Study Materials / Learning Materials

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SEMESTER LEARNING PLAN (RPS) HASANUDDIN UNIVERSITY FACULTY OF CULTURAL STUDIES ARABIC LITERATURE STUDY PROGRAM

Document

Code SEMESTER LEARNING PLAN

COURSE (MK) CODE MK family WEIGHT ( credits )

2 SEMESTER Compilation Date

Language Statistics Learning technologies T = 1 P = 1 5 January 29 ,

2019 AUTHORIZATION

Academic Senate Department Head

RPS developer RMK Coordinator Chairman of PRODI

Sri Astuti Thamrin Yusring Sanusi B

Andi Agussalim Sri Astuti Thamrin Haeruddin

Learning Outcomes (CP)

CPL-PRODI charged to the MK

CPL2 Able to demonstrate adaptive and collaborative abilities in various working conditions

CPL3 Able to apply logical, critical, systematic, and innovative thinking in the context of implementing science and technology

Subject Learning Outcomes (CPMK)

CPMK It could download g uasai basic concepts of statistics and research data within the scope of duties and expectations.

CPL Sub-CPMK

CPL- 2 Mastering the concept of academic integrity in general and the idea of plagiarism in particular, in terms of types of plagiarism, consequences of violations, and efforts to prevent them;

CPL- 3 Understand the basic concepts of statistics, data collection techniques, and case examples

CPL- 3 Understand statistical descriptions by tabulating data and presenting data in writing, tables, and graphs

CPL- 3 Able to calculate the size of concentration and distribution of data through manual computation and computation;

CPL- 3 Be able to calculate the correlation between two variables and their application;

CPL- 3 Able to find the relationship between the independent variable and the dependent variable using regression;

CPL- 3 Able to analyze the relationship between 2 variables using variation analysis, especially the t-test Short Descriptio

n MK

This course will lead lecture participants to have the ability to master the basic concepts of statistics and data in research. Furthermore, it is discussed the background that underlies the processing of data and statistical analysis. To understand the basic concepts of statistics, the material is made in the form of offline and online learning, or it can be said that it can be accessed via the web. Knowledge about statistical software to master the basic concepts of statistics will also be provided through a practicum.

Study Materials / Learning Materials

1. Introduction to the basic concepts of statistics 2. Data description

3. Measuring scale 4. Datacenter size 5. Data spread size

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6. Correlation 7. Regression

8. Analysis of variance

Reference

Main :

1. Sudjana, 2005. Statistical Methods. Edition 6. Tarsito. Bandung

2. Saefuddin, A., Notodiputro, KA, Alamudi, A., and Sadik, K. 2009. Basic Statistics. PT Grasindo. Jakarta

3. Baso, YS (2016). Online Arabic Learning Model based on the Learning Management System. Makassar: Hasanuddin University Arabic Literature Study Program.

Supporters :

4. B h a t t a r a y a, G . K ., A n d J o h n s o n s , R . A. , 2003 . S t a t i s t i c a l P r i n c i p l e s a n d M e t h o d s , J o h n W il e y a n d S o n s , N e w Y o r k.

Supporting lecturer

Sri Astuti Thamrin, S.Si, M.Stats, Ph.D Dr. Yusring Sanusi B., SS, M.App.Ling.

Dr. Andi Agussalim, SS, M.Hum.

Course s yarat -

Week of- Sub-CPMK

(Final ability of each learning stage)

Assessment

Help Learning, Learning Methods, Student Assignment,

[ Estimated time]

Learning materials [ Library ]

Rating Weigh t (%) Indicator Criteria & Form Online ( online ) Offline

( offline )

(1) (2) (3) (4) (5) (6) (7) (8)

1

Mastering the concept of academic integrity in general and the idea of plagiarism in particular, in terms of types of plagiarism, consequences of violations, and efforts to prevent them;

Thoroughness explains the abilities that will be obtained and the activities that will be carried out by the lecture participants and the ability to use the plagiarism application

Demonstrate the stages of personal actions to achieve sub- CPMK and demonstrate an application of plagiarism (eg plagscout)

SIKOLA Theory 1 PT + BM [(1 + 1) x (2x60 ”)]

Assignment: Class particip ants register on

the plagscout site and check the similarity of one of the previous semester's courses

assignments. Lecture participants mark

the similarity of their work and note the source copy of the similarity.

Form: Lecture Method: Coope rative Learning

TM [(1x (2x50 ”)]

College contracts, RPS, CPMK, Permenristek dikti number 44 of 2015, KKNI documents, Unhas academic regulations.

