecture

Group Discussion 2x(2x50”) ^Task-

(Problem &

Solving)

Accuracy describes the definition of the algorithm and knows the algorithm criteria Precision create flowchart program (5,6) Students are able to

explain the definition of pseudo-code based on program flowchart (4)

Definition and manufacture of pseudocode

Lecture

Discussion 2x(2x50”) ^Quiz-1 Precise create

pseudocode based on flowchart (7,8) Students are able to

explain the basic principles of Java programming include data types, keywords,

constants, variables

The concept of programming - Data type, keyword - Definitions of variables, constants

- variables in programming - type and casting conversion - Scope the appropriate variables

Lecture

Discussion 2x(2x50”) • TaSks

• Practice

• Accurate explanation of data types, keywords, variables, constants in Java

• Accuracy of type and casting conversion

• Actualization of exemplary examples.

(9,10) Students are able to apply the concept of Input- Output and Operator structure in programming.

I / O operation on java Operator assignment, bitwise on java

Parentheses

operator presedence on java

2x(2x50”) - TaSks -

Discussion - Practice

Accuracy of using I / O Operation on java

Operator

assignment, bitwise on java

Parentheses

• operator

presedence on java (11,12, 13,14) Students are able to apply

the concept of control structure (condition /

‐ If Statement , Switch Statement , Break, Exit,

- Lecture - Discussion - Practice

4x(2x50”)

- TaSks

• Accurate use of If Statement, Switch Statement, Break,

(14)

branching and repetition) in programming.

dan Continue dalam pemrograman Java

‐ For Loop Statement, While Loop Statement, Do While Statement dalam

pemrograman

- Assignment

-

Discussion - Practice - QUIZ

Exit, and Continue in Java

programming

• For Loop Statement, While Loop Statement, Do While Statement in programming

• Presentation of taSks

• Encoding skills

(15,16) MIDTERM EXAM

(17,18,19) Students are able to apply the concept of function (method) in programming.

Non argument function Parameters function

3x(2x50”)

‐

^Task

‐

Practice

• Accuracy makes functions both non arguments and arguments

• Mastering passing techniques (20,21) Students are able to apply

the concept of data type 1D and 2D arrays in programming.

Use of 1D and 2D array data types in programming

2x(2x50”)

‐

^Task

‐

Practice

• Accuracy

• Use of 1D and 2D array data types in programming

• Encryption writing skills with the use of 1D and 2D array data types.

• Accurate use of Data Type string

• Creation of functions that perform simple search and slimming processes

(22,23) Students are able to apply recursive concept and compare with iterative

Students are able to develop a

recursive method for

The recursive concept includes:

Understanding Recursive method for mathematical functions [1]: Chapter 18

[2]: Chapter 20

2x(2x50”) ‐ Task

‐ Practice Appropriateness explains the recursive concept

Clarity develops a recursive

(15)

mathematical functions

Students are able to solve problems with recursive

method for mathematical functions

(24,25,26) Students are able to apply string

manipulation with String class library in JAVA

Use of String class library and method -

3x(2x50”) ‐ Task

‐ Practice

‐ QUIZ

The accuracy of using methods in the Java Class Library String for encoding requires string manipulation

(27,28) Students are able to apply Java GUI toolkit concept for GUI based

programming

The use of components in the Java GUI toolkit includes: AWT, SWT, and Swing

[1]: Chapter 14 Page 550 - 574

[2]: Chapter 12 p. 446 – 474

2x(2x50”) ‐ Task

‐ Practice

The precision of making Java GUI

programming

(29,30) Students are able to understand Event- Driven concepts and are able to implement in Matlab

Event-Driven

[1]: Chapter 14 Page 561 - 574 [2]: Chapter 16 Page 600 - 603

2x(2x50”)

‐

Task

‐

^Practice

Accuracy of using event driven

Seriousness in working.

