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All Journal Jurnal Masyarakat Informatika JTIM : Jurnal Teknologi Informasi dan Multimedia Idealis : Indonesia Journal Information System JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Tantyoko, Henri
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Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : JUSTIN (Jurnal Sistem dan Teknologi Informasi)

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Comparison of the Word2vec Skipgram Model Method Linkaja Application Review using Bidirectional LSTM Algorithm and Support Vector Machine Ayuningtyas, Puji; Tantyoko, Henri
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 1 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i1.72530

Abstract

Word embedding is a phase in word processing that seeks to convert each word into a vector representation. Word2Vec is a sort of word embedding that is frequently utilized in natural language processing research. Choosing the proper algorithm can help increase the performance of the word embedding method while doing text data categorization tasks. This research uses the Bidirectional LSTM deep learning algorithm and the Support Vector Machine (SVM) machine learning algorithm. The crawling approach was used to obtain data by accessing the LinkAja Application ID on the Google Play Store. The total number of rows in the dataset was 35560. Labeling data involves categorizing it into two target classes: positive (score 1) and negative (score 0). This study employs the Word2Vec approach with skipgram architecture during the vectorization stage. Vector size, window, min count, and sg are the four parameters employed. The bidirectional LSTM architecture employs a sequential model that consists of three neural network layers: embedding, bidirectional, and dense. In the meanwhile, the SVM architecture employs the Radial Basis Function (RBF) kernel parameters. For the final stage of algorithm testing, the accuracy of the bidirectional LSTM (BiLSTM) algorithm was 0.9505, which means it was higher than the support vector machine (SVM) algorithm with an accuracy value of 0.93.
Co-Authors Andreas Rony Wijaya Aris Sugiharto Ayuningtyas, Puji Baihaqi, Muhamad Nur Dian Kartika Sari, Dian Kartika Dwi Putro, Aditya Latief, Muhammad Abdul Ma'rufatullah, Saihun Miftakhurrahman, Wildan Nabila, Luthfi Rakan Purnama, Satriawan Rasyid Vianita, Etna