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Lecture Portfolio
UNIVERSITAS BRAWIJAYA
FACULTY OF MATHEMATICS AND NATURAL SCIENCES
DEPARTMENT OF STATISTICS /
UNDERGRADUATE STATISTICS STUDY PROGRAMME
Course Name: Categorical Data Analysis
Course Code:
MAS62221
Laboratory:
Biostatisics
Semester : Even Lecturer Dr. Suci Astutik, S.Si., M.Si.
Introduction
Categorical Data Analysis is a compulsory course in the 4th semester in the biostatistics laboratory. The supporting courses are regression analysis and an introduction to probability theory. This course discusses a lot of statistical analysis methods on categorical data with a probability approach and the concept of regression models that are widely applied in life sciences. The learning strategy is carried out through understanding the concept of categorical data analysis theory and its application in life sciences. Learning evaluation is carried out to determine the level of understanding of students both in theory and application.
The student understanding is not only determined by the learning process (material and method of delivering the material) but also determined by the character of each student who is participating in this course. Therefore, it is necessary to have the teaching lecturer skill in designing learning strategies based on the results of the evaluation, suggestions, and obstacles of the students during the class.
1 Purpose
General Purpose:
This course is taught so that students can understand, explain and perform contingency table analysis and association test from contingency tables, understand and explain the basic principles of categorical data analysis models with binary response variables to be developed into polytomous response variables, understand and explain the probability model for categorical data (binomial, multinomial, Poisson), joint probability, marginal and conditional probability, as well as being able to predict parameters and testing hypotheses, have the skills to model categorical data with logistic, probit, and Gompertz with binary response variables for contingency tables, and at the same time, be able to perform parameters estimation and testing hypotheses and validating models, having the skills to model categorical data with log-linear for contingency tables and at the same time being able to estimate parameters and test hypotheses and choose the best model.
This course is taught to support the following Intended Learning Outcomes (ILO):
- ILO 1: The students are able to master basic scientific concepts and statistical analysis methods applied on computing, social science, humanities, economics, industry and life science.
- ILO 3: The students are able to manage, analyze, and complete the real case using statistical method on computing, social humanities, economics, industry and life science that helped by software, then present and communicate the results.
- ILO 4: The students are able to master at least two statistical software, including
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based on open source.
- ILO 5: The students are able to apply logical, critical, systematic, and innovative thinking independently when applied to science and technology that contain humanities values, based on scientific principles, procedures and ethics with excellent and measurable results.
- ILO 6: The students are able to take appropriate decisions to solve the problems expertly, based on the information and data analysis.
- ILO 8: The students are able to apply and internalize the spirit of independence, struggle, entrepreneurship, based on values, norms, and academic ethics of Pancasila in all aspects of life.
While the learning outcomes of the Categorical Data Analysis Course (Course Learning Outcome - CLO) are:
- CLO 1: Students are able to apply the principles of parameter estimation Students able to understand and explain basic concepts of nonparametric and use it for two and k population testing
- CLO 2: Students able to understand, explain, and do contingency table analysis, association test of contingency table
- CLO 3: Students able to understand and explain basic concepts of categorical data analysis model with binary response variables to be developed into the polytomus response variable
- CLO 4: Students are able to understand and explain probability model for categorical data (binomial, multinomial, Poisson), joint probability, marginal probability, and conditional probability and are able to do parameter estimation and hypothesis testing
- CLO 5: Students have skills to model categorical data using logistic, probit, and Gompertz with binary response variables for contingency table and do parameter estimation and hypothesis testing and model validation
- CLO 6: Students have skills to model categorical data using log-linear for contingency table and are able to do parameter estimation and hypothesis testing and choose the best model
Each Course Learning Outcomes (CLO) provide support for the Study Program Learning Outcomes (ILO) with a certain percentage, which details can be seen in the relationship matrix between CLO and ILO of Categorical Data Analysis, which is presented in Table 1.
