PERAMALAN HARGA MINYAK MENTAH DUNIA MENGGUNAKAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK - Diponegoro University | Institutional Repository (UNDIP-IR) Rahafatri Arya F
PERAMALAN HARGA MINYAK MENTAH DUNIA
MENGGUNAKAN METODE RADIAL BASIS FUNCTION
NEURAL NETWORK
SKRIPSI
Disusun oleh:
Rahafattri Ariya Fauzannissa
24010211140092
JURUSAN STATISTIKA
FAKULTAS SAINS DAN MATEMATIKA
UNIVERSITAS DIPONEGORO
SEMARANG
2015
PERAMALAN HARGA MINYAK MENTAH DUNIA
MENGGUNAKAN METODE RADIAL BASIS FUNCTION
NEURAL NETWORK
Disusun Oleh :
Rahafattri Ariya Fauzannissa
24010211140092
Skripsi
Sebagai Salah Satu Syarat untuk Memperoleh Gelar
Sarjana Sains pada Jurusan Statistika
JURUSAN STATISTIKA
FAKULTAS SAINS DAN MATEMATIKA
UNIVERSITAS DIPONEGORO
SEMARANG
2015
KATA PENGANTAR
Puji dan syukur penulis ucapkan atas kehadirat Allah SWT yang telah
memberikan rahmat dan karunia-Nya sehingga penulis dapat menyelesaikan tugas
akhir dengan judul Peramalan Harga Minyak Mentah Dunia Menggunakan
Metode Radial Basis Function Neural Network . Tugas Akhir ini tidak akan
selesai dengan baik tanpa adanya dukungan dan bantuan dari berbagai pihak.
Ucapan terima kasih penulis haturkan kepada :
1. Ibu Dra. Dwi Ispriyanti, M.Si, sebagai Ketua Jurusan Statistika Fakultas
Sains dan Matematika Universitas Diponegoro dan sekaligus Dosen
Pembimbing II.
2. Bapak Hasbi Yasin, S.Si, M.Si, sebagai Dosen Pembimbing I.
3. Bapak dan Ibu Dosen Jurusan Statistika Fakultas Sains dan Matematika
Universitas Diponegoro
4. Serta semua pihak yang telah membantu kelancaran penyusunan laporan
ini, yang tidak dapat disebutkan satu per satu.
Penulis menyadari bahwa pembuatan Tugas Akhir ini tak luput dari segala
bentuk kesalahan. Oleh karena itu, penulis mengharapkan kritik dan saran yang
membangun untuk perbaikan Tugas Akhir ini. Akhir kata, semoga Tugas Akhir
ini dapat memberikan manfaat bagi semua pihak.
Semarang, November 2015
Penulis
BSTRAK
y
y
y
y
y
y
y
y
time
series
y
y
y
Radial Basis Function
Neural Network
w
y
input layer, hidden layer output layer. y
y y
E training sebesar 0,9145 dan nilai MAPE training 0,74%, sedangkan
untuk MSE testing sebesar 4,2739 dan nilai MAPE testing adalah 1,63%.
Berdasarkan hasil peramalan, harga minyak mentah dunia pada tanggal 29 Juli
2015 sampai dengan 2 Agustus 2015 berkisar pada angka USD $55,91 per barel.
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Radial Basis Function Neural Network (RBFNN), Time Series,
Harga Minyak Mentah Dunia, MSE, MAPE, Peramalan.
v
18
ABSTRACT
Oil is the most important commodity in everyday life, because oil is one of
the main source of energy that is needed for the people. Changes in crude oil
prices greatly affect the economic conditions of a country. To forecast crude oil
prices, the past data of the crude oil that is the time series data will be studied so
that will produce crude oil price forecast in the future. Model of Radial Basis
Function Neural Network is suitable for large-scale data processing, because this
model does not require the use of all data input and has a total processing time of
rapid system. This model has a network architecture in the form of input layer,
hidden layer and output layer. Analysis conducted on the data as much as 1286
taken as 100 the data thus obtained value of 0.9145 MSE training and training
MAPE value of 0.74%, while for the testing of 4.2739 MSE and MAPE testing
value is 1.63%. Based on the results of forecasting, crude oil prices on July 29,
2015 until August 2, 2015 at USD $ 55.91 per barrel.
