FORECASTING METHOD PORTFOLIO BACHELOR DEGREE …

PORTFOLIO BACHELOR DEGREE PROGRAM SARJANA

Departement of

Mathematics

Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember

FORECASTING METHOD

1

1. FORCASTING METHOD PORTFOLIO

NAMA MK : Metode Peramalan

KODE MK : KM184821

SEMESTER : 8

NAMA DOSEN / TIM : Endah Rokhmati MP, S.Si., M.T., Ph.D

NAMA KOORDINATOR MK : Endah Rokhmati MP, S.Si., M.T., Ph.D

COURSE : Forcasting Method

CODE : KM184821

SEMESTER : 8

LECTURER / TEAM : Endah Rokhmati MP, S.Si., M.T., Ph.D

COURSE COORDINATOR : Endah Rokhmati MP, S.Si., M.T., Ph.D

2

I. Halaman Pengesahan / Endorsement Page

EVALUASI KURIKULUM 2018-2023 CURRICULUM EVALUATION 2018-2023 Nama Fakultas: Fakultas Sains dan Analitika Data Faculty Name: Faculty of Science And Data Analitycs Nama Prodi: Matematika Program Name: Mathematics Nama MK: Metode Peramalan Course: Forecasting Method

KM184821

Sem: 8

Kode/Code: KM184821

Bobot sks /Credits(T/P): 2 Rumpun MK: Matematika Terapan Cluster Course: applied Mathematics

Smt: 8

OTORISASI AUTHORIZATION

Penyusun Compiler Endah Rokhmati MP, S.Si., M.T., Ph.D

Koordinator RMK Cluster Coordinator Prof. Dr. Basuki Widodo, M.Sc

Kepala Departemen Head of Department Subchan, S.Si., M.Sc., Ph.D

TTD/SIGN.

TTD/SIGN. TTD/SIGN.

Tanggal/Date: ….. Tanggal/Date: ….. Tanggal/Date: …..

3

II. CPL yang dibebankan pada MK / PLO Charged to The Course

CPL Prodi / PLO

Sub CP Sub LO

CPL 1 PLO 1

CPL 2 PLO 2

CPL 3 PLO 3

CPL 4 PLO 4

CPL 5 PLO 5

CPL 6 PLO 7

CPL 7 PLO 7

Sub CP MK 1 Sub CLO 1

X

Sub CP MK2 Sub CLO 2

X X X


X X X

III. Bobot CPL yang dibebankan pada MK / Load of PLO Charged to The Course

Bobot CPL Prodi pada setiap Sub CP MK Load of PLO Charged to The Course

Total Sub CP Sub LO

CPL 1 PLO 1

CPL 2 PLO 2

CPL 3 PLO 3

CPL 4 PLO 4

CPL 5 PLO 5

CPL 6 PLO 7

CPL 7 PLO 7

Sub CP MK 1 Sub CLO 1

0.10 0.10


0.15 0.15 0.15 0.45


0.15 0.15 0.15 0.45

Total 0.10 0.30 0.30 0.30 1.00

4

IV. Rencana Penilaian / Asesmen & Evaluasi RAE, dan Rencana Tugas /

Assessment & Evaluation Plan (A&EP) and Assignment Plan

RENCANA ASSESSMENT & EVALUASI ASSESSMENT & EVALUATION PLAN Bachelor Degree Program of Mathematics Department Faculty of Science and Data Analytics MK : Metode Peramalan Course: Forecasting Method

RA&E A&EP

Write Doc Code

Kode/Code: KM184821

Bobot sks /Credits (T/P): 2 sks Rumpun MK: Matematika Terapan Course Claster: Applied Mathematics

Smt: 8

OTORISASI AUTHORIZATION

Penyusun RA & E Compiler A&EP Endah Rokhmati MP, S.Si., M.T., Ph.D

Koordinator RMK Course Cluster Coordinator Prof. Dr. Basuki Widodo, M.Sc

Ka PRODI Head of Dept. Subchan, S.Si., M.Sc., Ph.D

Mg ke/ Week

(1)

Sub CP-MK / Lesson Learning Outcomes (LLO)

(2)

Bentuk Asesmen (Penilaian)

Form of Assessment (3)

Bobot / Load (%)

(4)