Create a simple program that involves event-driven

(31,32) FINAL EXAM

(16)

Reference Main :

1. Java Programming Comprehensive, 10

^th

edition, Pearson Education, Inc., publishing as Prentice Pagel, 2013

2. Paul Deitel, Harvey Deitel, Java: How to Program, 9

^th

edition, Prentice Pagel, 2012

Supporting :

1. Abdul Kadir, “Algoritma & Pemrograman Menggunakan Java”, Andi Offset, 2012

(17)

Course

Course Name : Algorithm and Programming

Course Code : KM184202

Credit : 4

Semester : 2

Description of Course

Algorithms and programming is course that discuss the basic concepts of algorithms and procedural programming. The concepts of algorithm and programming is implemented in JAVA programming language and will be used to solve simple problems. The topic include: basic algorithms, data types, variables, I/O structures, operators, loops, control structures, functions and procedures, array, string manipulation, recursive, GUI and event driven.

The teaching system include tutorials, responses and scheduled workshops.

Learning Outcome

[C2]

[C3]

Course Learning Outcome

Main Subject

1. Algorithms: definition, criteria, flow chart, pseudo-code

2. Programming Concepts: paradigms, structured programming steps, programming languages

3. Java Programming Language: data types, keywords, constants, variables, I/O structures, operators, loops, control structures, functions and procedures, array, string manipulation, recursive, GUI and event driven.

Prerequisites

Reference

1. Java Programming Comprehensive, 10th edition, Pearson Education, Inc., publishing as Prentice Hall, 2013

2. Paul Deitel, Harvey Deitel, Java: How to Program, 9th edition, Prentice Hall, 2012

(18)

Supporting Reference

1. Abdul Kadir, “Algoritma & Pemrograman Menggunakan Java”, Andi Offset, 2012

(19)

Study Program Name Bachelor, Mathematics Department,FMKSD-ITS Course Name Artificial Neural Networks

Semester 8

Credits 2

Supporting Lecturer Prof. Dr. Mohammad Isa irawan, MT

Materials

 Modeling of ANN

 Matriks computation

 Algorithms in Artificial Neural Networks (ANNs)

 Some Aplications of ANNs

Learning Outcome

[C4]

Able to illustrate the framework of mathematical thinking in particular areas such as analysis, algebra, modeling, system optimization and computing science to solve real problems, mainly in the areas of environment, marine, energy and information technology.

[C5] Able to explain ideas and knowledge in mathematics and other fields to the society, in similar professional organizations or others.

[C5] Able to choose decisions and alternative solutions using data and information analysis based on an attitude of leadership, creativity and have high integrity in completing work individually or in a team.

1. Students are able to explain in any field the application of ANN

2. Students are able to analyze the simplest ANN algorithm to recognize AND, OR, NAND and NOR logic patterns.

3. Students are able to well explain the different implementation of ANN algorithm with 1 processing element and multi processing element.

4. Students are able to properly explain the network capable of storing memory

5. Students are able to properly explain the basic concepts of competition-based networks and problems that the network can solve

6. Students are able to explain the difference between the concept of backpropagation and variation network algorithms

7. Students are able to properly examine the scientific work on the ANN application

(20)

Estimation Student Learning Experiences

Assessment Criteria

and Indicator Weight ing Assess

ment (%) (1) Students are able to

explain where A neural network is applied.

‐ contracts Subject

‐ The introduction of artificial neural network applications [1] Irawan Chapter I

Lecture Introduction, simple case studies, group discussions

1x (2x50 ") Writing about some of the problems given solutions

 Good skills in explaining in any field of application of ANN

5 %

(1,2) Students are able to explain the neural network modeling of biological neural networks and artificial neural network algorithm simplest

‐ Fundamentals of computational models of neural networks 1 network processing elements

‐ Hebs algorithm,

‐ Perceptron, and

‐ There is line [1] Irawan Chapter I

- Lecture

- Exercises ²x (2x50 ") Writing about some of the problems given solutions

 Being able to analyze the simplest neural network algorithm to recognize patterns of logical AND, OR, NAND and NOR

10%

(3) Students are able to implementation of simple artificial neural network algorithm to identify simple patterns

‐ Project presentation simple algorithm application Hebs., Perceptron and Adaline [1] Irawan Chapter II