10

2 to 3

Understand the basic concepts of statistics, data collection techniques, and case examples

Accuracy describes roles statistics and connect them with other fields of science

10 = precisely explains three things, namely:

theory/concept, benefits, and the

SIKOLA Handout 2 PT + BM [(1 + 1) x (2x60 ”)]

Assignment: Lecture participants read at least 5

Form: Lecture Method: Fishbo wl Discussion Forum

Understandi ng the Basic Concepts of Statistics (research,

1 0

(3)

The accuracy determines the technique

data collection by type data used on

some cases

role

of statistics in va rious fields and data collection techniques

5 = only describes two of the three (theory/concept) , benefits or roles

references on statistical concepts and data collection (other than book 1 and book 2).

TM [(1x (2x50 ”)] population, samples, variables, parameters), data

sources, data types, and data collection techniques a nd

measuremen t scales Book 1, Chapter 1 Book 2, Chapter 1 Book 3, Chapter 1

4 to 5

Understand statistical descriptions by tabulating data and presenting data in writing, tables, and graphs

The accuracy of performing descriptive statistics by tabulating the data and presenting the data in hand, tables, and graphics

10 =

appropriately explains and practices descri ption statistics by tabulating data, giving data, and presenting it in writing, tables, and graphics

5 = just explain and practice some of the ways of describing statistically

Assignment: Lecture participants make tables and graphs according to the teaching material, which is equipped with an

explanation of the meaning of each table and chart in various applications in the field of science

Form: Lecture + Practicum Method: PjBL

TM [(1x (2x50 ”)]

Practicum [(2) x (2x170 ”)]

Data Checking, Data Coding, and Data Tabulation, Data Arrangement in writing, histogram tables and charts, scatter plots, circles, line charts Book 1, Chapter 3, Books 2 and 3, Chapter 2

1 0

6 to 8 Able to compute Accuracy in calculating the size 10 = precisely SIKOLA Theory 4 Form: Lecture + Data Center 20

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centralization and dispersion size from data, through manual computation and computation ;

of data centering and data

distribution calculates and

practices data centering and scattering measures in multiple fields

5 = only counts one of the data centering or scattering measures

PT + BM [(1 + 1) x (2x60 ”)]

Assignment: Lecture participants will calculate centralization and presentation of data from various fields, both quantitative and qualitative data, and hybrid tests.

Practicum Method: PjBL

TM [(1x (2x50 ”)]

size and data deviation measure Book 1, chapter 5 Chapter 2 - chapters 2- 3

9 to10

Be able to calculate the correlation between two variables and their application ;

The accuracy of calculating the correlation between 2

variables and applying them

10 = precisely calculates the spearman rank correlation and the Pearson product-moment correlation 5 = calculates only one of the two correlations

Assignment: College participants will look for problems that can be applied to calculate the correlation between two variables

TM [(1x (2x50 ”)]

Spearman rank correlati on and Pearson product- moment correlation

Book 1, Chapter 6-7

15

11 to 13

Able to find the relationship between the independent variable and the dependent variable using regression ;

Compliance in understanding the concept of regression analysis and demonstrating it in daily life data

10 = appropriate to analyze data using linear and multiple

regression using software 5 =

only analyzed data using one of the

two regression analyzes

Assignment: Class participa nts look for data and analyze the data using linear and multiple regression analysis

TM [(1x (2x50 ”)]

Practicum [(1) x (2x170 ")]

Linear regression, multiple regression

Book 1, Chapters 8- 9

Book 2, chapters 4-5

15

14 to 16

Able to analyze the relationship between 2 variables using variation analysis, especially the t-test

Complete understanding of the concept of variance analysis and demonstrate it in daily life data

10 = appropriate to analyze data using analysis of variance with t-

Assignment: Class

Analysis of variance, one variable, and two

20

(5)

test of one variable and two variables using software

5 = only analyzed the data using one of the two t-tests

participants look for data and analyze the data using analysis of variance with t- test

TM [(1x (2x50 ”)]

Practicum [(1) x (2x170 ")]

variable t- test Book 1, Chapter 10 Book 2, chapters 6-7

Note :

1. Learning Outcomes of Graduates of PRODI (CPL-PRODI) are abilities possessed by each PRODI graduate: the internalization of attitudes, mastery of knowledge and skills according to the level of the study program obtained through the learning process.

2. The CPL charged on the course are some of the learning outcomes of the study program graduates (CPL-PRODI), which are used for the formation/development of a system that consists of aspects of attitude, general skills, superior skills, and knowledge.

3. CP Course (CPMK) is an ability specifically described from the CPL charged to a course and is specific to the study material or learning material for that course.