Table 1. Relationship Matrix between CLO and ILO of Categorical Data Analysis
ILO1 ILO2 ILO3 ILO4 ILO5 ILO6 ILO7 ILO8
CLO1 0.3 0 0.2 0.125 0.125 0.125 0 0.125
CLO2 0.3 0 0.2 0.125 0.125 0.125 0 0.125
CLO3 0.3 0 0.2 0.125 0.125 0.125 0 0.125
CLO4 0.3 0 0.2 0.125 0.125 0.125 0 0.125
CLO5 0.3 0 0.2 0.125 0.125 0.125 0 0.125
CLO6 0.3 0 0.2 0.125 0.125 0.125 0 0.125
2 Teaching Strategy
Lectures in this semester are offline at the beginning of the even semester until before the midterm exam, then after the midterm exam (after March 16, 2020), all lectures are
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online including Categorical Data Analysis course. This is due to the Covid-19 pandemic. This course presents materials about the understanding of theoretical and applied concepts in categorical data. Learning strategies are divided into two, namely offline and online, as follows:
(a) Offline (before the midterm exam)
- At the beginning of the class, a class contract is delivered, which includes:
materials, references, assessment system, class rules.
- Provide lecture material at the beginning of the class or the latest one week before the lecture material is given and ask students to study the material.
- Give some questions before delivering the material to find out how far students prepare lecture material.
- Explain the material in detail both in theory and with case examples.
- Allow students to ask questions.
- Giving group assignments, such as ask the students to find problems in any case and solving them according to the theory being studied.
- Discuss the group assignments and ask for comments or suggestions from other students.
(b) Online (after the midterm exam)
- Coordinate with the class coordinator and asking all students in the Category Data Analysis course to join the Google classroom.
- Provides access to material/assignments/quizzes/midterm exam/final exam before classes (in pdf/ppt files) in Google Classroom.
- Ask students to study each lecture material before online class begin
- Provide detailed explanations via zoom meetings or Google meetings if students experience problems or difficulties in studying the material.
- Allow students to ask questions.
- Ask for suggestions from students during the material presentation session regarding the need for the lecturer to re-explain or slow down the speed in explaining.
- Prepare assignments in google classroom at the end of each sub-chapter of the material and submit it no later than one day before the next week's class.
Students are allowed to open notes or discuss with other students.
- Provide a post test (one or two relevant cases) to measure the material understanding that has been explained. Unlike the assignment, post test is given a maximum of 15 minutes of time to do it.
- Discuss the assignments or post tests, if there are difficulties or obstacles in their works.
- Activate the role of assistant to provide explanations and additional exercises to students in tutorial class.
3 Lecture Management
This course is a 3 credit points course with a tutorial class. The scheduled meetings are once a week (3 times 50 minutes) for 14 weeks and 8 tutorial classes by the assistant (50 minutes each). Midterm Exam is scheduled after 7 meetings, while the Final Exam is scheduled after the 14th meeting.
Lecture:
- Schedule: Meetings are scheduled every Friday, 7.30 - 10.15 WIB.
- At each meeting, students are allowed to ask questions about the material at the previous meeting. Furthermore, when offline, the lecturer gives one or two questions to find out whether students have studied the material before the class
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begin. When online, the lecturer will explain in detail about the material that he/she feels needs a more detailed explanation or if students experience problems or obstacles in learning the material. When offline, the lecturer presents material on the first two credits, then one last credit is used by students to exercises, applying the concepts that have been explained in different cases.
Whereas when online, exercises are given as assignments at the end of the meeting and will be discussed at the next meeting.
- Each meeting has specific learning outcomes according to the course materials.
To measure its outcomes, a post-test or assignment has been designed. The results of the post-test or assignment are used as evaluation to re-explain the material deemed necessary at the next meeting. As designed in the Semester Learning Plan, this course provides several assessments with the material and weight of each assessment of the final score as presented in Table 2. All of the assessments above must be done independently by students.
Tutorial Class:
- The assistants of tutorial class are: Femy Rahayu Quientania and Ratih Kartika Rahmatulnissa
- Tutorial class is offline (before midterm exam) and online (after midterm exam), because it does not require a computer laboratory
- It aims to strengthen the understanding of the course material through discussion sessions with assistants and exercises.
- The material for each week is the result of discussion with the lecturer, according to the speed of explaining the lecture material in each week.
