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All Journal Jurnal Teknik Mesin Rekayasa Mesin Prosiding Semnastek Dinamika Teknik Mesin Jurnal Rekayasa Material, Manufaktur & Energi Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Journal of Dynamics Logista: Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal TEMAPELA JTM - Jurnal Teknik Mesin Jurnal Teknik Mesin Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Vokasi Jurnal Teknik Industri Terintegrasi (JUTIN) Jurnal Surya Teknika Jurnal Pengabdian dan Peningkatan Mutu Masyarakat (JANAYU) Teknika Sains : Jurnal Ilmu Teknik Jurnal Teknik Mesin METAL : Jurnal Sistem Mekanik dan Termal Industrial Research Workshop and National Seminar Jurnal Andalas: Rekayasa dan Penerapan Teknologi International Journal of Innovation in Mechanical Engineering and Advanced Materials Proceeding Applied Business and Engineering Conference Jurnal Polimesin IJIMCE : International Journal of Innovation in Mechanical Construction and Energy
Ruzita Sumiati
Politeknik Negeri Padang
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THE IMPLEMENTATION OF PANDAS PROFILING AS A TOOL FOR ANALYZING MECHANICAL PROPERTIES DATA OF NICKEL-BASED SUPERALLOYS BASED ON ALLOY CHEMICAL COMPOSITION Desmarita Leni; Yuda Perdana Kusuma; Muchlisinalahuddin Muchlisinalahuddin; Ruzita Sumiati; Hendri Candra Mayana
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol 4, No 3 (2022)
Publisher : Universitas Mercu Buana, Prodi S2 Teknik Mesin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v4i3.19439

Abstract

The purpose of this study is to evaluate the mechanical properties of nickel-based superalloys with variations in alloy chemical compositions using the Exploratory Data Analysis (EDA) method with the assistance of the pandas profiling library on Google Colab. In this study, data from 312 tensile tests of nickel-based superalloys were used as research samples, with alloy chemical compositions including carbon (C), manganese (Mn), silicon (Si), chromium (Cr), nickel (Ni), molybdenum (Mo), vanadium (V), nitrogen (N), niobium (Nb), cobalt (Co), tungsten (W), aluminum (Al), and titanium (Ti), as well as mechanical properties such as yield strength (YS), tensile strength (TS), and elongation (EL). The methodology used in this study was the EDA method with the assistance of the pandas profiling library on Google Colab, which enables the automatic creation of a dataset report, presenting information on various aspects such as data structure, descriptive statistics, correlation, distribution, and missing values. The results show that yield strength has a fairly high correlation with titanium (0.51), medium correlations with nickel (0.25), vanadium (0.2), and cobalt (0.2). Tensile strength in nickel-based superalloys has a fairly high correlation with yield strength (0.88), carbon (0.49), and cobalt (0.55), and medium correlations with titanium (0.25) and vanadium (0.25). Elongation in nickel-based superalloys has a negative and fairly high correlation with tensile strength (-0.62) and yield strength (-0.58). Some warnings for missing data and zero values in some variables were identified. These results indicate that the pandas profiling library can be used as a tool to analyze the data of mechanical properties of nickel-based superalloys quickly and easily, and provide clear information on data patterns, data structure, and correlation among data.
Perancangan Mesin CNC Acrylic Cutting 3 Axis Dengan Menggunakan Laser Tube CO2 Elvando andha elvaris manalu; Asmed Asmed; Mulyadi Mulyadi; Yuliarman Yuliarman; Ruzita Sumiati
Jurnal Teknik Mesin Vol 16 No 1 (2023): Jurnal Teknik Mesin
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/jtm.16.1.880