Practice 1x (2x50 ") ‐ Source code is the result of lab

‐ Writing about some of the problems given solutions

 Good skills in explaining differences in neural network algorithm imple-tion 1 processing elements

 The precision-kan become clear implementation

5 %

(4,5) Students are able to explain the concept and application of artificial neural network algorithm that is capable of storing a memory

‐ Assosiative Memory

‐ counter Propagation

‐

Lecture,

Review session ²x (2x50 ") ‐ Writing about some of the problems given solutions

‐ Quis I

 Skill in explaining the network is capable of storing a memory

10 %

(6) Students are able to

explain the basic concept ‐ Kohonen SOM

‐ LVQ

Lecture,

Review session ¹x (2x50 ") ‐ Writing about

some of the  Skill in explaining the basic concepts of

10 %

(21)

of a neural network-based

competition problems given

solutions network-based competition (7) Students are able to apply

the concept of competition in the neural network through simple examples

‐ Presentation simple project SOM Kohonen network, LVQ and Counter Propagation for clustering and data classification

 Appropriateness explained types based competition

 Have an idea about solving problems with the help of a network- based competition

10%

8 MIDTERM EXAM (9) Students are able to

examine the papers on artificial neural network that utilizes the concept of competition

‐ Review of scientific work / paper application Kohonen SOM, LVQ and Counter Propagation

Group discussion, ¹x (2x50 ") Concise writing the review of scientific work on SOM Kohonen network, LVQ and Counter propagation

 Good skills in the review of scientific work on application Kohonen SOM, LVQ and

Counterpropagation

 Have an idea about solving problems with the help of Kohonen SOM, LVQ and Counterpropagation

10%

(10,11) Students are able to explain the concept and its variations backpropagation network

‐ Backpropagation network

‐ Variation

Lecture,

Group discussion, 2x (3x50 ")]

 Good skills in explaining different concepts

backpropagation network algorithm and its variations

20 %

(12) Students are able to explain the concept of network applications and variations backpropagation

‐ Backpropagation network application for pattern recognition of data

‐ Backpropagation network applications for forecasting

Lecture,

Group discussion, ¹x (2x50 ") ‐ Writing about some of the problems given solutions

 Good skills in explaining the network application backpropagation for pattern recognition and forecasting

10%

(13) Students are able to explain the imple-tion nets backpropagation for pattern recognition

‐ Project presentation Backpropagation network applications and variations

Lecture,

Group discussion, ¹x (2x50 ") ‐ Source code is the result of lab

 Appropriateness explained the types of back propagation algorithm

10 %

(22)

Reference Main :

1. Irawan, M. Isa, “Dasar-Dasar Jaringan Syaraf Tiruan ”, ITS Press, 2013

Supporting :

1. Laurene Fauset, “Fundamental of Artificial Neural Networks”, Penerbit Prentice Hall, 1994 2. Simon Haykin, “Kalman Filtering and Neuralnetwork”, Penerbit John Wiley & Sons, 2001

3. James A. Freeman and David M. Skapura, “Neural Networks Algorithms, Applications, and Programming Techniques”, Penerbit Addison Wesley, 1991

‐ Quiz II

 Have an idea about solving problems with network support backprropagation (14, 15) students are able to read

scientific papers that apply neural networks to solve problems

‐ Assessing international

journals or proceedings Presentation 2x (2x50 ") ‐ Summary results of the study

 The accuracy describes

understanding and solving cases

20%

16 FINAL EXAM

(23)

STUDENT LEARNING EVALUATION PLAN

Course : Artificial Neural Networks, Code: KM184828, sks:2 sks, smt:8 Learning Outcome :

1. Able to illustrate the framework of mathematical thinking in particular areas such as analysis, algebra, modeling, system optimization and computing science to solve real problems, mainly in the areas of environment, marine, energy and information technology.

2. Able to explain ideas and knowledge in mathematics and other fields to the society, in similar professional organizations or others.

3. Able to choose decisions and alternative solutions using data and information analysis based on an attitude of leadership, creativity and have high integrity in completing work individually or in a team.

