Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Rekayasa Material, Manufaktur & Energi
., Muchlisinalahuddin
Unknown Affiliation
Aerospace Engineering Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Perbandingan Alogaritma Machine Learning Untuk Prediksi Sifat Mekanik Pada Baja Paduan Rendah Leni, Desmarita; kusuma, Yuda Perdana; Sumiati, Ruzita; ., Muchlisinalahuddin; ., Adriansyah
Rekayasa Material, Manufaktur dan Energi Vol 5, No 2: September 2022
Publisher : Fakultas Teknik UMSU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/rmme.v5i2.11407

Abstract

The development of industrial technology encourages companies to be selective in determining the mechanical properties of materials, one of which is low-alloy steel. The purpose of knowing the mechanical properties of low alloy steel is to support the success of a construction product, transportation, machine elements, and so on. Heat treatment of metal is one of the test methods to determine the mechanical properties of steel by heating the steel at a certain temperature. The selection of low alloy steel composition has various variations to be applied so as to obtain the desired mechanical properties. The mechanical properties of low-alloy steel are strongly influenced by the composition contained in the steel. If the composition of the steel is added to a new element, the mechanical properties of the steel will change, so it needs to be retested. This research uses machine learning modeling to predict the mechanical properties of low-alloy steels based on their chemical compositions. This study compares three algorithms, namely decision tree (DT), random forest (RF), and artificial neural network (ANN), where the ANN algorithm has better performance by producing an RMSE value of 6.187 with training cycle parameter settings of 30.000, learning rate 0.007, momentum 0.9, and size of hidden layer 9.
Analisis Heatmap Korelasi dan Scatterplot untuk Mengidentifikasi Faktor-Faktor yang Mempengaruhi Pelabelan AC efisiensi Energi Leni, Desmarita; ., Muchlisinalahuddin; ., Maimuzar; ., Haris; ., Hendra
Rekayasa Material, Manufaktur dan Energi Vol 6, No 1: Maret 2023
Publisher : Fakultas Teknik UMSU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/rmme.v6i1.13133

Abstract

This research aims to evaluate the factors that affect AC energy efficiency using statistical analysis methods. The data used is energy-efficient AC labeling data obtained from the Directorate General of New, Renewable, and Energy Conservation Energy (EBETKE) database. Violin plots are used to see the distribution of the data, a correlation heatmap is used to display the level of correlation between variables, and scatterplots and R-squared values are used to visualize linear relationships and measure the strength of the relationship. The results of the study show that efficiency has a very strong positive correlation with rating, with a correlation coefficient of 0.75, while it has a weak negative correlation with other variables. The R-squared value obtained for the linear relationship between efficiency and rating is 0.56, which indicates that 56% of the variation in efficiency can be explained by the variation in rating. This result shows that rating is a very influential factor on AC energy efficiency.
Co-Authors ., Adriansyah ., Maimuzar Haris ., Haris Hendra . kusuma, Yuda Perdana Ruzita Sumiati