1

Mahasiswa mampu :

menjelaskan konsep dasar, pengertian dasar dan peranan metode peramalan di masalalu, saat ini dan yang akan datang

Menjelaskan konsep dasar peramalan

menjelaskan pengertian dasar peramalan

menjelaskan kegunaan peramalan

menjelaskan peranan metode peramalan di masalalu, saat ini dan yang akan datang. Students are able to:

explain the basic concepts, basic understanding and role of forecasting methods in the past, present and future

Explain the basic concepts of forecasting

explain the basic understanding of forecasting explain the use of forecasting

explain the role of forecasting methods in the past, present and future

Non-Test: Melakukan resume dari perkuliahan Non-test: Make summary of course

20

2 Mahasiswa mampu : Non-Test: Melakukan resume dari perkuliahan

5

Mg ke/ Week

(1)


(2)



Bobot / Load (%)

(4)

menjelaskan dasar-dasar peramalan kuantitatif, dasar-dasar probabilistik dan statistika inferensia sebagai penunjang metode peramalan kuantitatif

menjelaskan dasar-dasar peramalan kuantitatif.

menjelaskan dasar-dasar probabilistik penunjang metode peramlan

menjelaskan statistik inferensia penunjang metode peramalan

Students are able to: explain the basics of quantitative forecasting, basics of probabilistic and inferential statistics as supporting quantitative forecasting methods

explain the basics of quantitative forecasting. explain the basics of probabilistic support for the

forecasting method

explain inference statistics to support the forecasting method

Non-test: Make summary of course

3-5 Mahasiswa mampu :

mendapatkan model terbaik suatu data runtun waktu dengan metode rata-rata bergerak untuk pola stationer dan trend linier

mendapatkan model terbaik suatu data runtun waktu dengan metode rata-rata bergerak untuk pola stationer

mendapatkan model terbaik suatu data runtun waktu dengan metode rata-rata bergerak untuk pola trend linier

Students are able to:

get the best model of time series data with the moving average method for stationary patterns and linear trends

get the best model of time series data with the moving average method for stationary patterns

get the best model of time series data with the moving average method for linear trend patterns

Non-Test:

Melakukan resume dari perkuliahan

Non-test:

Make summary of course

9 Evaluasi Tengah Semester / Mid Semester Evaluation 30

10-15 Mahasiswa dapat menganalisis plot ACF, plot PACF dan Transformasi Box-Cox untuk menetapkan model sementara dengan metode Box-Jenkins.

Mahasiswa mampu mendapatkan model terbaik suatu data runtun waktu dengan metode Box-Jenkins (ARIMA).

Non-Test:


Presentasi makalah Non-test:


Presentation of paper

20

6

Mg ke/ Week

(1)


(2)



Bobot / Load (%)

(4)

Students can analyze ACF plots, PACF plots and Box-Cox transformations to establish a provisional model using the Box-Jenkins method.

Students are able to get the best model of time series data using the Box-Jenkins method (ARIMA)

16 Evaluasi Akhir Semester / Final Semester Evaluation 30

Total bobot penilaian 100%

7

V. Penilaian Sub CP MK / CLO Assessment

No NRP

Mahasiswa Nama Mahasiswa

Nilai Sub CP MK 1

Nilai Sub CP MK 2

Nilai Sub CP MK 3

Keterangan (lulus / Tidak Lulus)