Meet s

Specific Learning Objective (Sub‐Competence)

Elements of Competency in Assessment

Questio ns Number

Form of

Assessment %

Cognitive Psychomotor Affective

C1 C2 C3 C4 C5 C6 C7 P1 P2 P3 P4 P5 A1 A2 A3 A4 A5 1

Students are able to explain where A neural

network is applied. X

X

X X 3 ‐ Non-test

‐ Lecture note 5

2 Students are able to explain the neural network modeling of biological neural networks and artificial neural network algorithm simplest

X

(24)

3 Students are able to implementation of simple artificial neural network algorithm to identify simple patterns

X

4,5 Students are able to explain the concept and application of artificial neural network algorithm that is capable of storing a memory

X

6,7

Students are able to explain the basic concept

of a neural network-based competition X X

X

8 MIDTERM EXAM

9 Students are able to apply the concept of competition in the neural network through simple examples

X X

10,11 Students are able to examine the papers on artificial neural network that utilizes the concept of competition

X X X X X 2 ‐ Non-test

‐ Demo Program 10

(25)

12 Students are able to explain the concept and its

variations backpropagation network X X X X X 3 ‐ Non-test

13 Students are able to explain the concept of network applications and variations backpropagation

X X X X 3 ‐ Non-test

14 Students are able to explain the implantation

nets backpropagation for pattern recognition X X X X 3 ‐ Non-test

15 students are able to read scientific papers that

apply neural networks to solve problems X X X X X X 2 ‐ Non-test

‐ Demo Program 15

16 FINAL EXAM

Meets 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Number

Questions item 3 3 3 3 3 3 3 ETS 3 2 2 3 3 3 2 EAS

Percentage 5 5 5 15 15 5 5 5 5 15 10 15 100%

(26)

Informations :

C1 : Knowledge P1 : Imitation A1 : Receiving

C2 : Comprehension P2 : Manipulation A2 : Responding

C3 : Application P3 : Precision A3 : Valuing

C4 : Analysis P4 : Articulation A4 : Organization

C5 : Syntesis & Evaluation P5 : Naturalisation A5 : Characterization C6 : Creative

(27)

Format of Task Design

Study Program Name Bachelor, Mathematics Department,FMKSD-ITS Course Name Artificial neural networks

Course Code ^KM184828

Semester 8

Credits 2

Supporting Lecturer Prof. Dr. Mohammad Isa Irawan, MT

Weeks : 4, 5 Task : 1

1. Purpose of task :

Students are able to demonstrate through a simple program in Java or MATLAB to implemented Perceptron algorithm to recognized operator logic AND, OR, NAND dan NOR.

2. Task description

a. Claim object :

Project programming - Implementation of perceptron algorithm to recoqnized operator logic.

b. What to do and limitation :

Create a program that can demonstrate ability of perceptron to memorize input-output data, and recoqnized the pattern

c. Description of output of work produced / done:

Report and program that must be presented in front of other students 3. Assessment criteria

No. Assessed Aspects / Concepts Score

1 Able to demonstrate ability of perceptron algorithm 30 2 Able to implement in programming language Java / Python,

minimum in MATLAB

40

3 Able to demonstrate the program well, user friendly, and beautiful 30

Score Total 100

(28)

Format of Task Design

Semester 8

Credits 2

Supporting Lecturer Prof. Dr. Mohammad Isa irawan, MT

Week - : 15 Task : 2

1. Purpose of task :

Student able to explain others algorithms that have not implemented yet in a project.

2. Task description

a. Claim object :

Explain a part of book which contain an algorithm of ANNs b. What to do and limitation :

Make a presentation to explain an algorithm which has not implemented in project programming.

c. Method/way of done reference used:

Tasks are typed in a power point that contain algorithm, and example of application.

d. Description of output of work produced / done:

A power point and translation in word.

3. Assessment criteria 4.

No. Assessed Aspects / Concepts Score

1 Able to explain one or more other algorithm which have not been implemented yet in the programming project

30

2 Able to explain using examples from literature 40 3 Able to explain clearly and systematically 30

Score total 100

(29)

Test Plan

Semester 8

Credits 2

Example of description test

Course Name : Artificial neural networks Date : Friday, 01‐04‐2016

Duration/Type : 100 minutes / Closed Book Lecturer : Prof. Dr. M. Isa Irawan, MT

ATTENTION!!!