Abstract

Almost all activities are carried out with the help of technology. It is as if technology has become a basic need for human life today. Of all the areas that have made progress, machinery is the one that has had the greatest effect. Lots of machines have emerged with new innovations with very diverse functions, one of which is the Computer Numerical Control (CNC) machine. A CNC machine is a machine used in the manufacturing industry to quickly produce large quantities of components for the engineering sector. As the name of the CNC itself, every work from CNC uses a computer system that has been well formed to produce goods that are in accordance with what is desired. Departing from some of the problems above, the author concluded to make a similar technology at a much cheaper price with a more practical work system, therefore the author made a 3 Axis Acrylic Cutting CNC Machine Using a CO2 Laser Tube".
Pemodelan Machine Learning untuk Memprediksi Tensile Strength Aluminium Menggunakan Algoritma Artificial Neural Network (ANN) Desmarita Leni; Helga Yermadona; Ade Usra Berli; Ruzita Sumiati; Haris Haris
Jurnal Surya Teknika Vol 10 No 1 (2023): JURNAL SURYA TEKNIKA
Publisher : Fakultas Teknik UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jst.v10i1.4843

Abstract

This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.
PERANCANGAN METODE MACHINE LEARNING BERBASIS WEB UNTUK PREDIKSI SIFAT MEKANIK ALUMINIUM Desmarita Leni; Arwizet K; Ruzita Sumiati; Haris Haris; Adriansyah Adriansyah
Jurnal Rekayasa Mesin Vol. 14 No. 2 (2023)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v14i2.1370

Abstract

The main objective of this research is to design a web-based machine learning model that can predict the mechanical properties of aluminum based on its chemical composition. By inputting nine variables of chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, and Si, the model is able to provide predictions for two output data, Yield Strength (YS) and Tensile Strength (TS). The research aims to understand the relationship between chemical composition and mechanical properties of aluminum, and to develop a tool that can be used to predict these properties with a high level of accuracy. Overall, the goal of this study is to enhance the understanding of the properties of aluminum and how it can be utilized in various applications. This study designs a web-based machine learning modeling to predict the mechanical properties of aluminum in the percentage of chemical composition, where the input data in the modeling consists of 9 variables of chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and has 2 output data consisting of Yield Strength (YS) and Tensile Strength (TS). The modeling machine learning is designed using the Python programming language and additional libraries such as Pandas, Numpy, Scikit-learn, and Streamlit. The modeling in this study uses three algorithms consisting of Decision Trees (DT), Random Forest (RF), and Artificial Neural Network (ANN). Each algorithm is optimized with the best search parameters, and where the RF algorithm has better performance than DT and JST. The best modeling uses the RF algorithm with optimal parameters of number of trees at 20 and maximum depth of 10, with MAE values of 11.44, RMSE of 14.282, and R of 0.93 for Yield Strength (YS) predictions, and for Tensile Strength (TS) predictions, MAE values are obtained. 21,669, RMSE 27,301, and R 0.871. 
Perancangan Aplikasi Berbasis Web Sebagai Alat Pendukung Keputusan Dalam Memilih Ac Hemat Energi Maimuzar .; Ruzita Sumiati; Haris .; Desmarita Leni; Aggrivina Dwiharzandis
Rekayasa Material, Manufaktur dan Energi Vol 6, No 2: September 2023
Publisher : Fakultas Teknik UMSU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/rmme.v6i2.16255

Abstract

The increase in global energy demand has driven the need for efficient solutions in selecting energy-efficient air conditioners (ACs). This research focuses on designing a web-based application as a decision support tool for choosing energy-efficient ACs. Energy-efficient labeled AC data is obtained from the Directorate General of New and Renewable Energy and Energy Conservation (EBTKE) website. This database is processed according to the system's requirements, where each AC brand is evenly represented to prevent dominance by a few brands. There are 11 different AC brands in this dataset, and each brand has 10 data samples. The web-based application is developed using the Python programming language with the Streamlit framework. This application allows users to compare various AC brands by considering power, annual energy consumption, efficiency value, and electricity cost. In the application design, users can select AC brands according to their needs, set the operating duration, choose the AC efficiency level, and select the inverter AC type. The application presents comparisons in the form of bar charts, making it easy for users to understand the differences in AC characteristics. The average results from the efficiency comparison of each AC brand reveal that Daikin achieves the highest efficiency at 16.36 Energy Efficiency Ratio (EER), while the GREE brand has the lowest efficiency at 5.83 EER. This application can assist consumers and industrial AC stakeholders in making decisions to choose energy-efficient ACs according to their needs.
PEMBUATAN ALAT TRAINER STARTING SYSTEM UNTUK MEDIA PRATIKUM JURUSAN KENDARAAN RINGAN SMK DHUAFA PADANG Rivanol Chadry; Desmarita Leni; Nofriadi Nofriadi; Ruzita Sumiati; Dadi Budiman; Mulyadi Mulyadi
Jurnal Vokasi Vol 7, No 3 (2023): Jurnal Vokasi (November)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/vokasi.v7i3.4186