4. Subject Sub-CP (Sub-CPMK) is an ability described specifically from the CPMK that can be measured or observed. It is the final ability planned at each learning stage and specific to its learning material.

5. Indicators of ability assessment in the process and student learning outcomes are specific and measurable statements that identify the ability or performance of student learning outcomes accompanied by evidence.

6. Assessment criteria are benchmarks used as a measure or measure of learning achievement in assessments based on predetermined indicators. Assessment criteria are guidelines for assessors so that the review is consistent and unbiased. The requirements can be either quantitative or qualitative.

7. Form of assessment: test and non-test.

8. Forms of learning: Lecture, Response, Tutorial, Seminar or equivalent, Practicum, Studio Practice, Workshop Practice, Field Practice, Research, Community Service, and other equivalent forms of learning.

9. Learning Methods: Small Group Discussion, Role-Play & Simulation, Discovery Learning, Self-Directed Learning, Cooperative Learning, Collaborative Learning, Contextual Learning, Project-Based Learning, and other equivalent methods.

10. Learning Materials are details or descriptions of the study material presented in the form of several subjects and sub-topics.

11. The weight of the assessment is the percentage of the evaluation of each sub-CPMK achievement, which is proportional to the difficulty level of achieving the sub-CPMK.

The total is 100%.

12. TM = Face to Face, PT = structured assignment, BM = independent study.

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Hasanuddin University Faculty of Cultural Sciences West Asian Literature Department

SHORT SYLLABUS

COURSES

Name Language Statistics Code 357F4102

Credit 2

Semester 5

COURSE DESCRIPTION

This course is expected to support other systems in the preparation of the final project. This course consists of 7 Sub- CPMK with a task plan to measure the achievement of Sub-CPMK material mastery. Among this course's subject matter are the basic statistics, plagiarism, data processing, and analysis.

COURSE LEARNING OUTCOMES (CPMK)

1 Able to master the basic concepts of statistics and data in research within the scope of their duties and jobs.

SUB COURSE LEARNING OUTCOMES (Sub-CPMK)

1 Mastering the concept of academic integrity in general and the idea of plagiarism in particular, in terms of types of plagiarism, consequences of violations, and efforts to prevent them;

2 Understand the basic concepts of statistics, data collection techniques, and case examples

3 Understand statistical descriptions by tabulating data and presenting data in writing, tables, and graphs 4 Able to calculate the size of concentration and distribution of data through manual computation and

computation;

5 Be able to calculate the correlation between two variables and their application;

6 Able to find the relationship between the independent variable and the dependent variable using regression;

7 Able to analyze the relationship between 2 variables using variation analysis, especially the t-test LEARNING MATERIALS

1 Introduction to the basic concepts of statistics 2 Data description

3 Measuring scale 4 Datacenter size 5 Data spread size 6 Correlation 7 Regression

8 Analysis of variance REFERENCES

MAIN REFERENCES

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3. Baso, YS (2016). Online Arabic Learning Model based on the Learning Management System. Makassar: Hasanuddin University Arabic Literature Study Program.

SUPPORTING REFERENCES

PROVISIONS (If any) -

STUDENT DUTY PLAN

COURSES Arabic Language Computer Application Program

CODE 357F4102 credits 2 SEMESTER 5

SUPPORTING LECTURER Sri Astuti Thamrin, S.Si., M.Stat., Ph.D.

Dr. Yusring Sanusi B., SS, M.App.Ling Dr. Andi Agussalim, M.Hum.

FORM OF TASKS WORKING TIME OF DUTIES

Individual Will be announced

TITLE OF TASKS Plagiarism

SUB COURSE LEARNING OUTCOMES

Sub-CPMK1 . Mastering the concept of academic integrity in general and the idea of plagiarism in particular, in terms of types of plagiarism, consequences of violations, and efforts to prevent them;

DESCRIPTION OF TASKS

Participants make reading reports related to the concept of integrity in conducting research and make tutorials on the use of the plagiarism application

METHOD OF DUTY WORK

 Minimum word count 1500, use Times New Roman font, font size 12, margin size (top 4 cm, bottom 3 cm, left 4 cm, right 3 cm), Space 2, APA quote system.

 Stored in the form of an office word extension file . docx

 Give the file a name in the form: Tgs1_NamaL Complete_NIM.docx.

 The task file is uploaded according to the time specified in Sikola Task First Task FORM AND OUTSIDE FORMAT

a. The object of the work: Integrity and plagiarism

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b. Output Form:

Softcopy of office word extension .docx

INDICATORS, CRETERIA, AND WEIGHT OF ASSESSMENT According to the assessment rubric, attachment 1 IMPLEMENTATION SCHEDULE

1. Will be announced

ETC -

REFERENCES

COURSES Language Statistics

SUPPORTING LECTURER Sri Astuti Thamrin, S.Si. , M.Stat ., Ph.D.