Action Plan

1 6111540000079 RAMADHANI PRASYANTO 5.93 26.685 26.685 L

2 6111640000004 NITA TRI AGUSTIN 8.6 38.7 38.7 L

3 6111640000026 HENGKY KURNIAWAN 8.6 38.7 38.7 L

4 6111640000074 CHOIRIYAH SAPTA AGUSTINA 8.51 38.295 38.295 L

5 6111640000085 YOVIA GALUH SALSABILLA 8.39 37.755 37.755 L

6 6111640000089 MUHAMMAD RIZAL FANANI 8.51 38.295 38.295 L

7 6111640000110 HASNA KHALISHFI YASYFA 8.45 38.025 38.025 L

8 6111640000111 FATIMAH AZZAHRA ARKHAM 8.6 38.7 38.7 L

9 6111640000123 ALVARO BASILY SUPRIYANTO 8.45 38.025 38.025 L

10 6111740000003 NASICHAH 8.41 37.845 37.845 L

11 6111740000009 AGUSTINI FAJARIYANTI NINGSIH 8.47 38.115 38.115 L

12 6111740000013 NADA FITRIANI AZZAHRA 8.5 38.25 38.25 L

13 6111740000014 MIFTAKHUL JANAH SEFTIA A. 8.51 38.295 38.295 L

14 6111740000016 BRYLLIAN REYGA AKBAR P. 8.44 37.98 37.98 L

15 6111740000018 AZIZAH WAHYANTIKA 8.42 37.89 37.89 L

16 6111740000019 ADINDA OKTAVIANI 8.39 37.755 37.755 L

17 6111740000024 KRISTIAN DWI RATNA DEWI 8.41 37.845 37.845 L

18 6111740000027 KAROHMATUL AMALIA MS 8.5 38.25 38.25 L

19 6111740000048 SEKAR NUR SARASWATI 8.5 38.25 38.25 L

20 6111740000049 ALDI EKA WAHYU WIDIANTO 8.59 38.655 38.655 L

21 6111740000065 LARAS BERLIYANI PUTERI 8.59 38.655 38.655 L

22 6111740000072 RAHMARANI PUSPITA DEWI 8.39 37.755 37.755 L

23 6111740000073 SITI MASRIYAH 8.48 38.16 38.16 L

24 6111740000074 SINTA HIJJATUL ULYA 8.41 37.845 37.845 L

25 6111740000083 MUHAMMAD TSAQIF 8.36 37.62 37.62 L

8

VI. Penilaian CPL yang dibebankan pada MK berdasarkan pada nilai Sub CP MK / PLO assessment charged to the course based on

CLO assessment

No NRP

Mahasiswa Nama Mahasiswa Nilai CPL 2 Nilai CPL 3 Nilai CPL 4 Nilai CPL 5

Keterangan (lulus / Tidak Lulus)

