K-nearest neighbor analysis to predict the accuracy of product delivery using administration of raw material model in the cosmetic industry (PT Cedefindo)

K-nearest neighbor analysis to predict the accuracy of product delivery using administration of raw material model in the cosmetic industry (PT Cedefindo)

• By Novitasari, Hafifah, Bela; Hadianto, Nur; Sfenrianto; Rahmawati,Ami; Prasetyo, Rizal; Miharja,Jaja; Gata,Windu;
• Seminar Internasional
• 751

The purpose of this study is to apply the K-Nearest Neighbor (KNN) algorithm to predict the accuracy of product delivery using the raw material administration model, which has 3 attributes, namely administration of material purchases, administration of material readiness and processing work orders compared to existing targets. From this study, it was found that the KNN Algorithm was effective in predicting the accuracy of product delivery using the administration of raw material model applied to the production history data from July 2018 to December 2018 with a small error ratio. Thus, data mining with the KNN algorithm can be used in decision making within the company to predict the accuracy of product delivery.

• Katakunci
• URL http://
• Penerbit ,

REFERENSI

[1] Richard A, et al. , 1996, United States Patent (19) no. 19.

[2] Fargher H E and Smith R A, 1992, Method and System for Production Planning 19.

[3] Zhou, Zude, ed., 2010, Manufacturing Intelligence for Industrial Engineering: Methods for

System Self-Organization, Learning, and Adaptation.

[4] Eyre E C, 2015, Management in Office Administration.

[5] Pruthi R K, 2005, Theory of Public Administration.

[6] Su C, et al. ,2008, Some Progress of Supervised Learning (Advanced Intelligent Computing

Theories and Applications With Aspects of Artificial Intelligent) pp 661-666.

[7] Laaksonen, Jorma, and Erkki Oja, 1996. "Classification with learning k-nearest

neighbors."In Proceedings of International Conference on Neural Networks (ICNN'96), vol.

3, pp. 1480-1483. IEEE.

[8] Parsian M. ,2015, Data Algorithms.

[9] Borghesan, Francesco, Moncef Chioua, and Nina F. ,2019, Thornhill. Forecasting of process

disturbances using k -nearest neighbours, with an application in process control (Computer

and Chemical Engineering vol 128) pp 188- 200.

[10] Sodsee S. ,2014, Predicting caesarean section by applying nearest neighbor analysis (Procedia

Computer Science vol 31) pp 5-14.

[11] Alkhatib, Khalid, Hassan Najadat, Ismail Hmeidi, and Mohammed K. Ali Shatnawi, 2013,. Stock

price prediction using using K-Nearest Neighbor (KNN) algorithm (International Journal of

Business, Humanities and Technology vol 3) pp 32-44.

[12] Adeniyi, David Adedayo, Zhaoqiang Wei, and Y. Yongquan. , 2016, Automated web usage data

mining and recommendation system using K-Nearest Neighbor (KNN) classification method

(Applied Computing and Informatics vol 12) pp 90-108.

[13] Yesilbudak, Mehmet, Seref Sagiroglu, and Ilhami Colak. ,2013, A new approach to very short

term wind speed prediction using k-nearest neighbor classification (Energy Conversion and

Management vol 69) pp 77-86.