Any kind of violation (cheating, working together, etc.) that are done during Midterm Test or Final Test will be sanctioned with courses cancellation in the current semester.

1. By using a bipolar input (X1, X2) and target t, compare the result if we use Hebb network and Perceptron for training process of NOR function. How many iterations created? What conclusions can be drawn? (Alpha = 1, threshold = 0.1)

2. By using a bipolar input (X1, X2) and target t, compare the result if we use Perceptron for training process of AND NOT function. How many iterations created? What conclusions can be drawn? (Alpha = 1, threshold = 0.1)

3. Adaline network is used to recognize pattern of 2 bipolar input and 1 bipolar output with its target. Obtain the last weight from your Adaline network that can recognize NAND logic pattern well. Use the activation function. Tolerate value is 0.5.

𝑦 1, 𝑖𝑓 𝑦 0

1, 𝑖𝑓 𝑦 0 The algorithm of Adaline is:

Step 0: Weight initialization (very small random value) Set the learning rate 𝛼

Step 1: If the stopping condition is not fulfilled, do step 2‐6 Step 2: for every bipolar pair (s:t) do step 3‐5

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Step 3: Set activation from input unit 𝑖 1,2,3, … , 𝑛:

𝑥 𝑠

Step 4: Calculate the input network towards output unit:

𝑦 𝑏 ∑ 𝑥 𝑤

Step 5: Update the bias and weight 𝑖 1,2,3, … , 𝑛:

𝑏 𝑛𝑒𝑤 𝑏 𝑜𝑙𝑑 𝛼 𝑡 𝑦

𝑤 𝑛𝑒𝑤 𝑤 𝑜𝑙𝑑 𝛼 𝑡 𝑦 𝑥

Step 6: Check the stopping condition, if the biggest weight in step 2 is smaller than the tolerate value, then stop, otherwise continue

Diusulkan / Proposed Prof. Dr. M. Isa Irawan, MT Dosen Pengampu

Ditelaah / Reviewed Prof. Dr. M. Isa Irawan, MT Ketua RMK Komputasi

Disetujui / Approved

Dr. Didik Khusnul Arif, S.Si, M.Si Kaprodi Sarjana (S1)

Matematika

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Study Program Name Bachelor, Mathematics Department, FMKSD-ITS Course Name Artificial Neural Network

Semester 8

SKS 2

Materials  Artificial Neural Network

Learning Outcome

[C4]

[C5]

Able to explain ideas and knowledge in mathematics and other fields to the society, in similar professional organizations or others.

[C5]

Able to choose decisions and alternative solutions using data and information analysis based on an attitude of leadership, creativity and have high integrity in completing work individually or in a team.

5. Students are able to properly explain the basic concepts of competition-based networks and problems that the network can solve

6. Students are able to explain the difference between the concept of backpropagation and varietin network algorithms

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Meets Sub Course Learning Outcome

Breadth of Materials Learning methods Time Estimation

Assessment Criteria and Indicator

Weight ing Assess

ment (%) (1) Students are able to explain

where A neural network is applied.

‐ contracts Subject

‐ The introduction of artificial neural network applications [1] Irawan Chapter I

Lecture Introduction, simple case studies, group discussions

 Good skills in

explaining in any field of application of ANN

5%

(1,2) Students are able to explain the neural network

modeling of biological neural networks and artificial neural network algorithm simplest

‐ Fundamentals of computational models of neural networks

1 network processing elements

‐ Hebs algorithm,

‐ Perceptron, and

‐ There is line [1] Irawan Chapter I

- Lecture

- Exercises ²x (2x50 ") Writing about some of the problems given solutions

 Being able to analyze the simplest neural network algorithm to recognize patterns of logical AND, OR, NAND and NOR

10%

(3) Students are able to implementation of simple artificial neural network algorithm to identify simple patterns

‐ Project presentation simple algorithm application Hebs., Perceptron and Adaline [1] Irawan Chapter II