Abstract

Tim PKM PNP jurusan Teknik Mesin telah berhasil memberikan kontribusi yang dalam upaya meningkatkan kualitas media praktikum di SMK Dhuafa Padang, khususnya dalam bidang jurusan kendaraan ringan. Dengan pembuatan Alat Trainer Starting System yang efektif dan efisien, tercipta sebuah media praktikum yang memadai bagi para siswa-siswi. Alat praktikum ini memungkinkan siswa-siswi untuk secara efektif mengaplikasikan teori yang telah dipelajari dan mengembangkan keterampilan yang esensial dalam industri kendaraan bermotor. Untuk melihat kepuasan dari mitra tim pengabdian memberikanangket kepada beberapa orang guru kendaraan ringan, dan hasil menunjukkan kepuasan mereka terhadap alat media pembelajaran yang diberikan. Mengingat pentingnya Starting System dalam mesin kendaraan, pendekatan kurikulum SMK Dhuafa Padang yang fokus pada praktikum menjadi semakin terwujud. Meskipun tantangan hadir dalam bentuk keterbatasan alat praktikum yang tersedia, kolaborasi ini berhasil mengatasi hambatan tersebut dan memberikan solusi berkelanjutan. Sejalan dengan kemajuan teknologi pada kendaraan bermotor, inovasi di dalam pembelajaran menjadi semakin dibutuhkan, dan SMK Dhuafa Padang menjadi pilar penting dalam mempersiapkan siswa-siswi untuk menghadapi perubahan tersebut. Oleh karena itu, melalui pengabdian masyarakat ini, dampak positif telah diberikan kepada pendidikan siswa-siswi di jurusan kendaraan ringan SMK Dhuafa Padang.
Seleksi Fitur Berdasarkan Korelasi Pearson dalam Pemodelan Efisiensi Energi Bangunan Desmarita Leni; Aggrivina Dwiharzandis; Ruzita Sumiati; Haris Haris; Sicilia Afriyani
TEKNIKA SAINS Vol 8, No 2 (2023): TEKNIKA SAINS
Publisher : Universitas Sang Bumi Ruwa Jurai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24967/teksis.v8i2.2525

Abstract

Prediksi beban pemanasan dan pendinginan bangunan merupakan langkah penting untuk perencanaan dan pengelolaan sistem energi. Hal ini, tidak terlepas dari berkontribusi beban pemanasan dan pendinginan bangunan yang menyumbang 30% dari total konsumsi energi global. Penelitian ini bertujuan untuk menerapkan metode seleksi fitur berdasarkan korelasi Pearson dalam pemodelan prediksi beban pemanasan dan pendinginan bangunan menggunakan Artificial Neural Network (ANN). Korelasi Pearson digunakan untuk menganalisis hubungan antara variabel input dan variabel target. Fitur-fitur yang memiliki korelasi signifikan dengan variabel target digunakan sebagai dataset untuk pelatihan model, sedangkan yang tidak memiliki korelasi signifikan dihapus dari dataset pelatihan. Evaluasi dilakukan menggunakan metrik evaluasi seperti Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan R-squared untuk mengukur tingkat keakuratan dan kinerja model dalam memprediksi beban pemanasan dan pendinginan. Hasil pemodelan menunjukkan bahwa seleksi fitur berdasarkan korelasi Pearson menghasilkan prediksi yang sangat akurat untuk beban pemanasan dan pendinginan bangunan. Model ini menunjukkan kinerja yang baik selama pelatihan dan validasi dengan Cross Validation (CV) menggunakan k = 10. Hasil evaluasi model diperoleh nilai MAE 0.457, RMSE 0.628, dan R-squared 0.996 untuk beban pemanasan, sedangkan untuk beban pendinginan diperoleh nilai MAE sebesar 1.163, RMSE 1.74, dan R-squared 0.967. Hasil ini mengindikasikan bahwa seleksi fitur dengan korelasi Pearson dapat dijadikan pendekatan yang efektif untuk meningkatkan performa model prediksi menggunakan machine learning, terutama dalam konteks prediksi beban pemanasan dan pendinginan bangunan.
Comparative analysis of energy-efficient air conditioner based on brand Adriansyah Adriansyah; Desmarita Leni; Ruzita Sumiati
Jurnal POLIMESIN Vol 21, No 4 (2023): August
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v21i4.3625

