Analisis Performa Algoritma Stochastic Gradient Descent (SGD) Dalam Mengklasifikasi Tahu Berformalin

Admojo, Fadhila Tangguh; Sulistya, Yudha Islami

doi:10.56705/ijodas.v3i1.42

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…The model was trained using optimization algorithms Stochastic Gradient Descent (SGD) [45] and Cross Entropy Loss Function (CLF) [46]. The experimental data utilized in this research from the rolling bearing condition monitoring bench at the University of Paderborn(UPB) [47], Germany.…”

Section: Bearing Fault Diagnosis Methodsmentioning

confidence: 99%

Bidirectional Current WP and CBAR Neural Network Model-Based Bearing Fault Diagnosis

Zhu,

Liu

2023

IEEE Access

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In the era of artificial intelligence, the development of an efficient bearing, fault diagnosis method is of vital importance to ensure smooth production operations and avoid major economic losses. To this end, this paper proposes a bearing fault diagnosis method based on biphasic currents. The method first performs wavelet denoising on the biphasic current signal, then extracts its features by simple vector representation and algebraic operations, and finally, combines the CBAR model of Convolutional Block Attention Module (CBAM) and Residual Network (ResNet) for bearing fault diagnosis. The experimental results show that the highest accuracy rate reaches 100% in both single-point fault and single-point mixed with multiple faults conditions on the open source current bearing fault diagnosis dataset, respectively. Compared with other methods, the method proposed in this paper has the advantage of simple data processing, concise model structure, and high-fault diagnosis accuracy, which provides an effective way for dual-phase current-based bearing fault diagnosis. It is worth emphasizing that based on wavelet denoising, this paper uses the simplest vector representation and algebraic operations to preprocess the signal (WP), making the method more efficient and easy to implement.

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Section: Bearing Fault Diagnosis Methodsmentioning

confidence: 99%

Bidirectional Current WP and CBAR Neural Network Model-Based Bearing Fault Diagnosis

Zhu,

Liu

2023

IEEE Access

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“…SGDC is a simple and efficient approach to classifying linearly using discriminatory learning. This method is an iterative (re)optimization algorithm that is useful for finding the minimum function point that can be derived [17,18]. At the beginning of the algorithm, the process begins by making guesses.…”

Section: Sgd Classifiermentioning

confidence: 99%

“…Updates are performed simultaneously for all j values = 0, ..., n. Variable α is the learning rate that regulates how much the value renewal is. The equation for the value of J(θ) can be seen in Equation ( 3), where L is the loss function used in the training data (x1, y1)...(xn, yx), and R is the regularization or penalty for model complexity [18].…”

Section: Sgd Classifiermentioning

confidence: 99%

STB Sentiment Analysis Classification Multiclass Modeling Using Calibrated Classifier With SGDC Tuning As Basis and Sigmoid Method

Ariansyah

Winarno²,

Salam³

2023

IJCIS

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A set-top box (STB) is a device that converts digital signals into images and sounds that we can see on a regular analog television (TV). More and more people are looking for STB these days because the ordinary use of analog television (TV) will end in December 2022. The discontinuation of the normal use of analog television (TV) has generated a lot of positive, neutral, and negative sentiments. This sentiment data was obtained from social media Twitter using crawling data techniques. The feature extraction process in this study uses the TF-IDF method. In this study Stochastic Gradient Descent Classifier (SGDC) is used as a basis for determining the optimal method and comparing the SGDC tuning method with the Calibrated Classifier method. The test results show that the best optimization for this model is the Calibrated Classifier method with SGDC as its basis with an accuracy value of 80% on the test data and 100% on the training data. It shows that the Calibrated Classifier method can slightly improve the performance of testing and training on the SGDC Classifier has an accuracy value of 78.4% on the test data and 100% on the training data.

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“…Pembaruan dilakukan secara 113 bersamaan untuk semua nilai j = 0, ..., 𝑛. Parameter 𝛼 adalah learning rate yang mengatur sejauh mana pembaruan nilai dilakukan. Persamaan nilai 𝐽(𝜃) dapat ditemukan dalam Persamaan (5), dimana L adalah fungsi kerugian yang digunakan pada data pelatihan (𝑥 1 , 𝑦 1 ), … (𝑥 𝑛 , 𝑦 𝑥 ) dan 𝑅 adalah regularisasi atau hukuman terhadap kompleksitas model [23].…”

Section: Proses Identifikasiunclassified

Identifikasi Jenis Kendaraan Bermotor Dengan Algoritma Convolutional Neural Networks

Athoillah,

Putri

2023

variance

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Deteksi jenis kendaraan bermotor memainkan peran sentral dalam pengaturan lalu lintas, penegakan hukum, keamanan, dan sistem transportasi pintar. Dengan kemampuan luar biasa dalam mendeteksi dan mengklasifikasikan kendaraan dengan akurat, pihak berwenang dapat mengoptimalkan waktu sinyal lalu lintas, pengelolaan jalur, dan aliran lalu lintas secara efisien. Deteksi jenis kendaraan juga memberikan dukungan penting dalam penegakan peraturan lalu lintas dan memverifikasi kepatuhan kendaraan terhadap batasan tertentu, termasuk jalur kendaraan bersama, tol, dan peraturan parkir. Di sisi keamanan, teknologi ini berperan krusial dalam mengidentifikasi kendaraan mencurigakan, mencegah ancaman, dan meningkatkan keselamatan di area sensitif. Salah satu pendekatan populer dalam mendukung sistem deteksi jenis kendaraan bermotor otomatis adalah menggunakan algoritma deep learning, khususnya Convolutional Neural Network (CNN). Dengan kemampuannya mengenali pola dan fitur pada citra kendaraan menggunakan struktur jaringan syaraf tiruan, CNN mampu memberikan hasil yang luar biasa. Penelitian ini bertujuan mengembangkan sistem otomatis deteksi jenis kendaraan bermotor dengan menggunakan algoritma CNN. Hasil penelitian menunjukkan kinerja yang sangat baik, dengan rata-rata presisi sebesar 97,00%, sensitivitas/recall sebesar 97,60%, spesifisitas sebesar 97,59%, dan akurasi sebesar 97,30%.

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Analisis Performa Algoritma Stochastic Gradient Descent (SGD) Dalam Mengklasifikasi Tahu Berformalin

Cited by 5 publications

References 9 publications

Bidirectional Current WP and CBAR Neural Network Model-Based Bearing Fault Diagnosis

Bidirectional Current WP and CBAR Neural Network Model-Based Bearing Fault Diagnosis

STB Sentiment Analysis Classification Multiclass Modeling Using Calibrated Classifier With SGDC Tuning As Basis and Sigmoid Method

Identifikasi Jenis Kendaraan Bermotor Dengan Algoritma Convolutional Neural Networks

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