Comparison of RNN and LSTM Algorithms Based on Fasttext Embeddings in Sentiment Analysis on the Merdeka Mengajar Platform

Authors

  • Anjis Sapto Nugroho Department of Information Technology, Faculty of Information and Industrial Technology, Universitas Stikubank, Semarang, Indonesia
  • Kristiawan Nugroho Department of Information Technology, Faculty of Information and Industrial Technology, Universitas Stikubank, Semarang, Indonesia

DOI:

10.33395/sinkron.v9i1.14296

Keywords:

Sentiment analysis, Merdeka Mengajar, RNN, LSTM, accuracy

Abstract

As of 2024, the Merdeka Mengajar Platform has been used by more than 3.5 million teachers across Indonesia. This number represents an increase of more than 3.85% compared to the previous academic year, which was 3.37 million. However, the utilization of this application has not yet reached the expected target number of users, so an analysis is needed to identify the factors causing this. This research uses Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to perform sentiment analysis on reviews of the Merdeka Mengajar platform. RNN and LSTM are chosen for their advantages in handling sequential data, particularly in text processing for sentiment analysis. This research aims to address the challenges in understanding the positive or negative sentiments of users on the platform. The research methodology includes important stages such as data cleaning, preprocessing, and transforming text into numerical vectors using FastText embedding. Next, RNN and LSTM models are applied to predict sentiment based on patterns in the text data. The research results show that the LSTM model is capable of capturing long-term relationships in sequential data with an expected accuracy of 93.58%. Meanwhile, the RNN model yields a lower accuracy of 91.70%. The LSTM model is more effective in classifying sentiment with high accuracy, especially in text data with complex temporal contexts. This research contributes to understanding user perceptions and feedback regarding the Merdeka Mengajar platform, which is expected to provide insights for platform developers to enhance service quality.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Al-jumaili, A. S. A. (2024). NAMED ENTITY RECOGNITION. 25, 5258–5264. https://doi.org/10.12694/scpe.v25i6.3365

Amal, M. A. A., Zulherman, D., & Widadi, R. (2023). Klasifikasi Sinyal Phonocardiogram Menggunakan Short Time Fourier Transform dan Convolutional Neural Network. Jurnal Teknologi Informasi Dan Ilmu Komputer, 10(2), 237–244. https://doi.org/10.25126/jtiik.20231015424

Amrustian, M. A., Widayat, W., & Wirawan, A. M. (2022). Analisis Sentimen Evaluasi Terhadap Pengajaran Dosen di Perguruan Tinggi Menggunakan Metode LSTM. Jurnal Media Informatika Budidarma, 6(1), 535. https://doi.org/10.30865/mib.v6i1.3527

Ariyus, D., & Manongga, D. (2024). Enhancing Sentiment Analysis of Indonesian Tourism Video Content Commentary on TikTok : A FastText and Bi-LSTM Approach. 14(6), 18020–18028.

Atikah, L., Purwitasari, D., & Suciati, N. (2022). DETEKSI KEJADIAN LALU LINTAS PADA TEKS TWITTER DENGAN PENDEKATAN KLASIFIKASI MULTI-LABEL BERBASIS DEEP LEARNING MULTI-LABEL CLASSIFICATION USING DEEP LEARNING APPROACH ON TWITTER. 9(1), 87–96. https://doi.org/10.25126/jtiik.202295206

Azizah, R. A., Bachtiar, F., & Adinugroho, S. (2022). Klasifikasi Kinerja Akademik Siswa Menggunakan Neighbor Weighted K-Nearest Neighbor dengan Seleksi Fitur Information Gain. Jurnal Teknologi Informasi Dan Ilmu Komputer, 9(3), 605–614. https://doi.org/10.25126/jtiik.2022935751

Dang, N. C., Moreno-García, M. N., & De la Prieta, F. (2022). Sentiment Analysis Based on Deep Learning in E-Commerce. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13369 LNAI, 498–507. https://doi.org/10.1007/978-3-031-10986-7_40

Darojah, Z., Susetyoko, R., & Ramadijanti, N. (2023). Strategi Penanganan Imbalance Class Pada Model Klasifikasi Penerima Kartu Indonesia Pintar Kuliah Berbasis Neural Network Menggunakan Kombinasi SMOTE dan ENN. Jurnal Teknologi Informasi Dan Ilmu Komputer, 10(2), 457–466. https://doi.org/10.25126/jtiik.20231026480

Darussalam, & Arief, G. (2021). Jurnal Resti. Resti, 1(1), 19–25.

