Klasifikasi Konsumsi Energi Industri Baja Menggunakan Teknik Data Mining

Abstract

Human needs in fulfilling clothing, food and housing in today's life cannot be separated from the involvement of electrical energy. In several sectors of life, namely the household sector, industry, business, social, government office buildings, and public street lighting, electricity is needed. The energy consumption industry sector is relatively higher than other sectors, so it is necessary to control energy consumption, especially in the industrial sector. As a result, for a nation or region, forecasting the use of electrical energy becomes urgent and crucial. Research on this issue has emerged from various countries, for example, research from Korea on energy consumption prediction models for smart factories using a data mining algorithm that introduces and explores the steel industry energy consumption prediction model by producing the best model, namely Random Forest with an RMSE value of 7.33 in the test set. In addition, another study raised the title of an efficient energy consumption prediction model for an analytical data of industrial buildings in a smart city by presenting and exploring a predictive energy consumption model based on data mining techniques for a smart smallscale steel industry in South Korea using variables such as lagging and current. main reactive power, lagging power factor and leading current, carbon dioxide emission and load type. Research from Australia is also not left behind, discussing the prediction of industrial energy consumption using data mining techniques which presents and explores energy consumption prediction models using a data mining approach for the steel industry to show that the Random Forest model can best predict energy consumption and outperform other conventional algorithms in comparison. This study presents a classification of energy consumption in the steel industry, in order to know the pattern of using light loads, medium loads, and maximum loads using data mining techniques on public data that is already available on this matter, with the aim that energy users in the steel industry are wiser in using energy because you already know the pattern of each load. The methods used include Random Forest, Decision Tree, Naïve Bayes and Artificial Neural Networks resulting in accuracy of 91.13%, 90.50%, 70.97% and 75.56%, so that the classification method is the most suitable for use. In classifying industrial energy consumption on the steel industry energy consumption dataset, Random Forest.

Decision Tree for Smell Code Detection in Python: A Practical Implementation

“…The first stage in the system that will be built later is preprocessing, in this stage the dataset that has been collected is then cleaned so that the data that will become the training data becomes cleaner so that it will increase the accuracy value of the algorithm used, namely the decision tree (J48) [5] . The following is an image of raw data that has not been preprocessed: From the raw data shown above, it is necessary to carry out the preprocessing stages which can be explained as follows:…”

Pendeteksian Aroma Ganja Kering Menggunakan Algoritma Random Forest

“…RF (Random Forest) merupakan salah satu jenis algoritma klasifikasi yang terdiri dari lebih satu pohon keputusan yang setiap pohon keputusan dibentuk bergantung pada nilai-nilai vector acak sampel secara independen dan identik didistribusikan yang sama untuk semua pohon. Metode ini merupakan salah satu metode klasifikasi yang sangat akurat digunakan dalam melakukan prediksi, bisa menangani inputan variabel yang sangat besar jumlahnya tanpa overfitting, dan membantu menghilangkan korelasi antara pohon keputusan seperti karakteristik ensemble methods [3], [6].…”

“…Setiap simpul internal disebut simpul keputusan yang mewakili tes pada atribut atau subset atribut, dan masing-masing edge diberi label dengan nilai spesifik atau rentang nilai atribut input. Pengklasifikasi Decision Tree memperoleh akurasi yang serupa dan terkadang lebih baik jika dibandingkan dengan metode klasifikasi lainnya [6].…”

Performance Comparison of ARIMA, LSTM and SVM Models for Electric Energy Consumption Analysis

“…Therefore electricity consumption has increased every year. Therefore, for a country or region, the projection of the use of electrical energy is urgent and critical [3]. Among the various sectors mentioned, the industrial sector has a relatively high level of energy consumption compared to other sectors.…”