# Identifying irregularity electricity usage of customer behaviors using logistic regression and linear discriminant analysis
> Lawi A.

URL kanonis: https://discover.unhas.ac.id/publications/identifying-irregularity-electricity-usage-of-customer-behaviors-using-logistic
Jurnal / Konferensi: Proceeding 2017 3rd International Conference on Science in Information Technology Theory and Application of IT for Education Industry and Society in Big Data Era Icsitech 2017
Tahun terbit: 2017
DOI: https://doi.org/10.1109/ICSITech.2017.8257174
Citations: 11

## Authors
- Lawi A.

## Abstract
This study aims to implement a machine learning technique in identifying the irregularities of customer behavior on the use of prepaid electricity pulses. The methods used are Linear Discriminant Analysis and Logistic Regression. The performance of the classification system will be evaluated using the 10-fold cross-validation technique. Validation results are measured using accuracy, precision and recall values. In this research shows that the use of machine learning technique has a good performance in classification of electrical consumption behavior. Experimental results with the different amount of data testing indicate that Logistic Regression method has high accuracy, precision, and recall value when compared with Linear Discriminant Analysis that is 100%. This is due to Logistic Regression method can predict irregularities accurately because the addition of the amount of data does not affect the performance of the method.

## Keywords
- Linear discriminant analysis
- Logistic regression
- Computer science
- Artificial intelligence
- Regression analysis
- Linear regression
- Machine learning
- Statistics
- Logistic model tree
- Pattern recognition (psychology)
- Data mining
- Mathematics

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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