- Schedule: Tutorial class is held every Tuesday, 9.20 - 10.20 WIB.
- The tutorial class is held 8 times, and can only start at 5th week (policy of the study program is to give time for the selection of assistants and the accumulation of material from lecturers) and is not implemented during midterm exam week (8 and 9) with details of materials every week as follows:
1. 5th week: Contingency Table Analysis
2. 6th week: Association Test on Contingency Table 3. 7th week: Linear Probability Model
4. 10th week: Assignment I
5. 11th week: Discuss the answers of assignment I given the previous week 6. 12th week: Probit and Logistic Regression
7. 13th week: Gompit Regression and assignment II 8. 14th week: Log-Linear Regression
4 Course Material
- Introduction to parametric statistics and nonparametric testing for two and k population
- Contingency table analysis
- Association test of contingency table
- Basic principles of using categorical data analysis model with binary response variable to be developed into polytomus response variable, response variable data without normal distribution assumption
- Probability model for categorical data (binomial, multinomial, Poisson), joint probability, marginal probability, and conditional probability and parameter and hypothesis testing
- Logistic, binary, and Gompertz regression model with binary response variable for contingency table, parameter estimation using weighted MSE, hypothesis
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testing, ad model validation
- Log-linear model for contingency table, parameter estimation, and hypothesis testing, and choosing the best model (model validation)
The suitability between the course material designed in the curriculum and its practice can be seen in Appendix 1.
5 Lecture Participants
This course is a compulsory course which is participated by students of the Undergraduate Statistics Study Program of FMIPA UB, class 2018. Class B of Categorical Data Analysis was attended by 1 students.
6 Attendance Percentage
Lecturer attendance is 100% while the average of student attendance is 99%.
7 System Evaluation
- Evaluation every two weeks is done through assignments. Each assignment includes two meetings. Assignments are given every two weeks because on average one material topic is completed in two weeks. The purpose of this evaluation is to explore students' understanding of whether it is in accordance with the purposes of each week's meeting. The results of the assignment were used by the lecturer to re-discuss material that he/she felt was lacking in understanding.
- Evaluate some materials through Quizzes that measure the students' understanding from 3 or 4 meetings. The types of questions resemble the midterm/final exam questions, so the students have an idea about the preparation for the midterm/final exam. The midterm exam and final exam are online.
- Evaluation of the material up to mid-semester through the midterm exam, which is held on a scheduled basis.
- Evaluation of the material after mid-semester to the end of the semester through the final exam, which is held on a scheduled basis.
- Evaluation of the tutorial class is given by the assistant. The assessments are the activeness and the understanding of the material from the assignments. The assistants determine the types of questions on the assignment after consulting with the lecturer.
- In the week of midterm exam and final exam, all lecture activities are closed, so the students can concentrate midterm/final exam.
- Questions for all types of evaluation are standardized / the same for parallel classes, which is the result of discussion from the teaching team. The material evaluated for each assessment and its weight can be seen in Table 2.
Table 2. Measured assessment and material, as well as the weighting of each assessment of the final score and Course Learning Outcome (CLO)
Assessment Material
Weight to Final Score
CLO
1 CLO 2 CLO 3
CLO 4
CLO
5 CLO 6 Assessment Weight to CLO (Course Learning Outcome) Assignment
1
Contingency
Table Analysis 0.03 1 0 0 0 0 0
Assignment 2
Association
Test 0.03 1 0 0 0 0 0
P2RP-LP3M UB Assignment 3
Linear Probability
Model 0.03 0.5 0.5 0 0 0 0
Assignment 4
Logistic, Probit, Gompit
regression 0.03 0 1 0 0 0 0
Assignment 5
Log-linear
Regression 0.03 0 0 0.5 0.5 0 0
Quiz 1
Contingency Table Analysis;
Association
Test 0.2 0 0 0 0 0.5 0.5
Post Test 1
Logistic
Regression 0.1 0.167 0.167 0.167 0.167 0.167 0.167
Tutorial Class
Contingency Table Analysis;
Association Test; Linear Probability Model;
Logistic, Probit, Gompit regression;
Loglinear
Regression 0.1 0.5 0.5 0 0 0 0
Midterm Exam
Contingency Table Analysis;
Association
Test; 0.2 0 0 0.25 0.25 0.25 0.25
Final Exam
Linear Probability Model;
Logistic, Probit, Gompit regression;
Log-linear
Regression 0.2 1 0 0 0 0 0
8 Class Observation
In the lecture, students actively participate in both asking questions and being willing to come forward to solve cases on the whiteboard. Unfortunately, only about 40%-50% of students participate actively. Indirectly, students already have a pattern in their seating arrangement in the classroom. 40%-50% of students in this active category are students who sit in the two front rows, while students who sit in the 3 back rows are observed to be more passive. Some observations for students who are passive as follows.