Keywords: Radial Basis Function Neural Network (RBFNN), Time Series, Crude
Oil, MSE, MAPE, Forcasting.
18
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MENGGUNAKAN METODE RADIAL BASIS FUNCTION
NEURAL NETWORK
SKRIPSI
Disusun oleh:
Rahafattri Ariya Fauzannissa
24010211140092
JURUSAN STATISTIKA
FAKULTAS SAINS DAN MATEMATIKA
UNIVERSITAS DIPONEGORO
SEMARANG
2015
PERAMALAN HARGA MINYAK MENTAH DUNIA
MENGGUNAKAN METODE RADIAL BASIS FUNCTION
NEURAL NETWORK
Disusun Oleh :
Rahafattri Ariya Fauzannissa
24010211140092
Skripsi
Sebagai Salah Satu Syarat untuk Memperoleh Gelar
Sarjana Sains pada Jurusan Statistika
JURUSAN STATISTIKA
FAKULTAS SAINS DAN MATEMATIKA
UNIVERSITAS DIPONEGORO
SEMARANG
2015
KATA PENGANTAR
Puji dan syukur penulis ucapkan atas kehadirat Allah SWT yang telah
memberikan rahmat dan karunia-Nya sehingga penulis dapat menyelesaikan tugas
akhir dengan judul Peramalan Harga Minyak Mentah Dunia Menggunakan
Metode Radial Basis Function Neural Network . Tugas Akhir ini tidak akan
selesai dengan baik tanpa adanya dukungan dan bantuan dari berbagai pihak.
Ucapan terima kasih penulis haturkan kepada :
1. Ibu Dra. Dwi Ispriyanti, M.Si, sebagai Ketua Jurusan Statistika Fakultas
Sains dan Matematika Universitas Diponegoro dan sekaligus Dosen
Pembimbing II.
2. Bapak Hasbi Yasin, S.Si, M.Si, sebagai Dosen Pembimbing I.
3. Bapak dan Ibu Dosen Jurusan Statistika Fakultas Sains dan Matematika
Universitas Diponegoro
4. Serta semua pihak yang telah membantu kelancaran penyusunan laporan
ini, yang tidak dapat disebutkan satu per satu.
Penulis menyadari bahwa pembuatan Tugas Akhir ini tak luput dari segala
bentuk kesalahan. Oleh karena itu, penulis mengharapkan kritik dan saran yang
membangun untuk perbaikan Tugas Akhir ini. Akhir kata, semoga Tugas Akhir
ini dapat memberikan manfaat bagi semua pihak.
Semarang, November 2015
Penulis
BSTRAK
y
y
y
y
y
y
y
y
time
series
y
y
y
Radial Basis Function
Neural Network
w
y
input layer, hidden layer output layer. y
y y
E training sebesar 0,9145 dan nilai MAPE training 0,74%, sedangkan
untuk MSE testing sebesar 4,2739 dan nilai MAPE testing adalah 1,63%.
Berdasarkan hasil peramalan, harga minyak mentah dunia pada tanggal 29 Juli
2015 sampai dengan 2 Agustus 2015 berkisar pada angka USD $55,91 per barel.
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Radial Basis Function Neural Network (RBFNN), Time Series,
Harga Minyak Mentah Dunia, MSE, MAPE, Peramalan.
v
18
ABSTRACT
Oil is the most important commodity in everyday life, because oil is one of
the main source of energy that is needed for the people. Changes in crude oil
prices greatly affect the economic conditions of a country. To forecast crude oil
prices, the past data of the crude oil that is the time series data will be studied so
that will produce crude oil price forecast in the future. Model of Radial Basis
Function Neural Network is suitable for large-scale data processing, because this
model does not require the use of all data input and has a total processing time of
rapid system. This model has a network architecture in the form of input layer,
hidden layer and output layer. Analysis conducted on the data as much as 1286
taken as 100 the data thus obtained value of 0.9145 MSE training and training
MAPE value of 0.74%, while for the testing of 4.2739 MSE and MAPE testing
value is 1.63%. Based on the results of forecasting, crude oil prices on July 29,
2015 until August 2, 2015 at USD $ 55.91 per barrel.
Keywords: Radial Basis Function Neural Network (RBFNN), Time Series, Crude
Oil, MSE, MAPE, Forcasting.
18
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