Rochmaturiza, Afi (2018) Pengendalian Kualitas Produk Portland Pozzoland Cement (PPC) Dengan Pendekatan Regression Adjustment Control Chart Di Pt. Semen Indonesia (Persero), Tbk. Unit Gresik. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
PT. Semen Indonesia (Persero) Tbk unit Gresik telah menjadi holding company penghasil semen terbesar di Indonesia. Salah satu produk dengan penjualan yang tinggi yaitu PPC. Parameter utama penentu kualitas semen secara umum adalah kuat tekan yang berperan sebagai variabel respon. Faktor-faktor yang mempengaruhi kuat tekan diantaranya blaine, mesh dan LOI. Maka perlu dilakukan pengendalian kualitas secara statistik untuk mengetahui kebaikan proses secara menyeluruh. Pada penelitian ini digunakan pendekatan regression adjustment control chart dimana regresi digunakan untuk memodelkan data blaine, mesh dan LOI dengan respon kuat tekan. Residual yang diperoleh dari model tersebut nantinya akan dikendalikan menggunakan diagram kendali MEWMV untuk mendeteksi perubahan variabilitas proses dengan nilai pembobot optimal dan dan diagram kendali MEWMA untuk mendeteksi pergeseran rata-rata proses dengan nilai pembobot optimal sebesar 0,1. Baik fase satu maupun dua, variabilitas telah terkendali secara statistik sedangkan rata-rata proses tidak terkendali secara statistik karena ada data yang out of control.
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PT. Semen Indonesia (Persero) Tbk Unit Gresik is one of the largest cement company in Indonesia. One of the product which has the high demand is PPC. The major parameter in general that determine the quality of cement is compressive strength, which is made by respon variable. The variable that affect the compressive strength is blaine, mesh and LOI. Therefore, it is necessary to control the quality of the statistical basis to determine the good of the overall process. In this research used regression adjustment control chart which regression for making model of blaine, mesh and LOI with compressive strength. The residual was obtained by model, which it can be controlled by using MEWMV to detect changes in process variability with weighting value obtained the most optimal is and and MEWMA to detect changes in the process while the optimal weighting is 0,1. Both the phase one and phase two, the variabilty process are controlled statistically and avarage process are uncontrolled statistically because of the data is out of control.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Kata Kunci :Blaine, Free Lime, LOI, MEWMA, MEWMV, Regression Adjustment Control Chart |
| Subjects: | H Social Sciences > HA Statistics |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Afi Rochmaturiza |
| Date Deposited: | 11 Apr 2018 02:34 |
| Last Modified: | 15 Jun 2020 05:11 |
| URI: | http://repository.its.ac.id/id/eprint/50778 |
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