https://aboutmusicschools.com https://slotmgc.com https://300thcombatengineersinwwii.com https://mobilephonesource.co.uk https://discord-servers.io https://esmark.net https://slotmgc.com https://nikeshoesinc.us https://ellisislandimmigrants.org https://holidaysanthology.com https://southaventownecenter.net https://jimgodfreydesign.com https://mckinneypaintingpros.com https://enchantedmansion.org https://mckinneypaintingpros.com https://laurabrodieauthor.com https://holidaysanthology.com https://ardictionary.com https://113.30.151.116 https://103.252.118.20 https://206.189.83.174 https://157.230.39.109 https://128.199.85.208 https://172.104.51.149 https://174.138.21.250 https://157.245.50.183 https://152.42.239.189 https://188.166.210.125 https://152.42.178.155 https://192.53.172.202 https://172.104.188.91 https://103.252.118.157 https://63.250.61.107 https://165.22.104.74

Peta Kendali Demerit Untuk Data Autokorelasi (Moving Centerline Demerit dan Moving Range)

Authors

  • Nurmasyita Nasruddin Program Studi Statistika Fakultas MIPA Universitas Hasanuddin, Indonesia
  • Erna Tri Herdiani Program Studi Statistika Fakultas MIPA Universitas Hasanuddin, Indonesia
  • Nasrah Sirajang Program Studi Statistika Fakultas MIPA Universitas Hasanuddin, Indonesia

DOI:

https://doi.org/10.29313/statistika.v24i2.3145

Keywords:

Autokorelasi, Peta Kendali Demerit, Peta Kendali Moving Centerline Demerit, Peta Kendali Residual

Abstract

ABSTRAK

Proses industri seringkali menghasilkan data cacat yang bersifat autokorelasi, hal ini meyebabkan asumsi dasar penggunaan peta kendali tidak terpenuhi. Peta kendali demerit direkomendasikan untuk perusahaan yang terdapat berbagai macam tingkat kesalahan. Peta kendali demerit adalah metode pengendalian kualitas yang mengkategorikan jenis cacat ke dalam beberapa kelas berdasarkan tingkat keseriusannya. Peta kendali demerit sangat berguna dalam situasi di mana terdapat berbagai macam tingkat kesalahan, memungkinkan perusahaan untuk mengidentifikasi dan mengatasi cacat berdasarkan tingkat dampaknya terhadap kualitas produk. Penelitian ini bertujuan untuk memperoleh peta kendali Demerit pada data berautokorelasi dan menerapkan peta kendali Residual Demerit dan peta kendali Moving Centerline Demerit sebagai solusi dalam peta kendali Demerit autokorelasi terhadap pengendalian kecacatan produk pada data wadah plastik anti bocor. Metode yang digunakan adalah peta kendali demerit, peta kendali Residual, dan peta kendali Moving Centerline Demerit (MCD). Data yang digunakan merupakan data sekunder. Hasil penenelitian ini memperlihatkan bahwa peta kendali Residual dan peta kendali Moving Centerline Demerit sama unggulnya dalam mengatasi data autokorelasi pada peta kendali Demerit dimana sama-sama terdapat 4 out of control atau 4 titik yang mengindikasikan adanya masalah proses produksi yang tidak dapat diatasi oleh perusahaan.

ABSTRACT

Industrial processes often produce defect data that is autocorrelated, causing the basic assumptions of using control maps to not be met. If there are various levels of errors in the company, then the company is advised to use the Demerit control map. Demerit control map is a quality control method that categorizes defect types into several classes based on their seriousness. Demerit control maps are particularly useful in situations where there is a wide range of error rates, allowing companies to identify and address defects based on their level of impact on product quality. This study aims to derive Demerit control maps on autocorrelated data and apply the Residual Demerit control map and the Moving Centerline Demerit control map as solutions in the autocorrelated Demerit control map to product defect control on leak-proof plastic container data. The methods used are Demerit control map, Residual control map, and Moving Centerline Demerit (MCD) control map. The data used is secondary data. The results of this study indicate that the Residual control map and the Moving Centerline Demerit control map are equally superior in overcoming autocorrelated data on the Demerit control map where there are both 4 out of control or 4 points that indicate a production process problem that cannot be overcome by the company.

