| dc.contributor |
Fakultas Matematika dan Ilmu Pengetahuan Alam |
|
| dc.creator |
Fitria, Isna Maya |
|
| dc.creator |
Darwis, Sutawanir |
|
| dc.date |
2020-08-23 |
|
| dc.date.accessioned |
2021-03-15T03:45:09Z |
|
| dc.date.available |
2021-03-15T03:45:09Z |
|
| dc.identifier |
http://karyailmiah.unisba.ac.id/index.php/statistika/article/view/22816 |
|
| dc.identifier |
10.29313/.v6i2.22816 |
|
| dc.identifier.uri |
http://hdl.handle.net/123456789/28853 |
|
| dc.description |
Abstract. Machines are tools to facilitate human work. One proof that machines are needed for society is that many large machines are used in industry. There is one component of the machine that is very important, namely the bearing. The function of the bearing is to support a shaft so that the shaft can rotate without experiencing excessive friction. Excessive friction causes vibration and harms machine users who are unable to properly supervise. So that forecasting is needed because it has an influence on performance for performance to help companies in machine conditions. The most popular forecasting model is the Autoregressive Integrated Moving Average (ARIMA), where ARIMA is a time series-based model developed by Box and Jenkins (1976) and the Support Vector Regression (SVR) Model. SVR is a generalization of the Support Vector Machine Model in the regression case where the output is a continuous number. This thesis discusses how to model and find the accuracy value on the vibration bearing data of the FEMTO-ST Institute with selected feature extraction, namely root mean square (RMS) and produce accurate forecasts.Keywords: Support Vector Regression, ARIMA, Bearing Vibration.Abstrak. Mesin merupakan alat bantu untuk mempermudah pekerjaan manusia. Salah satu bukti mesin sangat dibutuhkan bagi masyarakat adalah banyak mesin besar digunakan dalam industri. Terdapat salah satu kompen dari mesin yang sangat penting yaitu bearing. Fungsi dari bearing adalah menumpu sebuah poros agar poros dapat berputar tanpa mengalami gesekan yang berlebihan. Gesekan yang berlebihan inilah mengakibatkan vibrasi dan merugikan pengguna mesin apabila tidak ditangani dengan baik. Sehingga peramalan sangat dibutuhkan karena memiliki pengaruh terhadap proses kinerja bagi praktisi untuk membantu perusahaan dalam memantau kondisi mesin. Terdapat model peramalan yang paling popular yaitu Autoregressive Integrated Moving Average (ARIMA), dimana ARIMA merupakan model berbasis time series yang dikembangkan oleh Box dan Jenkins (1976) serta Model Support Vector Regression (SVR). SVR merupakan generalisasi Model Support Vector Machine pada kasus regresi yang outputnya bilangan kontinu. Skripsi ini membahas cara memodelkan dan mencari nilai akurasi pada data vibrasi bearing FEMTO-ST Institut dengan ekstraksi fitur terpilih yaitu root mean square (RMS) serta menghasilkan peramalan yang akurat.Kata Kunci: Support Vector Regression, ARIMA, Vibrasi Bearing. |
|
| dc.format |
application/pdf |
|
| dc.language |
eng |
|
| dc.publisher |
Universitas islam Bandung |
|
| dc.relation |
http://karyailmiah.unisba.ac.id/index.php/statistika/article/view/22816/pdf |
|
| dc.relation |
http://karyailmiah.unisba.ac.id/index.php/statistika/article/downloadSuppFile/22816/4483 |
|
| dc.rights |
Copyright (c) 2020 Prosiding Statistika |
|
| dc.source |
Prosiding Statistika; Vol 6, No 2, Prosiding Statistika (Agustus, 2020); 49-57 |
|
| dc.source |
Prosiding Statistika; Vol 6, No 2, Prosiding Statistika (Agustus, 2020); 49-57 |
|
| dc.source |
2460-6456 |
|
| dc.source |
10.29313/.v6i2 |
|
| dc.subject |
Statistika |
|
| dc.subject |
Support Vector Regression, ARIMA, Bearing Vibration |
|
| dc.title |
Pemodelan Menggunakan Autoregressive Integrated Moving Average (ARIMA) dan Support Vector Regression (SVR) pada Vibrasi Bearing |
|
| dc.type |
info:eu-repo/semantics/article |
|
| dc.type |
info:eu-repo/semantics/publishedVersion |
|
| dc.type |
Peer-reviewed Article |
|
| dc.type |
kuantitatif |
|