TITLE OF TASKS

Statistical concepts and data collection techniques SUB COURSE LEARNING OUTCOMES

Sub-CPMK2 . Understand the basic concepts of statistics, data collection techniques and case examples DESCRIPTION OF TASKS

Making reading reports related to (1) the role of statistics and its relationship with other sciences (2) Data collection techniques accompanied by case examples

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 Minimum number of words is 700, use Times New Roman font, font size 12, margin size (top 4 cm, bottom 3 cm, left 4 cm, right 3 cm), Space 2, APA quote system.

 Stored in the form of an office word extension file . docx

 Give the file a name of the form: Tgs2_NamaL Lengkap_NIM.docx .

 The task file is uploaded according to the time specified in Sikola Task Second task FORM AND OUTSIDE FORMAT

a. Garapan object: Concept of statistical data and data collection technique b. Output Form:

Softcopy of office word extension .docx

INDICATORS, CRETERIA, AND WEIGHT OF ASSESSMENT By the assessment rubric, attachment 2

IMPLEMENTATION SCHEDULE

ETC -

REFERENCES

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Individual Practice Will be announced

TITLE OF TASKS

Tabulation and presentation of descriptive data SUB COURSE LEARNING OUTCOMES

Sub-CPMK3 . Understand statistical descriptions by tabulating data and presenting data in writing, tables, and graphs

 Lecture participants make tabulations from the data provided in the learning flow

 Lecture participants make graphs from the tabulated data

 Lecture participants describe the graph from the tabulated data METHOD OF DUTY WORK

 Using excel applications in the data processing

 The calculation uses a formula

 Data description is done in excel application

 Stored in the form of an excel file extension. xls x

 Give the file a name of the way: Tgs3_NamaL Complete_NIM. Xls x.

 The task file is uploaded according to the time specified in Sikola Task The third task FORM AND OUTSIDE FORMAT

a. Garapan object: tabulation and presentation of data b. Output Form:

Softcopy excel extension . xls x

ETC -

REFERENCES

3. Baso, YS (2016). Online Arabic Learning Model based on Learning Management System. Makassar:

Hasanuddin University Arabic Literature Study Program.

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TITLE OF TASKS

Calculates the size of data centering and distribution SUB COURSE LEARNING OUTCOMES

Sub-CPMK4 . Able to compute centralization and dispersion size from data, through manual computation and computation;

 Students determine the mean, median, and mode of the task data in the learning path METHOD OF DUTY WORK

 Using excel applications in data processing

 Stored in the form of an excel file extension . xlsx

 Give the file a name of the form: Tgs4_NamaL Lengkap_NIM.xlsx .

 The task file is uploaded according to the time specified in Sikola Task The fourth task FORM AND OUTSIDE FORMAT

a. Garapan object: centralization and dissemination of data b. Output Form:

Softcopy office excel extension . xls x

INDICATORS, CRETERIA AND WEIGHT OF ASSESSMENT According to the assessment rubric, attachment 4 IMPLEMENTATION SCHEDULE

ETC -

REFERENCES

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TITLE OF TASKS Two variable correlation

Sub-CPMK5 . Be able to calculate the correlation between two variables and their applications DESCRIPTION OF TASKS

 Students calculate the spearman rank correlation and the Pearson product correlation from the assignment data provided in the learning path

 Using excel applications in data processing

 Stored in the form of an excel file extension . xlsx

 Give the file a name of the form: Tgs5_NamaL Lengkap_NIM.xlsx .

 The task files are uploaded according to the time specified in Sikola Task The fifth task FORM AND OUTSIDE FORMAT

a. Arable Object: Correlation, variable b. Output Form:

Softcopy office word extension . xls x

INDICATORS, CRETERIA AND WEIGHT OF ASSESSMENT

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In accordance with the assessment rubric, attachment 5 IMPLEMENTATION SCHEDULE

ETC -

REFERENCES

SUPPORTING LECTURER Sri Astuti Tamrin, S.Si., M.Stat., P.hD.

