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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Manajemen Informatika
Fitri Indra Indikawati
Universitas Ahmad Dahlan
Computer Science & IT Electrical & Electronics Engineering

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Household Power Consumption Forecasting using IoT Smart Home Data Fitri Indra Indikawati; Guntur Maulana Zamroni
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 5, No 1 (2019): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (781.15 KB) | DOI: 10.26555/jiteki.v5i1.13184

Abstract

The use of the forecasting system is becoming more prominent in recent years. One of the implementations of the forecasting system is to predict electricity consumption demand. In this paper, we have developed a forecasting system for household electricity consumption using a well-known Extreme Gradient Boosting algorithm. We utilized time-series data from a smart meter dataset to make a predictive model. First, we evaluated the importance of time-series feature from the dataset and resampled the original dataset. Then, we used the resampled data to train the model and calculated training loss function. Our experimental studies with real IoT Smart Home data demonstrate that our forecasting system works well with small dataset using one-hour downsampling on the dataset.
Prediksi Kualitas Air Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) Ayunita Agustin; Faisal Fajri Rahani; Fitri Indra Indikawati
Jurnal Manajemen Informatika (JAMIKA) Vol 12 No 2 (2022): Jurnal Manajemen Informatika (JAMIKA)
Publisher : Program Studi Manajemen Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jamika.v12i2.8022

Abstract

Water conservation is very necessary to support the creation of clean water quality that is free from harmful substances that can disturb the environment. So a system is needed to monitor water quality to determine the level of pollution that occurs. This system will work to see water quality in real time with several quality parameters such as pH, temperature, and water turbidity. The purpose of this research is to produce a predictive model and find out the prediction results of a data mining-based system. The method used to predict water quality uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) method, because the water quality data is thought to contain seasonal patterns. The results of this study indicate that the SARIMA model can be applied to the dataset used and obtain the accuracy of the forecasting results on each of the tested parameter data. The results of water quality forecasting with this parameter are the result data for testing at a dataset of a depth of 30 cm and a depth of 60 cm for temperature parameters, namely MSE<0.1, and RMSE<0.02. For pH parameters, MSE<0.1, and RMSE<0.1. As well as the turbidity parameter, the results of MSE<0.02, and RMSE<0.13. From these results indicate that this system can predict water quality with past data.
Co-Authors Ayunita Agustin Faisal Fajri Rahani Guntur Maulana Zamroni