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All Journal Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Madura
Yudhis Thiro Kabul Yunior
Airport Electrical Engineering, Surabaya Aviation Polytechnic, Jl.Jemur Andayani I No.73 Surabaya, Indonesia
Humanities Economics, Econometrics & Finance Social Sciences

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Electricity Tariff Analysis and Prediction System Using the Arima Algorithm Based on the Internet of Things Slamet Hariyadi; Yudhis Thiro Kabul Yunior; Dwi Yanto
Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Vol 18, No 4a: 2025
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/pamator.v18i4a.32855

Abstract

The problem of monthly electricity bills in Indonesia is often a hot topic among the public. Some of the problems with electricity bills that are often complained about by the public include: The increase in basic electricity rates by the government or PLN (State Electricity Company) is often the main complaint. This increase can have a significant impact on people's monthly expenses. Many customers feel that their electricity bills do not match their actual usage. This can be caused by uncontrolled electricity usage or devices that consume more electricity than expected. There are cases where errors in electricity meter readings occur, either due to human error or damage to the meter. This error can result in electricity bills that are higher or lower than actual usage. Some customers feel they do not get clear information about the details of their bills. This lack of transparency can lead to distrust of PLN. Several additional costs such as administration fees, late payment fees, and other costs can add to the burden on monthly electricity bills. Based on the above problems, we built a Smart Precision Electric platform system based on IoT and Artificial Intelligence as a Predictive Model for Electricity Bills. The system we built is able to predict next month's electricity bill and identify the electronic devices that contribute the most to that cost using the integration of the Internet of Things Platform and Artificial Intelligence.
Co-Authors Dwi Yanto Slamet Hariyadi