- Staring blankly in class, no response when asked about their understanding.
- Just watching the explanation and derivation of formulas done by the lecturer on the whiteboard without taking notes or trying it themself. There are even those who have not opened any notes in the class.
To anticipate the un-uniform speed of understanding, the lecturer rearranged the explanation duration, according to observations about student understanding. Therefore, some materials have been postponed from the due schedule, without reducing the overall material that must be delivered.
Whereas online lectures, student activeness is observed from several students who provide responses or questions online from previously studied material or assignments.
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However, only about 12%-24% of students are active.
9 Learning Outcomes
The learning outcomes of each student are reflected in the scores in each assessment.
These scores, with their respective weights, are processed into final grades, which later, in accordance with the conversion rules, are converted into numerical scores that are printed on the Study Result Card / transcript. Besides being processed into the final score, the scores in each assessment, taking into account the percentage of CLO contributions to each ILO (Table 1) and the weight of each assessment of CLO (Table 2), are processed with the help of OBES software, so each student also has a score in each CLO and ILO.
The description of the score of each CLO can be seen in Table 3 and Figure 1. Figure 1 (a) presents the student average outcome index for each CLO. Figure 1 (b) presents the percentage of students with an outcome score above 60. In both figures, each corner of the pentagon represents each CLO, and the trajectory of the outer pentagon shows the highest outcome. The outer the position of the blue line, the higher the outcome index of CLO.
In accordance with the outcome category presented in Table 4, it can be concluded that:
- Apart from CLO1 (the concept of nonparametric analysis) and CLO2 (the concept of contingency table analysis) which are in the satisfactory category, other CLOs are in the excellent category.
- All percentages of student learning outcomes (CLO1 - CLO6) get an outcome score above 60 and have the same score except for CLO2 (100%). It means that all students understand the concept of contingency table analysis, both theoretically and application.
- All CLOs are in the high percentage category (HIGH) in terms of the number of students with an outcome score above 60.
Table 3. Score Description and Outcome Category for Each CLO of Categorical Data Analysis
CLO1 CLO2 CLO3 CLO4 CLO5 CLO6
Average 76.9 79.5 80.34 80.34 80.34 80.34
Outcom e Categor y
SATISFACTO RY
SATISFACTO RY
EXCELLE NT
EXCELLE NT
EXCELLE NT
EXCELL ENT Number
of Students with CLO>60
37 41 37 37 37 37
Percenta ge of Students with
CLO>60 90.24 100 90.24 90.24 90.24 90.24
Percenta ge Categor
y HIGH HIGH HIGH HIGH HIGH HIGH
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Table 4. CLO/ILO Score Category and Percentage Category of Students with CLO/ILO
>60
CLO/ILO Score Category Percentage Category of Students with CLO/ILO >60
Score ≥ 80 EXCELLENT Percent ≥ 70 HIGH
65 ≤ Score <80 SATISFACTORY 60 ≤ Percent < 70 MEDIUM 50 ≤ Score <65 DEVELOPING 50 ≤ Percent < 60 LOW
0 ≤ Score <50 UNSATISFACTORY Percent < 50 VERY LOW
Figure 1.Visualization (a) outcome index and (b) presentation of students with an outcome>60 in each CLO of Categorical Data Analysis
In addition to the scores for course learning outcomes (CLO), it can also be analyzed the scores of each ILO. The description of the ILO outcomes of this course is presented in Table 5 and Figure 2. Figure 2 (a) presents the average student outcome index for each ILO. Figure 2 (b) presents the percentage of students with an outcome score above 60. In both figures, each corner of the octagon represents each ILO, and the trajectory of the outer octagon shows the highest outcome. The outer the position of the blue line, the higher the outcome index of ILO.
