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Alternative representations of RNN architecture Long Short Term Memory LSTM or Long-Short Term Memory is a type of RNN architecture that is expected to fix a simple RNN weakness. With LSTM, the model can predict a sentence that has a different context by re-entering the calculation results from the previous input on the hidden layer. Thus, the model can know what context is in the sentence and can prefigure out the output sentence to be removed [10].
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Sentiment analysis is computational research of the opinions of many people who are textually expressed against a particular topic. Twitter is the most popular communication tool among Internet users today to express their opinions. Deep Learning is a solution to allow computers to learn from experience and understand the world in terms of the hier...
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... Mereka melakukan ini menggunakan analisis sentimen berbasis tweet menggunakan skor TF-IDF dan Naive Bayes Classifier dengan akurasi 77% (Bhatnagar & Choubey, 2021). Pada penelitian yang di lakukan oleh Handayani dan Muastikasari, mereka melakukan klasifikasi sentimen tweet tentang mobil listrik menggunakan RNN menggunakan Confusion Matrix dengan Precision 0.618, Recall 0.507 dan Akurasi 72% (Handayani & Mustikasari, 2020). Dalam penelitian lain yang dilakukan oleh Costello dengan menggunakan data dari komentar video pada youtube dengan query "mobil listrik". ...
Analisis sentimen adalah studi tentang komputer yang mengubah item dan kualitasnya menjadi teks, yang mencakup opini dan perasaan publik. Dalam pembelajaran mesin, algoritma klasifikasi membantu mengidentifikasi sentimen positif dan negatif dalam pernyataan atau dokumen. Kendaraan listrik merupakan produk dari circular economy dan juga teknologi baru dalam dunia otomotif, dalam penelitian klasifikasi sentimen, peneliti menggunakan algoritma Support Vector Machine (SVM) karena memiliki akurasi yang lebih baik dibandingkan algoritma lainnya. Penelitian ini bertujuan untuk meningkatkan kualitas analisis sentimen dengan menggunakan metode pemilihan variabel SVM. Bahasa pemrograman Python digunakan untuk membangun model, seleksi fitur menggunakan TF-IDF, menggunakan Cross Validation dan grid search untuk pelatihan, confusion matrix untuk evaluasi, dan menggunakan basis data MySQL untuk menyimpan dataset. Penelitian ini melibatkan klasifikasi sentimen dari 3000 tweet publik tentang kendaraan listrik di Twitter. Pada pengujian yang dilakukan dengan delapan skenario, akurasi terendah sebesar 81.3% dengan skenario tanpa data acak, tanpa penanganan negasi, dan bigram kemudian akurasi tertinggi sebesar 84% dengan skenario data acak, menggunakan penanganan negasi, dan unigram, dengan jumlah fitur unigram 182 dan bigram 116 setelah dilakukan seleksi fitur. Dari kedelapan skenario tersebut dapat disimpulkan bahwa pengacakan data dan fitur berupa unigram maupun bigram mempengaruhi akurasi klasifikasi sentimen pada kasus kendaraan listrik di Twitter.
... LSTM has gates that function to add or remove information. The gates are Forget gate, Input Gate, Cell State and Output gate [13], [14]. The way the gates work in LSTM is as follows : ...
The reason fishermen lose control is wave height and wind speed. The impact is also felt by all users of the marine sector. This research uses the Long Short Term Memory (LSTM) method because this method has accurate values in the forecasting process with a lot of historical data and uses the Prophet method to detect outliers with Newton interpolation to replace the detected outlier data. The total number of data was 2074 obtained from BMKG Perak Surabaya from January 2020 to November 2022 at four research points, namely north, northeast, east and south points. The test results provide varying error values with MAPE as the model evaluation value. The error value for sea wave height at the north, northeast, east and south points is 13.32 respectively; 13.32; 9.32 and 8.85 with data without interpolation. Meanwhile, the error value in the wind speed data is 14.74; 14.85; 15.14 and 14.52 with a 3rd order Newton interpolation process at the northeast and east points. MAPE values below 20% prove that the LSTM model is good for predicting wave height and wind speed data at four points in Sumenep Regency. The system implementation is made into a web-based application.
