FORECASTING METHOD
PORTFOLIO
BACHELOR DEGREE PROGRAM
SARJANA
Departement of
Mathematics
Faculty of Science and Data Analytics
Institut Teknologi Sepuluh Nopember
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
1
I. Halaman Pengesahan / Endorsement Page
EVALUASI KURIKULUM 2018-2023
CURRICULUM EVALUATION 2018-2023 KM184821
Nama Fakultas: Fakultas Sains dan Analitika Data
Faculty Name: Faculty of Science And Data Analitycs
Nama Prodi: Matematika
Program Name: Mathematics Sem: 8
Nama MK: Metode Peramalan
Course: Forecasting Method
Kode/Code: Bobot sks /Credits(T/P): 2 Rumpun MK: Matematika Smt: 8
KM184821 Terapan
Cluster Course: applied
Mathematics
OTORISASI Penyusun Koordinator RMK Kepala Departemen
AUTHORIZATION Compiler Cluster Coordinator Head of Department
Endah Rokhmati MP, S.Si., Prof. Dr. Basuki Widodo, M.Sc Subchan, S.Si., M.Sc.,
M.T., Ph.D Ph.D
TTD/SIGN. TTD/SIGN. TTD/SIGN.
Tanggal/Date: ….. Tanggal/Date: ….. Tanggal/Date: …..
2
II. CPL yang dibebankan pada MK / PLO Charged to The Course
CPL Prodi / PLO
Sub CP CPL 1 CPL 2 CPL 3 CPL 4 CPL 5 CPL 6 CPL 7
Sub LO PLO 1 PLO 2 PLO 3 PLO 4 PLO 5 PLO 7 PLO 7
Sub CP MK 1 X
Sub CLO 1
Sub CP MK2 X X X
Sub CLO 2
Sub CP MK3 X X X
Sub CLO 3
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 CPL 1 CPL 2 CPL 3 CPL 4 CPL 5 CPL 6 CPL 7
Sub LO PLO 1 PLO 2 PLO 3 PLO 4 PLO 5 PLO 7 PLO 7
Sub CP MK 1 0.10 0.10
Sub CLO 1
Sub CP MK2 0.15 0.15 0.15 0.45
Sub CLO 2
Sub CP MK3 0.15 0.15 0.15 0.45
Sub CLO 3
Total 0.10 0.30 0.30 0.30 1.00
3
IV. Rencana Penilaian / Asesmen & Evaluasi RAE, dan Rencana Tugas /
Assessment & Evaluation Plan (A&EP) and Assignment Plan
RENCANA ASSESSMENT & EVALUASI RA&E
ASSESSMENT & EVALUATION PLAN A&EP
Bachelor Degree Program of Mathematics
Department Write
Faculty of Science and Data Analytics Doc Code
MK : Metode Peramalan
Course: Forecasting Method
Kode/Code: Bobot sks /Credits (T/P): 2 sks Rumpun MK: Matematika Terapan Smt: 8
KM184821 Course Claster: Applied
Mathematics
OTORISASI Penyusun RA & E Koordinator RMK Ka PRODI
AUTHORIZATION Compiler A&EP Course Cluster Coordinator Head of Dept.
