Electricity voltage decision support system with naive bayes method to improve company reputation (case study at PT. PLN Malang)
Fullchis Nurtjahjani, Raden Sugeng Basuki, Joni Dwi Pribadi, Kadek Suarjuna Batubulan, Satrio Binusa Suryadi, Hidayati Mazmi, Adinda Dwi Larasati
politeknik negeri malang
Abstract
Currently there are still many customers with 900 Volt Ampere (VA) Power which is categorized as capable households (RTM). As the result, the electricity tariff that must be paid by 900 VA Power customers climb up to Rp 1,352.00 / Kilo Watt Hours (KWH). The proportion of electricity subsidies to central government spending is increase from 2.5% in 2005 to 4.7% in the 2012 according to State Budget. The average electricity subsidy spends around 7% of the central government budget. This condition shows that electricity subsidies are adequate to burden the central government budget. (Bureau of Budget Analysis and APBN-Sekjen RI-RI Implementation). Problems in PT PLN have uncertain impact on the subsidy budget and inefficiencies in PT PLN require comprehensive solutions. In implementing the electricity subsidy distribution program that is right on target and can help the performance of PT. PLN is on duty, so a new breakthrough is needed based on technological advances in the world, namely new data collection and re-data collection for consumers by bringing data that has been determined by PT PLN as supporting data. So that the consumer support data will be tested with a system has made to determine that the consumer deserves a subsidy or not. In order to find a solution on subsidies that are right on target, in its application can seek solutions to problem solving through the application of the Naive Bayes method. Based on the results of the research, an intelligent system is found to help determine consumers who get subsidized and non-subsidized electricity rates with the right target and this system can also increase accuracy in determining consumers with appropriate electricity subsidy rates for PT PLN admin.
Keywords: Keywords: Subsidies and Naive Bayes.
Topic: Information Systems Technology