Workload Measurement Model in Industry: Fish Processing Post Covid-19

Reducing the number of employees during the pandemic is the reaction manufacturers mostly take to survive in doing business due to the impact of the Covid-19 pandemic. This study aims to analyze the effect of layoffs on the workload of the affected manufacturers. The data is taken from one of the fish processing companies in the Makassar Industrial Estate, which has reduced its workforce due to the impact of the Covid-19 pandemic. Based on the results of the study showed that from the three divisions in the company, namely the admission, retouching, and packing divisions, the admission division in the fillet section obtained a different score. This study concludes whether the decision to lay off during the pandemic is right or not so that it can be a reference for companies experiencing economic impacts in terms of production efficiency and effectiveness. © 2023 Seventh Sense Research Group®

Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory

Electricity theft caused a major loss for electricity power provider. The anomaly detection helps to predict the abnormal load usage of a consumer. Usually, the classification method used in anomaly detection. This research paper proposed to identify the potential anomaly points by using threshold and outliers. The prediction in time-series applied Long Short-Term Memory (LSTM) algorithm. The historical electricity load dataset of a single industrial consumer was used to generate the prediction of electricity load. There were five optimizers used to produce the model: Adam, Adadelta, Adagrad, RMSProp, and Stochastic gradient descent (SGD). The prediction model was evaluated using mean squared error (MSE) and mean absolute error (MAE). The best model among all five models was generated by Adadelta optimizer with the error rate value of 0.091982 for MSE and 0.018433 for MAE. The prediction values were generated by this model. The anomaly point was detected by using threshold and outliers. The threshold value was 0.218983. One week in August 2019 was chosen to detect any anomaly load occurrences. There were 24 outliers were found within the selected week. The study shall expand on the electricity usage trend during COVID-19 pandemic period. © 2021 IEEE.