Perancangan Metode Machine Learning Berbasis Web Untuk Prediksi Sifat Mekanik Aluminium

Leni, Desmarita; Kusuma, Yuda Perdana; Muchlisinalahuddin, Muchlisinalahuddin; Earnerstly, Femi; Muharni, Riza; Sumiati, Ruzita

doi:10.21776/jrm.v14i2.1370

Cited by 4 publications

(6 citation statements)

References 18 publications

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“…The use of machine learning methods in the material science field has been widely researched, such as predicting the mechanical properties of low alloy steel using Artificial Neural Network (ANN), where this study produced accurate modeling that aligned with experimental testing (Reddy et al, 2009). Reddy et al Research comparing machine learning algorithms conducted by Leni et al stated that ANN performed better in predicting the mechanical properties of low alloy steel based on chemical elements and heat treatment (Leni et al, 2022). However, using ANN to predict the mechanical properties of low alloy steel, there is an issue of feature selection, which is to determine the most influential variable in predicting the outcome.…”

Section: Introductionmentioning

confidence: 89%

The Influence of Heatmap Correlation-based Feature Selection on Predictive Modeling of Low Alloy Steel Mechanical Properties Using Artificial Neural Network (ANN) Algorithm

Leni

2023

J. Energy Mater. Instrum. Technol.

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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.

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Section: Introductionmentioning

confidence: 89%

The Influence of Heatmap Correlation-based Feature Selection on Predictive Modeling of Low Alloy Steel Mechanical Properties Using Artificial Neural Network (ANN) Algorithm

Leni

2023

J. Energy Mater. Instrum. Technol.

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“…Pemilihan CV dengan jumlah k=10, tidak terlepas dari keseimbangan yang baik antara varians dan bias dalam estimasi performa model. Jika jumlah K terlalu kecil maka estimasi performa model cenderung memiliki varian yang tinggi, jumlah K 10 cukup besar untuk memberikan perkiraan yang stabil namun tidak terlalu besar sehingga masih efisien dalam penggunaan data [18]. Selain itu, jumlah K 10 juga membantu mengurangi risiko overfitting karena model dievaluasi pada berbagai subset data yang berbeda, sehingga lebih mungkin untuk mendeteksi pola yang lebih umum [19]…”

Section: Evaluasi Modelunclassified

Seleksi Fitur Berdasarkan Korelasi Pearson dalam Pemodelan Efisiensi Energi Bangunan

Leni,

Dwiharzandis,

Sumiati

et al. 2023

JTS

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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.

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“…Therefore, rapid and accurate identification is an urgent need in the scope of automation and manufacturing. Machine learning is one branch of artificial intelligence (AI) that enables computers or machines to learn from provided data, and these models can improve their performance as training data accumulates in the dataset [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in [8], SVM is applied to Zero Desmarita Leni 1 , Moh. Chamim 2 , Ruzita Sumiati 3* , Yazmendra Rosa 4 , Hanif 5 Jurnal Teknik Mesin (JTM) Vol . 16 No.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Mechanical Component Classification Using Support Vector Machine with A Radial Basis Function Kernel

Sumiati,

Chamim,

Leni

et al. 2023

jtm

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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.

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Perancangan Metode Machine Learning Berbasis Web Untuk Prediksi Sifat Mekanik Aluminium

Cited by 4 publications

References 18 publications

The Influence of Heatmap Correlation-based Feature Selection on Predictive Modeling of Low Alloy Steel Mechanical Properties Using Artificial Neural Network (ANN) Algorithm

The Influence of Heatmap Correlation-based Feature Selection on Predictive Modeling of Low Alloy Steel Mechanical Properties Using Artificial Neural Network (ANN) Algorithm

Seleksi Fitur Berdasarkan Korelasi Pearson dalam Pemodelan Efisiensi Energi Bangunan

Modeling Mechanical Component Classification Using Support Vector Machine with A Radial Basis Function Kernel

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