<> <http://www.w3.org/2000/01/rdf-schema#comment> "The repository administrator has not yet configured an RDF license."^^<http://www.w3.org/2001/XMLSchema#string> .
<> <http://xmlns.com/foaf/0.1/primaryTopic> <http://digilib.unila.ac.id/id/eprint/85424> .
<http://digilib.unila.ac.id/id/eprint/85424> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://purl.org/ontology/bibo/Article> .
<http://digilib.unila.ac.id/id/eprint/85424> <http://purl.org/dc/terms/title> "IMPLEMENTASI MODEL HYBRID CONVOLUTIONAL NEURAL\r\nNETWORK DAN BIDIRECTIONAL LONG SHORT-TERM MEMORY\r\nUNTUK ANALISIS TINGKAT KEPUASAN PENGGUNA\r\n\r\nAPLIKASI HALODOC"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/eprint/85424> <http://purl.org/ontology/bibo/abstract> "Perkembangan teknologi mengakibatkan terjadinya kemajuan teknologi sehingga\r\nmemunculkan berbagai jenis layanan yang memudahkan akses terhadap fasilitas\r\npelayanan termasuk pelayanan kesehatan dan mendorong pengembangan berbagai\r\njenis aplikasi. Aplikasi penyedia layanan kesehatan pilihan masyarakat salah\r\nsatunya yaitu aplikasi Halodoc. Ulasan dan rating dapat menjadi informasi bagi\r\npengguna lainnya mengenai kualitas pelayanan dari aplikasi Halodoc. Oleh karena\r\nitu, diperlukan cara yang tepat untuk menganalisis kepuasan pelanggan terhadap\r\npelayanan aplikasi Halodoc. Metode Deep learning yang dapat digunakan untuk\r\nmelakukan klasifikasi data teks adalah Convolutional Neural Network (CNN),\r\nLong Short-Term Memory (LSTM), dan Bidirectional Long Short-Term Memory\r\n(BiLSTM). Penelitian ini menerapkan resampling data menggunakan random\r\noversampling, word embedding menggunakan Word2Vec, dan hyperparameter\r\ntuning menggunakan Grid Search. Setelah dilakukan pengujian hyperparameter\r\ntuning didapatkan nilai learning rate dan nilai batch size terbaik, yaitu batch size\r\ndengan nilai 64 dan learning rate dengan nilai 1 × 10−3\r\n\r\n. Secara umum arsitektur\r\nmodel hybrid CNN-BiLSTM yang dibangun dalam penelitian ini yaitu lapisan\r\nembedding, lapisan konvolusi, lapisan pooling, lapisan BiLSTM, flatten, lapisan\r\nfully connected, dan lapisan output. Hasil perhitungan menunjukan bahwa tingkat\r\naccuracy dari model hybrid CNN-BiLSTM yang telah dibangun mampu\r\nmengklasifikasikan rating dari ulasan pengguna aplikasi Halodoc dengan\r\nkeakuratan mencapai 98,71% saat COVID-19 dan 98,16% setelah COVID-19.\r\n\r\nKata Kunci: Hybrid CNN-BiLSTM, Deep Learning, Text Mining, Halodoc.Technological developments have resulted in technological advances that have led\r\nto various types of services that facilitate access to service facilities, including\r\nhealth services, and encourage the development of various types of applications.\r\nOne of the health service provider applications chosen by the community is the\r\nHalodoc application. Reviews and ratings can be information for other users\r\nregarding the quality of service from the Halodoc application. Therefore, an\r\nappropriate way is needed to analyze customer satisfaction with Halodoc\r\napplication services. Deep learning methods that can be used to classify text data\r\nare Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM),\r\nand Bidirectional Long Short-Term Memory (BiLSTM). This research applies data\r\nresampling using random oversampling, word embedding using Word2Vec, and\r\nhyperparameter tuning using Grid Search. After testing the hyperparameter tuning,\r\nthe best learning rate and batch size value are obtained, namely batch size with a\r\nvalue of 64 and learning rate with a value of 1 × 10−3\r\n\r\n. In general, the architecture\r\nof the CNN-BiLSTM hybrid model built in this study is the embedding layer,\r\nconvolutional layer, pooling layer, BiLSTM layer, flatten, fully connected layer,\r\n\r\nand output layer. The calculation results show that the accuracy level of the CNN-\r\nBiLSTM hybrid model that has been built is able to classify ratings from Halodoc\r\n\r\napplication user reviews with accuracy reaching 98.71% during COVID-19 and\r\n98.16% after COVID-19.\r\n\r\nKeywords: Hybrid CNN-BiLSTM, Deep Learning, Text Mining, Halodoc.\r\n"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/eprint/85424> <http://purl.org/dc/terms/date> "2024-05-14" .
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