Endah Rokhmati MP, S.Si., Prof. Dr. Basuki Widodo, M.Sc Subchan, S.Si.,
M.T., Ph.D M.Sc., Ph.D
Bentuk Asesmen
Mg ke/ Sub CP-MK / Bobot /
(Penilaian)
Week Lesson Learning Outcomes (LLO) Load (%)
Form of Assessment
(1) (2) (4)
(3)
1 Mahasiswa mampu : Non-Test: 20
menjelaskan konsep dasar, pengertian dasar dan Melakukan resume dari
peranan metode peramalan di masalalu, saat ini perkuliahan
dan yang akan datang
Menjelaskan konsep dasar peramalan Non-test:
menjelaskan pengertian dasar peramalan Make summary of course
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
2 Mahasiswa mampu : Non-Test:
Melakukan resume dari
perkuliahan
4
Bentuk Asesmen
Mg ke/ Sub CP-MK / Bobot /
(Penilaian)
Week Lesson Learning Outcomes (LLO) Load (%)
Form of Assessment
(1) (2) (4)
(3)
menjelaskan dasar-dasar peramalan kuantitatif,
dasar-dasar probabilistik dan statistika inferensia Non-test:
sebagai penunjang metode peramalan kuantitatif Make summary of course
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:
mendapatkan model terbaik suatu data runtun Melakukan resume
waktu dengan metode rata-rata bergerak untuk dari perkuliahan
pola stationer dan trend linier
mendapatkan model terbaik suatu data runtun Non-test:
waktu dengan metode rata-rata bergerak untuk Make summary of
pola stationer course
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
9 Evaluasi Tengah Semester / Mid Semester Evaluation 30
10-15 Mahasiswa dapat menganalisis plot ACF, plot PACF Non-Test: 20
dan Transformasi Box-Cox untuk menetapkan Melakukan resume
model sementara dengan metode Box-Jenkins. dari perkuliahan
Mahasiswa mampu mendapatkan model terbaik Presentasi makalah
suatu data runtun waktu dengan metode Box-
Jenkins (ARIMA). Non-test:
Make summary of
course
Presentation of paper
5
Bentuk Asesmen
Mg ke/ Sub CP-MK / Bobot /
(Penilaian)
Week Lesson Learning Outcomes (LLO) Load (%)
Form of Assessment
(1) (2) (4)
(3)
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%
6
V. Penilaian Sub CP MK / CLO Assessment
NRP Nilai Sub Nilai Sub Nilai Sub Keterangan (lulus
No Nama Mahasiswa Action Plan
Mahasiswa CP MK 1 CP MK 2 CP MK 3 / Tidak Lulus)
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
7
VI. Penilaian CPL yang dibebankan pada MK berdasarkan pada nilai Sub CP MK / PLO assessment charged to the course based on
CLO assessment
NRP Keterangan (lulus /
No Nama Mahasiswa Nilai CPL 2 Nilai CPL 3 Nilai CPL 4 Nilai CPL 5 Action Plan
Mahasiswa Tidak Lulus)
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
8
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
9
Lampiran / Enclosure
A. Rencana Tugas & Rubrik Penilaian / Assignment plan and assessment rubric
Bentuk Asesmen
Mg ke/ Sub CP-MK / Bobot /
(Penilaian)
Week Lesson Learning Outcomes (LLO) Load (%)
Form of Assessment
(1) (2) (4)
(3)
1 Mahasiswa mampu : Non-Test: 20
menjelaskan konsep dasar, pengertian dasar dan Melakukan resume dari
peranan metode peramalan di masalalu, saat ini perkuliahan
dan yang akan datang
Menjelaskan konsep dasar peramalan Non-test:
menjelaskan pengertian dasar peramalan Make summary of course
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
2 Mahasiswa mampu : Non-Test:
menjelaskan dasar-dasar peramalan kuantitatif, Melakukan resume dari
dasar-dasar probabilistik dan statistika inferensia perkuliahan
sebagai penunjang metode peramalan kuantitatif
menjelaskan dasar-dasar peramalan kuantitatif. Non-test:
menjelaskan dasar-dasar probabilistik penunjang Make summary of course
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:
10
Bentuk Asesmen
Mg ke/ Sub CP-MK / Bobot /
(Penilaian)
Week Lesson Learning Outcomes (LLO) Load (%)
Form of Assessment
(1) (2) (4)
(3)
mendapatkan model terbaik suatu data runtun Melakukan resume
waktu dengan metode rata-rata bergerak untuk dari perkuliahan
pola stationer dan trend linier
mendapatkan model terbaik suatu data runtun Non-test:
waktu dengan metode rata-rata bergerak untuk Make summary of
pola stationer course
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
9 Evaluasi Tengah Semester / Mid Semester Evaluation 30
10-15 Mahasiswa dapat menganalisis plot ACF, plot PACF Non-Test: 20
dan Transformasi Box-Cox untuk menetapkan Melakukan resume
model sementara dengan metode Box-Jenkins. dari perkuliahan
Mahasiswa mampu mendapatkan model terbaik Presentasi makalah
suatu data runtun waktu dengan metode Box-
Jenkins (ARIMA). Non-test:
Make summary of
Students can analyze ACF plots, PACF plots and course
Box-Cox transformations to establish a provisional Presentation of paper
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%
11
B. Rubrik Atau Marking Scheme Assessment / Rubric or marking Marking Scheme
Assessment
12
C. Bukti – soal (Asesmen dan Tugas) / Evidence of assignment and assessment
1. Mid Semester Evaluation
2. Final Semester Evaluation
13
D. Bukti jawaban soal dan Hasil Tugas / Evidence of solution and assignment result
1. Mid Semester Evaluation
14
2. Final Semester Evaluation
15