Several things can be concluded from the support of this course for the Study Program Learning Outcomes (ILO):
- All ILO in Satisfactory category, namely:
- ILO 1 - The students are able to master basic scientific concepts and statistical analysis methods applied on computing, social science, humanities, economics, industry and life science, and
- ILO 3 - The students are able to manage, analyze, and complete the real case using statistical method on computing, social humanities, economics, industry and life science that helped by software, then present and communicate the results.
- ILO 4 - The students are able to master at least two statistical software, including based on open source.
- ILO 5 - The students are able to apply logical, critical, systematic, and innovative thinking independently when applied to science and technology that
(a) (b)
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contain humanities values, based on scientific principles, procedures and ethics with excellent and measurable results.
- ILO 6 - The students are able to take appropriate decisions to solve the problems expertly, based on the information and data analysis.
- ILO 8 - The students are able to apply and internalize the spirit of independence, struggle, entrepreneurship, based on values, norms, and academic ethics of Pancasila in all aspects of life.
Although not all students got an outcome score above 60 for all of the ILO's, the category of the percentage of students with an outcome score above 60 is still HIGH.
Table 5. Score Description and Outcome Category for Each ILO of Categorical Data Analysis
ILO1 ILO2 ILO3 ILO4 ILO5 ILO6 ILO7 ILO8
Weigh ted Avera
ge 78.93 78.93 78.93 78.93 78.93 78.93
Outco me Categ ory
SATISFA CTORY
SATISFA CTORY
SATISFA CTORY
SATISFA CTORY
SATISFA CTORY
SATISFA CTORY Numb
er of Stude nts with ILO>6
0 39 39 39 39 39 39
Percen tage of Stude nts with ILO>6 0
95.12 95.12 95.12 95.12 95.12 95.12
Categ
ory HIGH HIGH HIGH HIGH HIGH HIGH
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Figure 1.Visualization (a) outcome index and (b) presentation of students with an outcome>60 in each ILO of Categorical Data Analysis
10 Obstacle
- Students' understanding of the theory of probability and calculus is lacking, while these two topics are the basis for understanding the material in this course - Online lectures in the second half of the semester, which reduces the flexibility
for lecturers to derive detailed formulas. Online lecture settings also make it difficult for lecturers to explore students' understanding.
11 Grade Distribution
The final score is obtained from the weighting of all components of the assessment as presented in column three in Table 2, while the descriptive statistics of the final score can be seen in Table 6. The mean of the students' final score is 78.9, with less and more than 0 points from the mean. There is one student with the lowest score (55.2) and one student with the highest score (89.4).
Table 6. Descriptive Statistics of Final Score of Categorical Data Analysis 2019/2020
Mean 78.9
Median 81.0
Standard
Deviation 8.0
Range 34.3
Minimum 55.2
Maximum 89.4
After being converted into letter grades under the assessment conversion standards, the distribution of letter grades can be seen in Figure 3. The figure shows that there are more than half of the students with grades above C, the highest percentage is grade C+.
This means that most of the students' understandings are in the sufficient category.
More attention must be paid to students with grades D, in order to repeat this course in the regular semester or the short semester, in order to avoid the rule that D grades are
(a) (b)
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not more than 10% of the total credit points at the end of the study.
Figure 3. Grade Distribution of Categorical Data Analysis 2019/2020
12 Conclusion
- With all the obstacles and basic abilities of students who have been given, the final score obtained still reflects that the strategies and learning methods are well accepted by the majority of students.
- Slightly missed the material delivery schedule plan with its realization, which initially aimed to adjust the speed with the student's ability, instead sacrificing discussion time on certain materials, so the learning outcomes in the material were not optimal.
13 Recommendation
- Lecture in class (offline) is easier to understand
- The students need an explanation when the assignment is finished - Provide time tolerance for students who have network problems.
