SEMESTER LEARNING PLAN
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Course Title: Chemometric (KEMO)
MK code: AKM21 357
Credit Weight: 2
Group of Courts: Compulsory
Semester: 5
Prerequisite Course: MD2, KAI1
Lecturer:
Didik Setiyo W., S.Si., M.Si.,
Drs. Abdul Haris, M.Si
Dr. M. Cholid DJ., S.Si., M.Si
Graduate Learning Outcomes (GLO)
| Attitude | GLO1-(S9) | Demonstrate an attitude of responsibility for work in their field of expertise independently. |
| Knowledge | GLO2-( PP2) | Mastering complete operational knowledge of functions, operating standard chemical instruments, and analyzing data and information from these instruments |
| General Skills | GLO 3 -(KU2) | Able to demonstrate independent, quality, and measurable performance. |
| GLO 4 -(KU5) | Able to make decisions regularly in the context of solving problems in their area of expertise, based on the results of analysis of information and data | |
| GLO 5 -(KU7) | Able to be responsible for the achievement of group work results and supervise and evaluate the completion of work assigned to workers under their responsibility | |
| Special skills | GLO 6 -(KK3) | Able to analyze several alternative solutions in the fields of identification, analysis, isolation, transformation, and synthesis of available chemicals and present analysis conclusions for appropriate decision making |
| GLO 7 -(KK4) | Able to use software to determine the structure and energy of macromolecules, software to assist analysis and synthesis in the field of chemistry |
Course Learning Outcomes
| CLO-1 | Able to apply (C3) basic statistical methods in practicum and research data analysis through quantitative data analysis and decision making/experimental conclusions, as well as developing the basis for research and analysis strategies to prove hypotheses |
Course Description
This course focuses on the study of solving chemical analysis problems from a statistical point of view. After taking this course, students are expected to apply basic chemometric skills in analyzing data obtained in chemical practicum and research. The first part of the engineering lecture is designed using examples of significant test cases faced by chemists, especially those related to quantitative data of chemical analysis. The second part focuses more on understanding instrumental data analysis and experimental design.
| Week | Expected ability (Sub-CLO) | Study Materials/ Learning Materials | Learning methods | Student Learning Experience | Time (minutes) | Evaluation | |
| Criteria and Indicators | % | ||||||
| 1 | Students can identify (C1) types of errors related to chemical analysis data problems and can propose (A3) suggestions for overcoming errors with statistics correctly at least 80% | BK21. Basic data problems in chemical analysis (PB1)
a. Analysis problem b. Quantitative analysis error c. Types of errors d. Random and systematic error on e. Systematic error handling |
Discovery learning
Cooperative learning |
Students listen, take notes and ask questions about the subject
Students form small groups and discuss the types of errors (errors) by taking case examples |
FF: 2 x 50
ST: 2 x 60 SS: 2 x 60 |
The accuracy of case classification in error type
Active in discussion |
5 |
| 2-3 | Students can compare (C2) errors in classical analysis and manage (A4) repeated statistical measurement data, correctly at least 80% | BK21. Chemometric in classical analysis (PB2)
a. Mean and standard deviation b. Error distribution c. Average sampling distribution d. Level of confidence (trust) e. Presentation of results f. Error propagation |
Discovery learning
Cooperative learning |
Students listen, take notes, ask questions about the subject, discuss in small groups to calculate the mean, standard deviation, error distribution, side distribution, determine the average confidence level, present measurement data, and calculate error propagation. | FF: 2 x 50
ST: 2 x 60 SS: 2 x 60 |
Truth in comparison of error and Activeness in discussion | 5 |
| 4-6 | Students can test the significance (C4) of data analysis correctly and construct a conclusion (P4) based on the test results accurately at least 80% | BK21. Significance Test (PB3)
a. Comparison of experimental results with known values b. Comparison of the mean of the two samples (data) c. Paired-t test d. – One-tailed and two-tailed test e. F test to compare two standard deviations f. Outlier Test g. Analysis of variance h. Comparing multiple averages i. Chi-squared Test j. Data normality test |
Discovery learning
Cooperative learning Problem Based Learning |
1) Students listen, take notes, and ask questions about the subject.