Abstract

The availability of numerous air conditioners in the market with various brands and types often leads consumers to be unaware that the purchased air conditioner may be inefficient in terms of energy usage. This research aims to determine the most energy-efficient air conditioner based on the brand of air conditioners available in the market. The research method consists of four stages: data collection, data preprocessing, data analysis, and interpretation of results and conclusions. The data used in this study was obtained from the database of the Directorate General of New, Renewable, and Energy Conservation (EBETKE), which consists of 11 AC brands sold in the market. Data analysis was performed using data distribution analysis techniques, standard deviation calculations, and correlation analysis between variables, such as the Pearson's correlation coefficient. The results of this study show that the AC brand with the highest average efficiency value is Mitsubishi Electric, with a value of 16.36 Energy Efficiency Ratio (EER), while the AC brand with the lowest average efficiency value is GREE, with a value of 5.640 (EER). Each AC brand has a different average efficiency value, with significant variations. From the correlation heatmap results, the AC power does not appear to significantly affect the AC efficiency value, where AC with lower power tends to have higher efficiency values, but there are also AC with high power and high efficiency values. Additionally, the cooling capacity value also appears to have a small effect on the AC efficiency value, where AC with lower cooling capacity tends to have higher efficiency values. However, some AC brands have high cooling capacity values but also have high efficiency values. This study also shows a moderate correlation between the AC efficiency value and the AC's annual energy consumption value, where AC with higher efficiency values tends to have lower annual energy consumption values.
Predictive Modeling For Low Alloy Steel Mechanical Properties: A Comparison Of Machine Learning Algorithms And Parameter Optimization Desmarita Leni; Lega Putri Utami; Ruzita Sumiati; Moh. Camim; Sharif Khan
IJIMCE : International Journal of Innovation in Mechanical Construction and Energy Vol. 1 No. 1 (2024): IJIMCE : International Journal of Innovation in Mechanical Construction and Ene
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ijimce.v1i1.7

Abstract

The development of machine learning in predicting the mechanical properties of alloy steel has become an important research subject in recent years. This is due to the ability of machine learning to extract complex patterns from large and intricate data, which can be used to understand the relationship between chemical composition, microstructure, and mechanical properties of alloy steel. This research aims to design a machine learning model to predict the mechanical properties of low alloy steel, such as Yield Strength (YS) and Ultimate Tensile Strength (UTS), based on the percentage composition of chemical elements in low alloy steel and the heat treatment applied. The machine learning model in this study consists of 10 input variables and 2 target variables. The research compares the performance of 3 machine learning algorithms, namely Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The research findings indicate that the ANN algorithm model performs best in predicting the mechanical properties of low alloy steel. This model has Mean Absolute Error (MAE) values of 16.5 and 19.593 for predicting YS and UTS, Root Mean Square Error (RMSE) values of 19.111 and 22.005, and coefficient of determination (R) values of 0.964 and 0.947 for YS and UTS respectively. The modeling uses the ANN algorithm with an 80% training data and 20% testing data split, and applies the K-Fold Cross Validation method with a value of K=5. The best parameters obtained are a learning rate of 0.001, momentum of 0.1, and a hidden layer neuron count of 9. These results indicate that ANN has great potential in addressing the complexity and variability in material data. The implications of these findings are that the implementation of ANN in manufacturing and material engineering industries can enhance the accuracy and efficiency in material strength prediction processes, which, in turn, can aid in designing and developing better and more durable products.
Modeling Mechanical Component Classification Using Support Vector Machine with A Radial Basis Function Kernel Ruzita sumiati; Moh. Chamim; Desmarita Leni; Yazmendra Rosa; Hanif Hanif
Jurnal Teknik Mesin Vol 16 No 2 (2023): Jurnal Teknik Mesin
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/jtm.16.2.1250

