Implementasi Algoritma Deep Learning untuk Analisis Sentimen Pengguna Platform Pendidikan
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Abstract
More than 3.5 million Indonesian educators had used the Merdeka Mengajar Platform by 2024. Comparing this figure to the 3.37 million from the previous academic year, there has been an increase of almost 3.85%. However, an investigation is required to determine the reasons why the application's use has not yet achieved the anticipated goal number of users. This study does sentiment analysis on evaluations of the Merdeka Mengajar platform using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). The benefits of RNN and LSTM in processing sequential data, especially in text processing for sentiment analysis, led to their selection. The purpose of this study is to solve the difficulties in determining if users' sentiments on the platform are favorable or negative. Important steps in the research technique include preprocessing, data cleaning, and employing FastText embedding to convert text into numerical vectors. Then, using patterns in the text data, RNN and LSTM models are used to forecast sentiment. The study's findings demonstrate that the LSTM model can, with an estimated accuracy of 93.58%, identify long-term associations in sequential data. The RNN model, on the other hand, produces a lesser accuracy of 91.70%. Particularly in text data with intricate temporal circumstances, the LSTM model performs better at accurately classifying sentiment. By better understanding customer opinions and input on the Merdeka Mengajar platform, this study helps platform developers improve the quality of their services.
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Nugroho, A., Prasetyo, E., Priyanto, A., & Puryono, D. (1). Implementasi Algoritma Deep Learning untuk Analisis Sentimen Pengguna Platform Pendidikan. SOSCIED, 8(2), 482-496. https://doi.org/10.32531/jsoscied.v8i2.999
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
LPPM Politeknik Katolik Saint Paul Sorong
References
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A. R. Isnain, H. Sulistiani, B. M. Hurohman, A. Nurkholis, and S. Styawati, ‘Analisis Perbandingan Algoritma LSTM dan Naive Bayes untuk Analisis Sentimen’, J. Edukasi dan Penelit. Inform., vol. 8, no. 2, p. 299, 2022, doi: 10.26418/jp.v8i2.54704.
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Nadia Ristya Dewi, E. Yulia Puspaningrum, and H. Maulana, ‘Analisis Sentimen Tweet Vaksinasi Covid-19 Menggunakan RNN Dengan Metode TF-IDF Dan Word2Vec’, J. Inform. dan Sist. Inf., vol. 3, no. 1, pp. 56–65, 2022, doi: 10.33005/jifosi.v3i1.449.
D. Ariyus and D. Manongga, ‘Enhancing Sentiment Analysis of Indonesian Tourism Video Content Commentary on TikTok?: A FastText and Bi-LSTM Approach’, vol. 14, no. 6, pp. 18020–18028, 2024.
Darussalam and G. Arief, ‘Jurnal Resti’, Resti, vol. 1, no. 1, pp. 19–25, 2021.
K. P. C. Kalaiarasan, ‘“Web Services Performance Prediction with Confusion Matrix and K-Fold Cross Validation to Provide Prior Service Quality Characteristics”’, J. Electr. Syst., vol. 20, no. 2s, pp. 284–292, 2024, doi: 10.52783/jes.1139.
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R. A. Azizah, F. Bachtiar, and S. Adinugroho, ‘Klasifikasi Kinerja Akademik Siswa Menggunakan Neighbor Weighted K-Nearest Neighbor dengan Seleksi Fitur Information Gain’, J. Teknol. Inf. dan Ilmu Komput., vol. 9, no. 3, pp. 605–614, 2022, doi: 10.25126/jtiik.2022935751.
R. Sharma, S. Shrivastava, S. Kumar Singh, A. Kumar, S. Saxena, and R. Kumar Singh, ‘Deep-Abppred: Identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec’, Brief. Bioinform., vol. 22, no. 5, pp. 1–19, 2021, doi: 10.1093/bib/bbab065.
Y. A. Suwitono and F. J. Kaunang, ‘Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Daun Dengan Metode Data Mining SEMMA Menggunakan Keras’, J. Komtika (Komputasi dan Inform., vol. 6, no. 2, pp. 109–121, 2022, doi: 10.31603/komtika.v6i2.8054.
Z. Darojah, R. Susetyoko, and N. Ramadijanti, ‘Strategi Penanganan Imbalance Class Pada Model Klasifikasi Penerima Kartu Indonesia Pintar Kuliah Berbasis Neural Network Menggunakan Kombinasi SMOTE dan ENN’, J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 2, pp. 457–466, 2023, doi: 10.25126/jtiik.20231026480.
M. A. Amrustian, W. Widayat, and A. M. Wirawan, ‘Analisis Sentimen Evaluasi Terhadap Pengajaran Dosen di Perguruan Tinggi Menggunakan Metode LSTM’, J. Media Inform. Budidarma, vol. 6, no. 1, p. 535, 2022, doi: 10.30865/mib.v6i1.3527.