David, M. S., & Renjith, S. (2021). Comparison of word embeddings in text classification based on RNN and CNN. IOP Conference Series: Materials Science and Engineering, 1187(1), 012029. https://doi.org/10.1088/1757-899x/1187/1/012029

Gomez-Adorno, H., Bel-Enguix, G., Sierra, G., Barajas, J. C., & Álvarez, W. (2024). Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish. Informatics, 11(2). https://doi.org/10.3390/informatics11020024

Isnain, A. R., Sulistiani, H., Hurohman, B. M., Nurkholis, A., & Styawati, S. (2022). Analisis Perbandingan Algoritma LSTM dan Naive Bayes untuk Analisis Sentimen. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 8(2), 299. https://doi.org/10.26418/jp.v8i2.54704

Ketaren, A., Rahman, F., Meliala, H. P., Tarigan, N., & Simanjuntak, R. (2022). Monitoring dan Evaluasi Pemanfaatan Platform Merdeka Mengajar pada Satuan Pendidikan Aswinta. Jurnal Pendidikan Dan Konseling, 4(6), 10340–10343. https://doi.org/https://doi.org/10.31004/jpdk.v4i6.10030

Khan, L., Amjad, A., Afaq, K. M., & Chang, H. T. (2022). Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media. Applied Sciences (Switzerland), 12(5). https://doi.org/10.3390/app12052694

Kurniasari, L., & Setyanto, A. (2020). Sentiment Analysis using Recurrent Neural Network. Journal of Physics: Conference Series, 1471(1). https://doi.org/10.1088/1742-6596/1471/1/012018

Merinda Lestandy, Abdurrahim Abdurrahim, & Lailis Syafa’ah. (2021). Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 802–808. https://doi.org/10.29207/resti.v5i4.3308

Muhuri, P. S., Chatterjee, P., Yuan, X., Roy, K., & Esterline, A. (2020). Using a long short-term memory recurrent neural network (LSTM-RNN) to classify network attacks. Information (Switzerland), 11(5). https://doi.org/10.3390/INFO11050243

Nemes, L., & Kiss, A. (2021). Social media sentiment analysis based on COVID-19. Journal of Information and Telecommunication, 5(1), 1–15. https://doi.org/10.1080/24751839.2020.1790793

Sharma, R., Shrivastava, S., Kumar Singh, S., Kumar, A., Saxena, S., & Kumar Singh, R. (2021). Deep-Abppred: Identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec. Briefings in Bioinformatics, 22(5), 1–19. https://doi.org/10.1093/bib/bbab065

Smagulova, K., & James, A. P. (2019). A survey on LSTM memristive neural network architectures and applications. European Physical Journal: Special Topics, 228(10), 2313–2324. https://doi.org/10.1140/epjst/e2019-900046-x

Suwitono, Y. A., & Kaunang, F. J. (2022). Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Daun Dengan Metode Data Mining SEMMA Menggunakan Keras. Jurnal Komtika (Komputasi Dan Informatika), 6(2), 109–121. https://doi.org/10.31603/komtika.v6i2.8054

Utami, H. (2022). Analisis Sentimen dari Aplikasi Shopee Indonesia Menggunakan Metode Recurrent Neural Network. Indonesian Journal of Applied Statistics, 5(1), 31. https://doi.org/10.13057/ijas.v5i1.56825

Widayat, W. (2021). Analisis Sentimen Movie Review menggunakan Word2Vec dan metode LSTM Deep Learning. Jurnal Media Informatika Budidarma, 5(3), 1018. https://doi.org/10.30865/mib.v5i3.3111

Zuraiyah, T. A., Mulyati, M. M., & Harahap, G. H. F. (2023). Perbandingan Metode Naïve Bayes, Support Vector Machine Dan Recurrent Neural Network Pada Analisis Sentimen Ulasan Produk E-Commerce. Multitek Indonesia, 17(1), 27–43. https://doi.org/10.24269/mtkind.v17i1.7092

Downloads


Crossmark Updates

How to Cite

Nugroho, A. S., & Nugroho, K. (2025). Comparison of RNN and LSTM Algorithms Based on Fasttext Embeddings in Sentiment Analysis on the Merdeka Mengajar Platform. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(1), 117-128. https://doi.org/10.33395/sinkron.v9i1.14296