Abstract

The process of identification and classification of products in the era of modern manufacturing industries has become a crucial pillar in enhancing efficiency, productivity, and product quality. In this research, the modeling of manufacturing product classification, such as mechanical components consisting of four classes: bolts, washer, nuts, and locating pin, was conducted. The proposed model in this study is the Support Vector Machine (SVM) with Radial Basis Function (RBF). The dataset used consists of digital images of mechanical components, with each component having 400 samples, resulting in a total of 1600 samples. The dataset is divided into training and testing data, with 300 samples for each component in the training set, and 100 samples removed from the training set for external testing as model validation. The best model parameters were determined using grid search by varying the parameter values of C and γ (gamma). The model was evaluated using K=3 fold cross-validation, and external testing utilized a confusion matrix to calculate Accuracy, Precision, Recall, and F1-Score values. The research results indicate that the SVM model with the RBF kernel, using the combination of C=10 and γ=scale, achieves the best performance in classifying the four mechanical components. This is evident from the training results of the model, which were able to obtain evaluation metrics such as Accuracy of 94.17%, Precision of 0.94, Recall of 0.94, and F1-Score of 0.94. Meanwhile, the validation results with new data not present in the training dataset show that the model can achieve evaluation metrics with an Accuracy of 93%, Precision of 0.93, Recall of 0.93, and F1-Score of 0.93. These results are consistent with the training performance, indicating that the SVM model with the RBF kernel excels in classifying digital images of mechanical components, such as bolts, nuts, washer, and locating pin.
Co-Authors Ade Usra Berli Adriansyah Adriansyah Afifah, Afifah Aggrivina Dwiharzandis Aggrivina Dwiharzandis Aidil Zamri Aidil Zamri Andrew Kurniawan Vadreas, Andrew Kurniawan Anissa Vivia Fidela Arwizet K Asmed Asmed Bukhari Dadi Budiman Dandi Ilham Delffika Canra Desmarita Leni Desmarita Leni Doni Marzuki Elvando andha elvaris manalu F. Earnestly Fahri Reza Fahri Triharyono Fanni Sukma Fardinal Fardinal Fardinal, Fardinal Fathir Alqodri Fharel Abdillah Fitri Adona Genta Ramadeto Ghandy Junne Putra Gusriwandi Gusriwandi Hamzah Putra Hanif Hanif Hanif Hanif Haris Haris Helga Yermadona Hendri Candra Mayana Ikbal Jana Hafiza Khairul Amri Khairul Amri Lega Putri Utami M. Luthfi Artia Maimuzar Maimuzar Meiki Eru Putra Moh. Camim Moh. Chamim Muchlisinalahuddin Mulyadi Nandi Pinto Nofriadi Nofriadi Nofriyandi, R Nusyirwan Nusyirwan Rahil Abde Andika Rajimar Suhal Hasibuan Rakiman Rakiman Rakiman Rakiman Ridho Pratama Fajri Riezky Idvi Alfitra Rina Rina Rina Rina Rina, Rina Rino Sukma Riswan Riswan Rivanol Chadry Robby Novrizal Saputra M, Dendi Adi Sharif Khan Sicilia Afriyani Siska Angraini Rikosa Rikosa Suliono Suliono Tri Ego Wiranata Uyung Gatot S. Dinata Uyung Gatot Syafrawi Dinata veny Selviyanty.YH, veny Yanziwar Yanziwar Yazmendra Rosa Yogi Kogama Yuda Perdana Kusuma Yuhefizar Yuhefizar Yuli Yetri Yuliarman Yuliarman Yuliarman Yuliarman Yusri Mura