2) Students work on practice questions and present the results, 3) Students test whether the treatment in the experiment has a significant impact on change, can compare the two methods, conclude whether the two methods are significantly different (significant), |
FF: 2 x 50
ST: 2 x 60 SS: 2 x 60 |
Truth in deciding the type of statistical test
Truth in correlating conclusions and statistical test results |
5 |
| 7 | Students can apply (C3) the chemometric approach in obtaining the correct analytical data quality at least 80% | BK21. Quality of Analytical Measurement (PB4)
a. Sampling and Sampling Strategy b. Separation and estimation of variance with ANOVA c. Introduction to Quality Control (QC) methods d. Collaborative trial |
Discovery learning
Cooperative learning Problem Based Learning |
1) Students listen, take notes, and ask questions about the subject.
2) Students form small groups and discuss the types of errors (errors)by taking the case |
FF: 2 x 50
ST: 2 x 60 SS: 2 x 60 |
||
| 8 | Midterm exam | 1-7 meeting | – | Written exam | 90 | – | |
| 9-11 | Students can evaluate (C5) measurement data with regression and correlation and can calibrate data (P3) and calculate (C3) measurement detection limits, can state specifications (P5) the quality of measurement methods correctly at least 80% | BK21. Calibration Method: Regression and calibration curve in instrumental analysis (PB5)
a. Instrumental analysis calibration chart b. Regression line, correlation coefficient c. Errors in the slope and intercept of a regression line. Calculation of concentration e. Determination of measurement detection limit (LoD) f. Calibration curve: standard addition method g. Applications Line regression on the comparison of two methods h. Weighted regression line i. The intersection of two straight lines j. Calculation of ANOVA and regression k. Nonlinear regression |
Discovery learning
Cooperative learning Problem Based Learning |
1) Students listen, take notes, and ask questions about the subject.
2) Students work on practice questions and present the results, 3) Students criticize the condition of the measurement data and evaluate in-depth qualitative and quantitative, 4) Students make conclusions on the evaluation work above data |
FF: 2 x 50
ST: 2 x 60 SS: 2 x 60 |
Truth in solving linear regression calculations
The truth of the conclusion based on the data |
5 |
| 12-13 | Students can understand (C2) another concept of an analytical approach for data types that are not normally distributed | BK21. Non-parametric statistics (PB6)
a. median b. The Sign Test c. The Wald-Wolfowits runk test d. Wilcoxon Signed rank test e. Non-parametric test two or more samples f. Non-parametric regression |
Discovery learning
Cooperative learning Problem Based Learning |
Students listen, take notes and ask questions about the subject
Students form small groups and discuss statistical tests on non-parametric data |
FF: 2 x 50
ST: 2 x 60 SS: 2 x 60 |
||
| 14-15 | Students can understand (C2) various experimental designs and designs (C6) and implement methods (P2) and manage data (A4) to determine (C4) and estimate (A3) the effect of experimental variables through ANOVA. | BK21.Trial design and optimization (PB7)
a. Randomization and blocking b. Two-way ANOVA c. Latin Square design d. Interaction e. Factorial Design |
Discovery learning
Cooperative learning Problem Based Learning |
1) Students listen, take notes, and ask questions
about the subject 2) Students can understand experimental design based on the parameters varied and controlled and able to process the data obtained to get a conclusion that follow the rules of statistics 3) Students practice solving ANOVA problems independently and diligently Active in discussion |
FF: 2 x 50
ST: 2 x 60 SS: 2 x 60 |
Match between design and data evaluation method | 5 |
| 16 | Final exams | Meeting 9-15 | – | Written exam | 90 | The truth and completeness of the answer to the question | – |
| Total Rating | 100 | ||||||
Reference:
- Miller, C.J. dan Miller N.J., 2005, Statistics and Chemometrics for Analytical Chemistry, edisi ke-5, Ellis Horwood Limited, rt, Hants, GBR
- Meier P.C. dan Zund R.E., 2000, Statistical Methods in Analytical Chemistry, edisi ke-2, John Wiley and Sons, Inc., Toronto
- Skoog D.A., West D.M. dan Holler F.J., 1994, Analytical Chemistry: An Introduction, edisi ke-6, Saunders College Pub., Philadelphia
- Skoog D.A. dan West D.M., 1985, Principles of Instrumental Analysis, edisi ke-3, Saunders College Pub., Philadelphia
Glossary
GLO = Graduate Learning Outcome
CLO = Course Learning Outcomes
FF = Face to Face Learning
ST = Structured tasks
SS = Self Study