References

Aksioma, D. F. (2019). Pengendalian Kualitas Kantong Semen di PT. Industri Kemasan Semen Gresik Menggunakan Peta Kendali Demerit dan Fuzzy Demerit. Jurnal Sains Dan Seni ITS, 7(2), 156–161.

Azizah, I. N., Arum, P. R., & Wasono, R. (2021). Model Terbaik Uji Multikolinearitas untuk Analisis Faktor-Faktor yang Mempengaruhi Produksi Padi di Kabupaten Blora Tahun 2020. Prosiding Seminar Nasional UNIMUS, 4.

Bisri, H. (2019). Menghilangkan Autokorelasi Pada Diagram Kontrol Shewhart Menggunakan Diagram Kontrol Residual Berdasarkan Model Extention Support Vector Regression. Institut Teknologi Sepuluh Nopember.

Graxinha, A. M. F., & Pereira, J. M. D. (2023). Real Time Statistical Process Control for Autocorrelated Serial Data: A Simulation Approach. International Journal of Computing, 22(2), 107–116.

Irwanto, M. A. (2017). Analisis Kapabilitas Proses Pengelasan pada Pembuatan Kapal SSV2 PT. Pal Indonesia. Institut Teknologi Sepuluh September.

Montgomery, D. C. (2020). Introduction to Statistical Quality Control (8 ed.). John Wiley & Sons.

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting (Second Edition). John Wiley & Sons.

Nembhard, D. A., & Nembhard, H. B. (2000). A Demerits Control Chart for Autocorrelated Data. Quality Engineering, 13(2), 179-190.

Nursiwan, A. (2023). Analisis Dampak Suku Bunga dan Inflasi terhadap Profitabilitas Bank Syariah dengan Pendekatan Time Series. Persya: Jurnal Perbankan Syariah, 1(1), 18–24.

Piter, F. P., Asdi, Y., & Yozza, H. (2021). Analisis Pengendalian Kualitas Menggunakan Diagram Kendali Demerit Pada Kualitas Produk PT. Sinar Sosro Kpb Ungaran. Jurnal Matematika UNAND, 9(4), 366–372.

Ramadhani, G. S., Wilandari, Y., & Suparti, S. (2014). Analisis Pengendalian Kualitas Menggunakan Diagram Kendali Demerit (Studi Kasus Produksi Air Minum Dalam Kemasan 240 ml di PT TIW). Jurnal Gaussian, 3(3), 401–410.

Rismayanti, R., Lestari, S. P., & Rahwana, K. A. (2022). The Effect of Raw Material Inventory Control Costs and Direct Labor Costs on Production Volume (Case Study on Al-Yafi embroidery mukena in Tasikmalaya Regency 2018-2021 Period). Journal of Indonesian Management (JIM), 2(2), 287–292.

Syarifuddin, S., Herdiana, E. T., & AF, M. S. (2018). Perbandingan Bagan Kendali Modifikasi Shewhart dan Bagan Kendali ARMAST pada ARMA (1, 1). Jurnal Matematika, Statistika dan Komputasi, 15(1), 75–87.

Ylmaz, H., & Yank, S. (2020). Design of Demerit Control Charts with Fuzzy C-Means Clustering and An Application in Textile Sector. Textile and Apparel, 30(2), 117–125.

Zhou, P., Lin, D. K. J., Niu, X., & He, Z. (2020). Performance Evaluation Method for Network Monitoring Based on Separable Temporal Exponential Random Graph Models with Application to The Study of Autocorrelation Effects. Computers & Industrial Engineering, 145, 106507.

Downloads

Published

2024-11-25

Issue

Section

Articles