- Need to consider the midterm exam and final exam questions which tend to be many and difficult
- Provide examples of application in cases, so as not to get confused while doing assignments
- Questions in the quiz and the material provided during lectures need to be more adjusted
- Need to discuss time with students on quiz implementation
- It is necessary to motivate students more, so the students get a high enthusiasm for learning to understand the material being taught.
66%
17%
2%
10%
5%
0% 0% 0%
0%
10%
20%
30%
40%
50%
60%
70%
A B+ B+ C+ C D+ D E
Persen
Nilai
Persen Nilai Huruf
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Appendix 1
Wee
k Plan
Implementation in Week
1 2 3 4 5 6 7 8 and 9 10 11 12 13 14 15 16 17
1 College contract, introduction of certain integrals, use of certain integrals, drawing and calculating the area of an area
Class
Contract
2 Introduction to parametric statistics and nonparametric tests for two and k marginal distribution populations,
Contingen cy table, Associatio n test
3 Contingency table analysis
Independe ncy test for ordinal
data
4 Contingency table analysis
Associat ion test
of continge
ncy table on
small data
5 Test associations on the
contingency table
Detect indepen dency problem s in the continge
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Wee
k Plan
Implementation in Week
1 2 3 4 5 6 7 8 and 9 10 11 12 13 14 15 16 17
ncy table 6 Test associations
on the
contingency table
Detect indepen dency problem s in the continge ncy table
7 Quiz Quiz
8 and 9
Midterm exam
Midterm
exam
10 The basic principles of using categorical data analysis models with binary response variables to be developed into polytomic response variables, response variable data without normal distribution assumptions and probability models for categorical data (binomial, multinomial, poisson), joint, marginal, and
Linear probabilit y model
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Wee
k Plan
Implementation in Week
1 2 3 4 5 6 7 8 and 9 10 11 12 13 14 15 16 17
conditional probability, and parameter testing and hypothesis testing 11 The basic
principles of using categorical data analysis models with binary response variables to be developed into polytomic response variables, response variable data without normal distribution assumptions and probability models for categorical data (binomial, multinomial, poisson), joint, marginal, and conditional probability, and parameter testing and hypothesis testing
Logistic Regressi on
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Wee
k Plan
Implementation in Week
1 2 3 4 5 6 7 8 and 9 10 11 12 13 14 15 16 17
12 Logistic
regression model, probit regression model, and Gompertz regression model with binary response variables for contingency tables, parameter estimation with weighted OLS, hypothesis testing and model validation
Probit regres sion
13 Logistic
regression model, probit regression model, and Gompertz regression model with binary response variables for contingency tables, parameter estimation using weighted OLS, hypothesis testing, and model validation
Gompit Regressi on
14 Log-linear model for contingency tables, parameter estimation, and hypothesis testing
Quiz
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Wee
k Plan
Implementation in Week
1 2 3 4 5 6 7 8 and 9 10 11 12 13 14 15 16 17
as well as selecting the best model (model validation) 15 Log-linear model
