Garuda - Garba Rujukan Digital

Ririn Regiana Dwi Satya, Ririn Regiana Dwi

Program Studi Teknik Industri, Fakultas Teknik Dan Ilmu Komputer, Universitas Indraprasta PGRI

Author-ID : 515439

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

Published : 26 Documents Claim Missing Document

Claim Missing Document

Articles

Title

1

Usulan Perencanaan Persediaan Produk FMCG Menggunakan Metode Algoritma Apriori dan Jaringan Syaraf Tiruan (JST) pada PT Borwita Indah: Proposed FMCG Product Inventory Planning Using the Apriori Algorithm Method and Artificial Neural Network (ANN) at PT Borwita Indah Putra, Bayu Triadi; Hunusalela, Zeny Fatimah; Satya, Ririn Regiana Dwi
Jurnal Rekayasa Sistem Industri Vol. 13 No. 2 (2024): Vol. 13 No. 2 (2024): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26593/jrsi.v13i2.7015.29-44

The problem faced by PT Borwita Indah is the occurrence of overstock and understock on FMCG products. This study aims to determine the inventory planning of FMCG products with related factors, namely sales results, stock of finished goods, and to be able to predict accurately so that there is no buildup of products in the warehouse. This research using the apriori algorithm for data collection with associative rules which is carried out through support and confidence calculation mechanisms and artificial neural networks for forecasting methods which have a fairly low data error rate and are good enough in the generalization process so that this model is able to predict timeseries data for several time periods for future. Itemset data pattern obtained resulted in a level of confidence in Head&Shoulders with Pantene 90.9%, and a level of confidence in Pantene with Head&Shoulders 90.9%. Meanwhile, the level of confidence for Vicks with Head&Shoulders reaches 100%. Forecasting results for the next 12 periods for each Pantene, Head&Shoulders, and Vicks product have a fluctuating forecasting trend so that safety stock can be taken into account. The amount of safety stock that must be in the company is 504 pcs of Pantene per day, 49 pcs of Head&Shoulders per day, 35 pcs of Vicks. It is hoped that with this research the company can carry out optimal product inventory control.

Usulan Perencanaan Persediaan Produk FMCG Menggunakan Metode Algoritma Apriori dan Jaringan Syaraf Tiruan (JST) pada PT Borwita Indah: Proposed FMCG Product Inventory Planning Using the Apriori Algorithm Method and Artificial Neural Network (ANN) at PT Borwita Indah Putra, Bayu Triadi; Hunusalela, Zeny Fatimah; Satya, Ririn Regiana Dwi
Jurnal Rekayasa Sistem Industri Vol. 13 No. 2 (2024): Vol. 13 No. 2 (2024): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26593/jrsi.v13i2.7015.29-44

The problem faced by PT Borwita Indah is the occurrence of overstock and understock on FMCG products. This study aims to determine the inventory planning of FMCG products with related factors, namely sales results, stock of finished goods, and to be able to predict accurately so that there is no buildup of products in the warehouse. This research using the apriori algorithm for data collection with associative rules which is carried out through support and confidence calculation mechanisms and artificial neural networks for forecasting methods which have a fairly low data error rate and are good enough in the generalization process so that this model is able to predict timeseries data for several time periods for future. Itemset data pattern obtained resulted in a level of confidence in Head&Shoulders with Pantene 90.9%, and a level of confidence in Pantene with Head&Shoulders 90.9%. Meanwhile, the level of confidence for Vicks with Head&Shoulders reaches 100%. Forecasting results for the next 12 periods for each Pantene, Head&Shoulders, and Vicks product have a fluctuating forecasting trend so that safety stock can be taken into account. The amount of safety stock that must be in the company is 504 pcs of Pantene per day, 49 pcs of Head&Shoulders per day, 35 pcs of Vicks. It is hoped that with this research the company can carry out optimal product inventory control.

Title Search

Found 2 Documents Search Journal : Jurnal Rekayasa Sistem Industri

Abstract

Abstract

Title

Found 2 Documents
Search
Journal : Jurnal Rekayasa Sistem Industri