 Good skills in explaining differences in neural network algorithm imple-tion 1 processing elements

 The precision-kan become clear implementation

5%

(4,5) Students are able to explain the concept and application of artificial neural network algorithm that is capable of storing a memory

‐ Assosiative Memory

‐ counter Propagation

‐

Lecture,

Review session ²x (2x50 ") ‐ Writing about some of the problems given solutions

‐ Quis I

 Skill in explaining the network is capable of storing a memory

10%

(6) Students are able to explain the basic concept of a neural network-based competition

‐ Kohonen SOM

‐ LVQ

Lecture,

Review session ¹x (2x50 ") ‐ Writing about some of the problems given solutions

 Skill in explaining the basic concepts of network-based competition

10%

(33)

(7) Students are able to apply the concept of competition in the neural network through simple examples

‐ Presentation simple project SOM Kohonen network, LVQ and Counter Propagation for clustering and data classification

 Appropriateness explained types based competition

 Have an idea about solving problems with the help of a network- based competition

10%

8 MIDTERM EXAM (9) Students are able to

examine the papers on artificial neural network that utilizes the concept of competition

‐ Review of scientific work / paper application Kohonen SOM, LVQ and Counter Propagation

Group discussion, ¹x (2x50 ") Concise writing the review of scientific work on SOM Kohonen network, LVQ and Counter propagation

 Good skills in the review of scientific work on application Kohonen SOM, LVQ and

Counterpropagation

 Have an idea about solving problems with the help of Kohonen SOM, LVQ and Counterpropagation

10%

(10,11) Students are able to explain the concept and its

variations backpropagation network

‐ Backpropagation network

‐ Variation

Lecture,

Group discussion, 2x (3x50 ")]

 Good skills in explaining different concepts

backpropagation network algorithm and its variations

20%

(12) Students are able to explain the concept of network applications and variations backpropagation

‐ Backpropagation network application for pattern recognition of data

‐ Backpropagation network applications for forecasting

Lecture,

Group discussion, ¹x (2x50 ") ‐ Writing about some of the problems given solutions

 Good skills in explaining the network application backpropagation for pattern recognition and forecasting

10%

(13) Students are able to explain the imple-tion nets

backpropagation for pattern recognition

‐ Project presentation Backpropagation network applications and variations

Lecture,

Group discussion, ¹x (2x50 ") ‐ Source code is the result of lab

 Appropriateness explained the types of back propagation algorithm

 Have an idea about solving problems with

10%

(34)

Reference Main :

1. Irawan, M. Isa, “Dasar-Dasar Jaringan Syaraf Tiruan ”, ITS Press, 2013

Supporting :

1. Laurene Fauset, “Fundamental of Artificial Neural Networks”, Penerbit Prentice Pagel, 1994 2. Simon Haykin, “Kalman Filtering and Neuralnetwork”, Penerbit John Wiley & Sons, 2001 3. James A. Freeman and David M. Skapura, “Neural Networks Algorithms, Applications, and

Programming Techniques”, Penerbit Addison Wesley, 1991

‐ Quiz II network support backprropagation (14, 15) students are able to read

scientific papers that apply neural networks to solve problems

‐ Assessing international journals or proceedings

Presentation 2x (2x50 ") ‐ Summary results of the study

 The accuracy describes

understanding and solving cases

20%

16 FINAL EXAM

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Course

Course Name : Artificial Neural Network

Course Code : KM184828

Credit : 2

Semester : 8

Description of Course

The course of artificial neural networks is a course that studies computational algorithms that mimic how biological neural networks work. This course is part of the Data Science, because the algorithm learned works well when applying data processing.

Learning Outcome

[C4]

[C5] Able to explain ideas and knowledge in mathematics and other fields to the society, in similar professional organizations or others.

[C5] Able to choose decisions and alternative solutions using data and information analysis based on an attitude of leadership, creativity and have high integrity in completing work individually or in a team.