Action Plan

1 6111540000079 RAMADHANI PRASYANTO 83 58.05 54.77 84.88 Lulus

2 6111640000004 NITA TRI AGUSTIN 83 86.16 86.18 84.88 Lulus

3 6111640000026 HENGKY KURNIAWAN 83 86.16 86.18 84.88 Lulus

4 6111640000074 CHOIRIYAH SAPTA AGUSTINA 83 85.21 85.12 84.88 Lulus

5 6111640000085 YOVIA GALUH SALSABILLA 83 83.95 83.71 84.88 Lulus

6 6111640000089 MUHAMMAD RIZAL FANANI 83 85.21 85.12 84.88 Lulus

7 6111640000110 HASNA KHALISHFI YASYFA 83 84.58 84.41 84.88 Lulus

8 6111640000111 FATIMAH AZZAHRA ARKHAM 83 86.16 86.18 84.88 Lulus

9 6111640000123 ALVARO BASILY SUPRIYANTO 83 84.58 84.41 84.88 Lulus

10 6111740000003 NASICHAH 87 83.95 83.71 86.38 Lulus

11 6111740000009 AGUSTINI FAJARIYANTI N. 87 83.95 83.71 86.38 Lulus

12 6111740000013 NADA FITRIANI AZZAHRA 87 83.95 83.71 86.38 Lulus

13 6111740000014 MIFTAKHUL JANAH SEFTIA A. 83 85.21 85.12 84.88 Lulus

14 6111740000016 BRYLLIAN REYGA AKBAR P. 87 83.95 83.71 86.38 Lulus

15 6111740000018 AZIZAH WAHYANTIKA 83 84.58 84.41 84.88 Lulus

16 6111740000019 ADINDA OKTAVIANI 83 83.95 83.71 84.88 Lulus

17 6111740000024 KRISTIAN DWI RATNA DEWI 87 83.95 83.71 86.38 Lulus

18 6111740000027 KAROHMATUL AMALIA MS 87 83.95 83.71 86.38 Lulus

19 6111740000048 SEKAR NUR SARASWATI 87 83.95 83.71 86.38 Lulus

20 6111740000049 ALDI EKA WAHYU WIDIANTO 87 84.84 85.92 86.38 Lulus

21 6111740000065 LARAS BERLIYANI PUTERI 87 84.84 85.92 86.38 Lulus

22 6111740000072 RAHMARANI PUSPITA DEWI 83 83.95 83.71 84.88 Lulus

23 6111740000073 SITI MASRIYAH 83 85.21 85.12 84.88 Lulus

24 6111740000074 SINTA HIJJATUL ULYA 87 83.95 83.71 86.38 Lulus

25 6111740000083 MUHAMMAD TSAQIF 83 85.21 85.12 84.88 Lulus

9

VII. Tindakan hasil Evaluasi untuk Perbaikan / Action plan evaluation for

improvement

Unsur yang di evaluasi

CPL Prodi

CP MK Dosen

Sub CP MK Dosen

Model Pembelajaran Prodi + Dosen

Bentuk asesmen Prodi + Dosen

10

Lampiran / Enclosure

A. Rencana Tugas & Rubrik Penilaian / Assignment plan and assessment rubric

Mg ke/ Week

(1)


(2)



Bobot / Load (%)

(4)

1

Mahasiswa mampu :

menjelaskan konsep dasar, pengertian dasar dan peranan metode peramalan di masalalu, saat ini dan yang akan datang

Menjelaskan konsep dasar peramalan

menjelaskan pengertian dasar peramalan

menjelaskan kegunaan peramalan

menjelaskan peranan metode peramalan di masalalu, saat ini dan yang akan datang. Students are able to:

explain the basic concepts, basic understanding and role of forecasting methods in the past, present and future

Explain the basic concepts of forecasting

explain the basic understanding of forecasting explain the use of forecasting

explain the role of forecasting methods in the past, present and future


20

2 Mahasiswa mampu :

menjelaskan dasar-dasar peramalan kuantitatif, dasar-dasar probabilistik dan statistika inferensia sebagai penunjang metode peramalan kuantitatif

menjelaskan dasar-dasar peramalan kuantitatif.

menjelaskan dasar-dasar probabilistik penunjang metode peramlan

menjelaskan statistik inferensia penunjang metode peramalan

Students are able to: explain the basics of quantitative forecasting, basics of probabilistic and inferential statistics as supporting quantitative forecasting methods

explain the basics of quantitative forecasting. explain the basics of probabilistic support for the

forecasting method

explain inference statistics to support the forecasting method


3-5 Mahasiswa mampu : Non-Test:

11

Mg ke/ Week

(1)


(2)



Bobot / Load (%)

(4)

mendapatkan model terbaik suatu data runtun waktu dengan metode rata-rata bergerak untuk pola stationer dan trend linier

mendapatkan model terbaik suatu data runtun waktu dengan metode rata-rata bergerak untuk pola stationer

mendapatkan model terbaik suatu data runtun waktu dengan metode rata-rata bergerak untuk pola trend linier

Students are able to:

get the best model of time series data with the moving average method for stationary patterns and linear trends

get the best model of time series data with the moving average method for stationary patterns

get the best model of time series data with the moving average method for linear trend patterns


Non-test:


9 Evaluasi Tengah Semester / Mid Semester Evaluation 30

10-15 Mahasiswa dapat menganalisis plot ACF, plot PACF dan Transformasi Box-Cox untuk menetapkan model sementara dengan metode Box-Jenkins.

Mahasiswa mampu mendapatkan model terbaik suatu data runtun waktu dengan metode Box-Jenkins (ARIMA).

Students can analyze ACF plots, PACF plots and Box-Cox transformations to establish a provisional model using the Box-Jenkins method.

Students are able to get the best model of time series data using the Box-Jenkins method (ARIMA)

Non-Test:


Presentasi makalah Non-test:


Presentation of paper

20

16 Evaluasi Akhir Semester / Final Semester Evaluation 30

Total bobot penilaian 100%

12

B. Rubrik Atau Marking Scheme Assessment / Rubric or marking Marking Scheme

Assessment

13

C. Bukti – soal (Asesmen dan Tugas) / Evidence of assignment and assessment

1. Mid Semester Evaluation

2. Final Semester Evaluation

14

D. Bukti jawaban soal dan Hasil Tugas / Evidence of solution and assignment result

1. Mid Semester Evaluation

15

2. Final Semester Evaluation

FORECASTING METHOD PORTFOLIO BACHELOR DEGREE …

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