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All Journal Media Ilmiah Teknik Sipil Jurnal Mesin Nusantara Journal of Energy, Material, and Instrumentation Technology
Angelia, Nike
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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The Influence of Heatmap Correlation-based Feature Selection on Predictive Modeling of Low Alloy Steel Mechanical Properties Using Artificial Neural Network (ANN) Algorithm Leni, Desmarita; Sumiati, Ruzita; Adriansyah; Angelia, Nike; Nofriyanti, Elsa
Journal of Energy, Material, and Instrumentation Technology Vol 4 No 4 (2023): Journal of Energy, Material, and Instrumentation Technology
Publisher : Departement of Physics, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jemit.v4i4.203

Abstract

This study aims to evaluate the influence of heatmap correlation-based feature selection on predictive modeling of low alloy steel mechanical properties using an artificial neural network (ANN) algorithm. Heatmap correlation was used to determine the chemical elements most correlated to the low alloy steel mechanical properties, such as Yield strength (YS) and Tensile strength (TS). There were 15 input variables of chemical elements in this study, and after feature selection, 11 input variables were obtained for YS, and 13 input variables were obtained for TS. The ANN model was validated using K-fold 10 cross-validation and evaluated using loss metric, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results showed that modeling with feature selection was able to improve the YS prediction, with a decrease in value of 6.83% in MAE and 4.97% in RMSE, while the TS prediction decreased by 16.46% in MAE and 18.34% in RMSE after feature selection. These results indicate that the use of feature selection provides better performance compared to the model without feature selection, and heatmap correlation can be used as an alternative to improve model performance in predictive modeling of low alloy steel mechanical properties using the ANN algorithm.  
Mengoptimalkan Analisis Sifat Mekanik Material Berbasis Data Dengan Pandas Profiling Leni, Desmarita; Earnestly, Femi; Angelia, Nike; Nofriyanti, Elsa; Adriansyah, Adriansyah
Jurnal Mesin Nusantara Vol 7 No 1 (2024): Jurnal Mesin Nusantara
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/jmn.v7i1.21206

Abstract

The analysis of mechanical properties based on data is a method used to analyze the mechanical properties of a material using data, typically obtained from a material database. This process encounters several challenges, such as large volume of data, complexity in data processing, as well as difficulties in data visualization and interpretation. In this study, Pandas Profiling, a Python library designed specifically for automated dataset analysis, was employed. The dataset used consisted of tensile test results for various austenitic stainless steel types such as SUS 304, SUS 316, SUS 321, SUS 347, and NCF 800H. This dataset comprised 1916 samples with attributes related to mechanical properties and factors influencing them. The analysis results using Pandas Profiling indicated a strong negative correlation between heat treatment temperature and Yield Strength (YS) and Ultimate Tensile Strength (UTS). Additionally, a positive correlation was found between chemical elements such as Copper (Cu) and Nickel (Ni) with Elongation (EL). Furthermore, the analysis results revealed that stainless steel treated with water cooling exhibited a higher average UTS value, measuring at 493 MPa, compared to air cooling, which only reached 403 MPa. Pandas Profiling offers an effective solution to overcome common challenges in data-based mechanical property analysis, including dealing with large data volumes, complex data processing, as well as challenges in data visualization and interpretation. By utilizing Pandas Profiling, researchers can easily comprehend the dataset comprehensively, identify patterns, trends, and relationships among variables, and optimize the analysis process of data-based material mechanical properties.
Analisis Beban Kerja Dan Beban Mental Masinis Terhadap Keselamatan Perjalanan Kereta Api (Studi Kasus: Masinis pada Divre II Sumatera Barat): Analysis Of Workload And Mental Load Of Machinists On Train Travel Safety (Case Study: Machinists at Divre II West Sumatra) Afriyani, Sicilia; Nofriyanti, Elsa; Angelia, Nike; Sari, Putri Kumala; Fitria, Winda; Mayunita, Finisa Nur
Media Ilmiah Teknik Sipil Vol. 12 No. 3 (2024): Media Ilmiah Teknik Sipil
Publisher : ​Institute for Researches and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/mits.v12i3.8162

Abstract

As a mode of mass transportation, trains are one of the choices for people to travel. Safety and security of train travel are important factors in the implementation of railway transportation. One of the problems in railways is the low safety performance reflected by the high number of accidents. Machinists play an important role in the safety of railroad trave. The increasing number of schedules and trips as well as the lack of the number of machinists assigned to Divre II West Sumatra resulted in an increasingly dense schedule arrangement that affects the workload and mental burden felt by machinists and of course will also have an impact on the safety of the train journey itself. Therefore, it is necessary to conduct research on workload and mental load on machinists. This study aims to predicting the probability of train accidents in terms of factors affecting the level of fatigue, predicting the probability of train accidents in terms of factors affecting workload and mental load, analyzing the relationship between factors affecting workload and mental load felt by machinists in relation to train accidents. The method using modeling with a Bayesian Network Structure (SBN) using questionnaire data conducted on machinists in Divre II West Sumatera which is then analyzed using Gennie 2.2 Software to obtain the probability value of accidents that may occur to machinists based on the machinist classification level and finally find out what factors affect the workload and mental of machinists.
Co-Authors Adriansyah Adriansyah Adriansyah Afriyani, Sicilia Desmarita Leni Femi Earnestly Mayunita, Finisa Nur Nofriyanti, Elsa Ruzita Sumiati Sari, Putri Kumala Winda Fitria