Course Learning Outcome

5. Students are able to properly explain the basic concepts of competition- based networks and problems that the network can solve

6. Students are able to explain the difference between the concept of backpropagation and varietin network algorithms

Main Subject

1. Modeling of artificial neural networks from biological neural networks, 2. A simple pattern recognition with Perceptron, Hebb and Adaline, 3. Character recognition with Percepron, Associative memories, 4. Classification with BP, and LVQ,

5. Clustering with Kohonen SOM, 6. Forecasting BP, and RBF 7. Alternative model of ANN

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Prerequisites

Linear Algebra Elementer Computer Programming Reference

1. Irawan, M. Isa, “Dasar-Dasar Jaringan Syaraf Tiruan ”, Penerbit ITS Press, 2013

Supporting Reference

1. Laurene Fauset, “Fundamental of Artificial Neural Networks”, Penerbit Prentice Hall, 1994

2. James A. Freeman and David M. Skapura, “Neural Networks Algorithms, Applications, and Programming Techniques”, Penerbit Addison Wesley, 1991

3. Simon Haykin, “Kalman Filtering and Neuralnetwork”, Penerbit John Wiley & Sons, 2001

(37)

(38)

Study Program Name Bachelor, Mathematics Department, FMKSD-ITS Course Name Elementary Linear Algebra

Semester 2

Sks 4

Supporting Lecturer Dian Winda S, SSi, MSi

Materials • Matrices and Vectors

• Vector Space

• Linear Transformation Learning

Outcome [C2] Able to explain basic concepts of mathematics that includes the concept of a proof construction both logically and analytically, modeling and solving the simple problems, as well as the basic of computing.

[C3] Able to solve problems based on theoretical concepts in at least one field of mathematics:

analysis and algebra, modeling and system optimization, and computing science.

1. Students are able to follow developments and apply mathematics and be able to communicate actively and correctly either oral or written.

2. Students are able to explain intelligently and creatively about the significant role of ALE applications in the field of related knowledge clusters and other fields.

3. Students have a special ability and able to process their ideas enough to support the next study in accordance with the related field.

4. Students are able to present their knowledge in Elementary Linear Algebra independently or in teamwork.

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Estimatio n

Assessment Criteria

and Indicator Weight ing Assess

ment (%) 1-4 • Students are able to

complete the SPL by the Gaussian or Gauss Jordan elimination method And able to explain why SPL has no settlement.

• Students are able to use operations on the matrix and understand the algebraic properties of the matrix

• The understanding of system of linear equation and

augmented matrix

• Elementary Row Operation

• Gaussian and Gauss Jordan elimination

• Operation Matrix.

the properties of algebra in the matrix

[Ref. 1 page: 9-98]

• Lectures,

• Student conditioning,

• Question and answer.

• Giving exercise

• Group discussion,

4x(2x50’)

Task Exercise

questions • Accuracy defines system of linear equation and augmented matrix.

• Ability to solve system of linear equation by elementary row operation

• Be able to solve using Gaussian and Gauss Jordan

• Be able to explain the properties of algebra in the matrix

15%

5-6 • Students are able to find inverse matrix, can complete system of linear equation by inversing matrix

• Students recognize the types of matrices and properties of the matrix

• Looking for Inverse matrix

• Complete the system of linear equation with the inverse matrix

• Matrix type: Diagonal matrix, triangular matrix, symmetry matrix and its properties [Ref. 1 page: 99-139]

• Lectures,

• Giving exercise

• Group discussion

2x(2x50’) Task Exercise

questions • Be able to get the inverse of a matrix

• Able to complete system of linear equation by inversing matrix

• Be able to explain the types and properties of the matrix

5%

7-8 • Students are able to find the determinant of a matrix with Cofactor expansion

• Students are able to find the determinant of a matrix by Row Reduction

• Students are able to understand the properties of the determinant

• Counting determinants with Cofactor expansion

• Counting determinants by Reducing Rows

• the properties of the determinant

• complete SPL with Cramer rules [Ref. 1 page: 173-211]

• Lectures,

• Giving exercise

• Group discussion

2x(2x50’) Task Exercise

questions • Able to calculate determinants with Cofactor expansion

• Capable of Counting determinants by Row Reduction

• Be able to explain the properties of the determinant

10%