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Journal : Faktor Exacta

Konfigurasi Hyperparameter Long Short Term Memory untuk Optimalisasi Prediksi Penjualan Ali Khumaidi; Dhistianti Mei Rahmawan Tari; Nuke L. Chusna
Faktor Exacta Vol 15, No 4 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i4.15286

Abstract

To support business development and competition, forecasting capabilities with good accuracy are required. PT. Sumber Prima Inti Motor does not want the customer's spare part needs not to be available when ordered, therefore an appropriate procurement and sales forecasting strategy is needed. Long Short Term Memory (LSTM) is a fairly good algorithm for forecasting, in this study using LSTM to predict sales of spare parts for the next 60 days. The CRISP-DM method is used and to obtain optimal model performance, hyperparameter configuration is performed. The configurations used are number of hidden layers, data partition, epoch, batch size, and dropout scenario. The best results from the LSTM model hyperparameter configuration are 3 hidden layers, 3 dropouts, epoch 150, and batch size 30. The performance of the training and testing models with RMSE is 0.0855 and 0.0846.
Development of a Production Machine Maintenance Predictive Model Using the Elman Recurrent Neural Network Algorithm Ajat Zatmika; Harry Dwiyana Kartika; Ali Khumaidi
Faktor Exacta Vol 16, No 1 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i1.15450

Abstract

PT Simba Indosnack Makmur is a factory that produces snacks. In the production process the machine has worked very optimally, the problem that is often faced by the Quality Control department is often finding non-standard product weights. This problem is caused by a machine that already requires maintenance. So far, the maintenance process has to get approval from the manager, which sometimes takes quite a long time to be inspected so that the maintenance process is delayed, which results in reduced production targets. By implementing a predictive maintenance model that utilizes time series data in the production process, applying the Elman Recurrent Neural Network will be able to provide notifications for machine maintenance before the machine is inaccurate in snack production. The Elman structure was chosen because it can make iterations much faster, thus facilitating the convergence process. The input vector used uses windows size. The results of the study using a target error of 0.001 show the smallest MSE value of 0.002833 with windows size 11. Then by using 13 neurons in the hidden layer a minimum error value of 0.003725 is obtained.
Application of Ensemble Tree Algorithm for Installment Payment Arrears Prediction at Makmur Bersama Credit Union Khumaidi, Ali; Darmawan, Risanto; Reztrianti, Diajeng
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.21819

Abstract