Algoritma Deep Learning-LSTM untuk Memprediksi Umur Transformator

Ningrum, Ayu Ahadi; Syarif, Iwan; Gunawan, Agus; Satriyanto, Edi; Muchtar, Rosmaliati

doi:10.25126/jtiik.2021834587

Cited by 6 publications

(6 citation statements)

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“…Dalam hal ini, strategi pembelajaran mendalam dengan LSTM lebih disarankan daripada ketiga perhitungan lainnya, dengan nilai RMSE = 0,0004 dan Squared Correlation = 0,9690 (Ningrum et al, 2021).…”

Section: Pendahuluanunclassified

Analisis Sentimen Terhadap Pengaruh Minat Belanja Berdasarkan Komentar di Marketplace Menggunakan Metode Recurrent Neural Network (RNN)

Lasatira,

Hartomo,

Sembiring

2023

JSinbis

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Product reviews on the marketplace can provide useful information if they are properly processed. Product review analysis can be performed by merchants to obtain information that can be used to evaluate products and services. It is not enough to look at the number of stars in product review analysis activities; it is also necessary to look at the entire contents of the review comments to determine the intent of the review. This can be done manually in small quantities, but in large quantities, the system is more efficient. In order to understand the intent of the reviews, a system that can effectively analyze many reviews is required. Using the Recurrent Neural Network (RNN) method, this study aims to analyze sentiment on the influence of shopping interest based on comments in the marketplace. The RNN model is trained to recognize positive and negative sentiments using data from the marketplace. The sentiment analysis results will be used to assess the impact on user shopping interest in the marketplace. Sentiment analysis was performed in this study using the RNN method in the GRU/LSTM training model with epochs. The researcher determined the epoch to achieve high accuracy. The data used for model training and testing is separated into training and testing data before it is used. A comparison of 80% of training data and 20% of test data is used to split data. This study uses a training model with 77 epochs and a batch size of 128 to create a system that automatically calculates comment sentiment in the marketplace with a 100% accuracy value and determines positive and negative sentiments.

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Section: Pendahuluanunclassified

Analisis Sentimen Terhadap Pengaruh Minat Belanja Berdasarkan Komentar di Marketplace Menggunakan Metode Recurrent Neural Network (RNN)

Lasatira,

Hartomo,

Sembiring

2023

JSinbis

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“…Dalam prediksi genre film, sinopsis film dapat dianggap sebagai data yang memiliki urutan kata-kata yang penting untuk memahami konsep film tersebut. Dengan menggunakan jaringan LSTM, kita dapat memanfaatkan kemampuan jaringan ini untuk mengekstraksi fitur-fitur penting dari sinopsis dan memprediksi genre film secara akurat (Ningrum et al, 2021).…”

Section: Pendahuluanunclassified

Prediksi Genre Film Dengan Klasifikasi Multi Kelas Sinopsis Menggunakan Jaringan LSTM

Amelia,

Santoso

2023

INTECOMS

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Sinopsis film menyajikan informasi penting mengenai alur cerita dan tema yang dapat digunakan untuk memprediksi genre film. Namun, memprediksi genre film secara manual dengan melihat sinopsis terlebih dahulu dapat menjadikannya kurang efektif karena sinopsis yang panjang dan sangat bervariasi. Oleh karena itu, diperlukannya pendekatan otomatis dengan menggunakan teknik klasifikasi multi kelas dengan jaringan LSTM (Long Short-Term Memory) untuk memprediksi genre film berdasarkan sinopsisnya guna memberikan kontribusi yang bermanfaat dalam industri perfilman. Penelitian ini menggunakan jaringan LSTM untuk klasifikasi multi kelas untuk memprediksi genre film dengan dataset yang diambil dari situs Kaggle yang terdiri dari berbagai sinopsis film dengan genre yang berbeda-beda. Setelah itu, sinopsis film tersebut diolah melalui proses tokenisasi, vektorisasi, embedding kata menggunakan NLP (Natural Language Processing). Hasil pengujian terhadap dataset dengan menggunakan metode klasifikasi multi kelas dengan jaringan LSTM mampu mencapai tingkat akurasi 98% dan loss 5% dalam memprediksi genre film berdasarkan sinopsis

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“…Bi-LSTM consists of two LSTM networks that have a function to process data sequences from the forward and backward directions [22]. LSTM itself will learn the data that must be removed and stored in each neuron [23]. The algorithm is a development of RNN by overcoming problems about vanishing gradients [24].…”

Section: B Data Preprocessingmentioning

confidence: 99%

Next Word Prediction for Book Title Search Using Bi-LSTM Algorithm

Trigreisian,

Harani,

Andarsyah

2023

ijcs

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Finding a suitable book title is still quite difficult at the moment. We often guess what book title we want, but in reality the book title is often not available. This research aims to overcome these problems by producing an accurate and efficient prediction model in predicting the next words in book title search using a deep learning algorithm, namely Bidirectional Long Short Term Memory (Bi-LSTM). The research stages consist of data collection, data preprocessing, data modeling, evaluation, and implementation. This research uses a dataset of Indonesian book titles obtained from the bukukita.com online bookstore website with 5618 data. The results show that the resulting deep learning model can predict the next words in the book title search with an accuracy of 81.82%. The model is implemented in the form of a web application using the Django framework, Python language, and MySQL database.

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Algoritma Deep Learning-LSTM untuk Memprediksi Umur Transformator

Cited by 6 publications

References 0 publications

Analisis Sentimen Terhadap Pengaruh Minat Belanja Berdasarkan Komentar di Marketplace Menggunakan Metode Recurrent Neural Network (RNN)

Analisis Sentimen Terhadap Pengaruh Minat Belanja Berdasarkan Komentar di Marketplace Menggunakan Metode Recurrent Neural Network (RNN)

Prediksi Genre Film Dengan Klasifikasi Multi Kelas Sinopsis Menggunakan Jaringan LSTM

Next Word Prediction for Book Title Search Using Bi-LSTM Algorithm

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