Implementasi LSTM Pada Analisis Sentimen Review Film Menggunakan Adam Dan RMSprop Optimizer

Witanto, Karlina Surya; Er, Ngurah Agus Sanjaya; Karyawati, Anak Agung Istri Ngurah Eka; Kadyanan, I Gusti Agung Gede Arya; Suhartana, I Ketut Gede; Astuti, Luh Gede

doi:10.24843/jlk.2022.v10.i04.p05

Cited by 5 publications

(6 citation statements)

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“…1, the input text used is from the column of public opinions on Twitter. The process involves deleting null values, i.e., removing columns with null values to prevent issues during the model training and testing processes [10], performing case folding to convert text data into lowercase [1], cleaning text containing usernames, numbers, URLs, emojis, spaces, and other meaningless symbols [1] [11], separating sentences into individual words, known as tokenizing [1] [12]. Subsequently, stopword removal is carried out, which involves eliminating words based on a predefined stopword list.…”

Section: Data Transformation and Text-preprocessingmentioning

confidence: 99%

“…The quality of service offered serves as a critical factor influencing patients' choices when selecting Puskesmas for their medical needs. However, there is a limited focus on evaluating the performance of Puskesmas services in existing studies [1]. With the continuous growth of the population, Community Health Centers are compelled to compete in delivering optimal care to patients, thereby enhancing the overall reputation of the center [2].…”

Section: Introductionmentioning

confidence: 99%

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LSTM and Word Embedding: Classification and Prediction of Puskesmas Reviews Via Twitter

Tukino,

Hananto,

Nanda

et al. 2024

E3S Web Conf.

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Puskesmas is a location for top-notch volunteer work that benefits the village and city governments alike. Therefore, patient feedback regarding the kinds of services offered by the community health center is required in an attempt to improve the quality service performance. Patient opinions can be expressed through reviews or opinions about the quality of patient care on social media sites like Facebook, Instagram, Twitter, WhatsApp, and Instagram. On the other hand, thoughts shared on social media are lengthy, unstructured texts. This complicates text analysis and makes it impossible to compare the caliber of services offered by Puskesmas managers. Furthermore, a number of Community Health Centers lack websites that allow users to rank Community Health Centers according to user interest and visual appeal and efficiency in operations. Thus, the purpose of this study is to classify and present sentiment analysis from Twitter about community health centers' health services. The scope focuses on five factors: administrative services, finances, mechanisms, health worker friendliness and skills, and administrative services. The LSTM word embedding model and the adadelta and adamax optimizers are used in word embedding for text mining. A confusion matrix was used to evaluate the developed model's degree of accuracy in categorizing and forecasting patient reviews. Results from the LSTM and Adamax models with a precision level of 76%, Recall 69% and Accuracy 71%. The results of this research show that the LSTM method and Adamax optimizer can classify and predict public opinion data about Puskesmas services via Twitter quite well. A high level of accuracy is very important to ensure that community opinions can be properly identified by the model, so that it can support the decision-making process in improving the type of Puskesmas services. To improve the model, further studies can be conducted on how to select parameters, select features, and create a quality dataset.

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Section: Data Transformation and Text-preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LSTM and Word Embedding: Classification and Prediction of Puskesmas Reviews Via Twitter

Tukino,

Hananto,

Nanda

et al. 2024

E3S Web Conf.

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“…Word Embedding adalah sebuah metode yang digunakan untuk mengubah sequence kata menjadi vektor [5]. Pada penelitian ini, Word Embedding digunakan sebagai layer awal pada model, setiap judul berita yang berupa sequence 20 token diubah menjadi vektor berukuran 64 nilai [6].…”

Section: Word Embeddingunclassified

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Nugraha,

Mahendra

2023

JLK

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“…Epochs yang digunakan dalam penelitian ini yaitu 10, 15, 20, 30, dan 50. RMSprop merupakan metode optimasi yang menggunakan besarnya gradien terkini sehingga bermanfaat untuk menormalkan gradien, alasan fungsi ini disebut RMS karena mampu mempertahankan rata-rata bergerak di atas gradien root mean square [17]. Algoritma RMSProp berasal dari konsep penurunan gradien dan Resilient Back Propagation (RProp), yang berada di ranah tingkat pembelajaran adaptif dan dirancang untuk pelatihan Jaringan Syaraf Tiruan.…”

Section: Pelatihan Model Cnn Dan Validasi Model Cnnunclassified

Convolutional Neural Network Untuk Perbandingan Optimizer Pada Citra Batang Pohon

Zuzzaifa,

Rianto

2023

JSC

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In the surrounding environment, there are various types of trees with different characteristics. One characteristic of a tree that is difficult to distinguish is its trunk. After researchers made observations, the trunks of Pine and Tabebuya trees had the same characteristics, namely cracking. The problem of incorrectly identifying the characteristics of a tree's trunk can be overcome by classification. Deep Learning with the Convolutional Neural Network (CNN) algorithm is a method commonly used in image classification. The stages in this research include image data retrieval, data preprocessing, CNN architecture formation, model training, and model validation. Image retrieval was carried out directly by researchers, then the 1000 best images were selected. The image dataset is then divided into 75% training data and 25% validation data. Testing was carried out by comparing the Stochastic Gradient Descent (SGD) optimizer and the Adaptive Learning Rate Optimization Method (RMSprop) using epochs 10, 15, 20, 30, 50, and 80. The results showed that the SGD optimizer produced the highest accuracy compared to the RMSProp optimizer. The most optimal result when applying the SGD optimizer is 0.9360 with epochs 80, while for the RMSProp optimizer it is 0.9160 with epochs 20.

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Implementasi LSTM Pada Analisis Sentimen Review Film Menggunakan Adam Dan RMSprop Optimizer

Cited by 5 publications

References 0 publications

LSTM and Word Embedding: Classification and Prediction of Puskesmas Reviews Via Twitter

LSTM and Word Embedding: Classification and Prediction of Puskesmas Reviews Via Twitter

Untitled

Convolutional Neural Network Untuk Perbandingan Optimizer Pada Citra Batang Pohon

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