for contingency tables, parameter estimation, and hypothesis testing as well as selecting the best model (model validation)
Log- linear regres sion
16 Quiz Quiz
17 Final Exam
Final
Exam Attendance (%)
98% 98% 100% 100% 98% 96% 100
% 100% 100% 100% 100% 100% 100
% 100% 98% 98%
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Appendix 2. List of Grade Details
NIM NAMA ABS1 Q1 T1 T2 T3 T4 T5 P1 UTS1 UAS1
'185090500111033 Diana Safitri 85 77.5 85 85 85 85 85 91.65 62.5 87.5
'185090500111034 Kavilatul Bariroh 85 35 85 85 85 85 85 93.8 55 60
'185090500111035 Lailatul Hasanah 85 35 85 85 85 85 85 89 70 27.5
'185090500111037 M. Samudra Putra Veridayanto 85 90 85 85 85 85 85 93.7 72.5 82.5
'185090500111038 Arif Rachmandani 85 62.5 85 85 85 85 85 93.45 75 82.5
'185090500111039 I Wayan Adi Arta Laksana 85 87.5 85 85 85 85 85 94 100 85
'185090500111040 Adinda Sekar Ayu 85 80 85 85 85 85 85 93.675 77.5 82.5
'185090500111041 Nur Fitachati Diana 85 72.5 85 85 85 85 85 92.8 75 37.5
'185090500111045 Victoria Miranda Yosepha Panjaitan 79 77.5 85 85 85 85 85 91.325 72.5 77.5
'185090500111046 Sonya Milenita Alpreda 85 65 85 85 85 85 85 92.2 87.5 87.5
'185090500111047 Tenti Amelya 85 90 85 85 85 85 85 93 77.5 80
'185090501111002 Balqis Sundusiyah 85 75 85 85 85 85 85 88.7 75 85
'185090501111004 Eka Retnoningati 85 32.5 85 85 85 85 85 89.85 57.5 75
'185090501111005 Jeni Indah Rahmawati 85 67.5 85 85 85 85 85 91.4 77.5 67.5
'185090501111007 Kristina Dwi Yulianti 85 40 85 85 85 85 85 92.3 42.5 42.5
'185090501111008 Leony Kumala Trisnawati 85 85 85 85 85 85 85 90.1 77.5 60
'185090501111012 Amilatul Ilmi 85 80 85 85 85 85 85 91.6 62.5 85
'185090501111013 Ifa Choirun Nisa' 85 85 85 85 85 85 85 94.05 77.5 50
'185090501111014 Indah Retnowati 85 77.5 85 85 85 85 85 90.8 75 80
'185090501111017 Sahiradewi Daffana Parahitasari 85 92.5 85 85 85 85 85 93.4 77.5 82.5
'185090501111018 Elok Pratiwi 85 90 85 85 85 85 85 93.3 77.5 82.5
'185090501111019 Pratiwi Dwi Yanti 85 92.5 85 85 85 85 85 93 77.5 85
'185090501111020 Lailatul Fitria 85 75 85 85 85 85 85 91.95 65 80
'185090501111023 Avida Zahra 85 70 85 85 85 85 85 96.1 77.5 95
'185090501111024 Ira Humairo 85 75 85 85 85 85 85 91.4 77.5 82.5
'185090501111026 Riska Melani Fresdianti 85 80 85 85 85 85 85 95.7 77.5 80
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NIM NAMA ABS1 Q1 T1 T2 T3 T4 T5 P1 UTS1 UAS1
'185090501111030 Isaac Dwadattusyah Haikal Azziz 85 85 85 85 85 85 85 95.9 77.5 90
'185090501111031 Cindy Veronica Rofi`Atin 85 87.5 85 85 85 85 85 93.25 82.5 82.5
'185090501111033 Henida Ratna Ayu Putri 85 95 85 85 85 85 85 93.8 77.5 92.5
'185090501111036 Putu Wiwin Andrini 85 92.5 85 85 85 85 85 92.7 57.5 85
'185090507111012 Intan Nur Alfiah 85 77.5 85 85 85 85 85 92.6 57.5 42.5
'185090507111013 Raihan Masyal Haidar 85 80 85 85 85 85 85 92.9 72.5 77.5
'185090507111014 Carmelia Nabila Permatasari 85 65 85 85 85 85 85 94.6 92.5 82.5
'185090507111015 Tubagus Lintang Trenggono 85 57.5 85 85 85 85 85 93.3 75 80
'185090507111017 Muhammad Farhan Fadhilah 85 90 85 85 85 85 85 92 75 82.5
'185090507111018 Roro Nurfauziah Amini 85 80 85 85 85 85 85 93.2 92.5 80
'185090507111022 Rizky Dwi Saputra 85 82.5 85 85 85 85 85 92.9 75 77.5
'185090507111028 Vincentia Septya Putri 85 70 85 85 85 85 85 91.9 77.5 82.5
'185090507111031 Muhammad Panca Hikmawanto 85 70 85 85 85 85 85 65.75 77.5 77.5
'185090507111032 Reza Panduwaskita 85 80 85 85 85 85 85 90.5 77.5 92.5
'185090519111001 Saphira Kusbandiyah 85 60 85 85 85 85 85 90.2 87.5 82.5