Group Will be announced

TITLE OF TASKS

The relationship between two independent and dependent variables SUB COURSE LEARNING OUTCOMES

Sub-CPMK6 . Able to find the relationship between the independent variable and the dependent variable using regression

 Perform linear regression analysis from the first data in the learning path using the SPSS application

 Perform multiple regression analysis from the second data in the learning path using the SPSS application

 Each group downloads task data in the learning path

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 Perform analysis using the SPSS application

 The results of the SPSS analysis are transferred to the word and then described, don't forget to provide a cover with the names of the group members

 Give the file name word format Tgs6_Nama Group .docx

 The task file is uploaded according to the time specified in Sikola Task The sixth task FORM AND OUTSIDE FORMAT

a. Articulated Object: Liner and multiple regression b. Output Form:

Softcopy word extension. docx

ETC -

REFERENCES

SUPPORTING LECTURER Sri Astuti Tamrin, S.Si., M.Stat., P.hD.

Group Will be announced

TITLE OF TASKS

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T test

Sub-CPMK7 . Able to analyze the relationship between 2 variables using variation analysis, especially the t test

 Each group conducted a t-test on the task data provided in the learning path METHOD OF DUTY WORK

 Each group downloads task data in the learning path

 Perform analysis using the SPSS application

 The results of the SPSS analysis are transferred to the word and then described, don't forget to provide a cover with the names of the group members

 Name the word file with the format Tgs7_NamaKel grup.docx

 The task file is uploaded according to the time specified in Sikola Task The seventh task FORM AND OUTSIDE FORMAT

a. Cultivated Object: Test t b. Output Form:

Softcopy extension .docx

INDICATORS, CRETERIA AND WEIGHT OF ASSESSMENT According to the appendix 7 assessment rubric

ETC -

REFERENCES

Attachment 1. Rubric for Assessment of Sub-CPMK 1

Indicators: M endemonstrasikan one plagiarism applications (eg plagscout) 5: Very good, 4: Good, 3: Poor, 2: Not good, 1: Very bad

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No. Assessment Aspects Score

(a) (b) (c) (d) (e)

1 The concept of integrity 5 4 3 2 1

2 Relationship of integrity and plagiarism 5 4 3 2 1

3 Tutorial on using plagiarism application 5 4 3 2 1

Total

Overall Score = (column total (a) + column total (b) + column total (c) + column total (d) + column total (e))

Student score = (Overall score X 100) / 15

Appendix 2. Assessment Rubric for Sub-CPMK 2

Indicators: Limitations explain three things, namely: theory / concept, benefits, and the role of statistics in various fields and data collection techniques 5: Very good, 4: Good, 3: Poor, 2: Not good, 1: Very bad

(a) (b) (c) (d) (e)

1 Theory / concept 5 4 3 2 1

2 Benefits 5 4 3 2 1

3 The role of statistics in various fields 5 4 3 2 1

4 Data collection technique 5 4 3 2 1

Total

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Appendix 3. Assessment Rubric for Sub-CPMK 3

Indicators: The accuracy of explaining and practicing descriptive statistics by tabulating data, presenting data and presenting it in writing, tables and graphics 5: Very good, 4: Good, 3: Poor, 2: Not good, 1: Very bad

(a) (b) (c) (d) (e)

1 Tabile data 5 4 3 2 1

2 Presentation of data 5 4 3 2 1

3 Tables / graphs 5 4 3 2 1

4 Data description 5 4 3 2 1

Total

Indicators: Accuracy of calculating and practicing centralizing and distributing measures of data in multiple fields 5: Very good, 4: Good, 3: Poor, 2: Not good, 1: Very bad

(a) (b) (c) (d) (e)

1 Mean 5 4 3 2 1

2 Median 5 4 3 2 1

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3 Mode 5 4 3 2 1

Total

Attachment 5. Sub-CPMK Assessment Rubric 5

Indicator: The accuracy of calculating the spearman rank correlation and the Pearson product moment correlation 5: Very good, 4: Good, 3: Poor, 2: Not good, 1: Very bad

(a) (b) (c) (d) (e)

1 Spearman rank 5 4 3 2 1

2 Pearson product correlation 5 4 3 2 1

Total

Student score = (overall score X 100) / 10

Annex 7 . Assessment Rubric of Sub-CPMK 6

Indicator: The accuracy of analyzing data using linear and multiple regression using software 5: Very good, 4: Good, 3: Poor, 2: Not good, 1: Very bad

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(a) (b) (c) (d) (e)

1 Use of Software 5 4 3 2 1

2 Linear regression 5 4 3 2 1

3 Multiple registration 5 4 3 2 1

Total

Indicator: The accuracy of analyzing data using analysis of variance with t test of one variable and two variables using software 5: Very good, 4: Good, 3: Poor, 2: Not good, 1: Very bad

(a) (b) (c) (d) (e)

1 T test analysis 5 4 3 2 1

2 T test results 5 4 3 2 1

3 Description of the results of the t test 5 4 3 2 1

Total

Student score = (overall score X 100) / 15

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