Sentiment Analysis on User Reviews of the Edlink Application Using the Random Forest Classifier Method

Sebastianus Adi Santoso Mola, Dian Putri Novita Polly, Nelcy D. Rumlaklak

Abstract


Edlink is a learning platform developed by PT. Sentra Vidya Utama (SEVIMA), established in 2004. Although it offers useful features, some aspects need improvement based on user reviews on Google Play Store. This study aims to accurately classify user sentiment to identify areas that need enhancement. The main challenges include language diversity, sentiment class imbalance, and the need for a reliable classification method. The random forest classifier method was chosen for its ability to handle overfitting and optimize performance. The dataset consists of 1,117 reviews divided into three classes: 385 negative, 118 neutral, and 614 positive. Data was collected through web scraping and processed using cleaning, normalization, tokenizing, stemming, negation conversion, and stopword removal, then weighted using TF-IDF. Testing results showed an accuracy of 86% using 5-Fold cross-validation and SMOTE. The 10-Fold cross-validation test demonstrated that this method outperforms other classification methods with 90% accuracy.

Keywords


Edlink Application Sentiment; Analysis Random; Forest Classifier

Full Text:

PDF

References


Erna, “Apa itu SEVIMA? Profil dan Sejarah Edtech yang Sudah 2 Dekade Merevolusi Pendidikan Tinggi Indonesia | SEVIMA.” Accessed: Nov. 21, 2023. [Online]. Available: https://sevima.com/apa-itu-sevima-profil-dan-sejarah-edtech-yang-sudah-2-dekade-merevolusi-pendidikan-tinggi-indonesia/

G. Rininda, I. H. Santi, and S. Kirom, “PENERAPAN SVM DALAM ANALISIS SENTIMEN PADA EDLINK MENGGUNAKAN PENGUJIAN CONFUSION MATRIX,” JATI J. Mhs. Tek. Inform., vol. 7, no. 5, pp. 3335–3342, 2023.

S. Kumar, P. P. Roy, D. P. Dogra, and B.-G. Kim, “A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications,” Nov. 19, 2023, arXiv: arXiv:2311.11250. doi: 10.48550/arXiv.2311.11250.

R. Maulana, A. Voutama, and T. Ridwan, “Analisis Sentimen Ulasan Aplikasi MyPertamina pada Google Play Store menggunakan Algoritma NBC,” J. Teknol. Terpadu, vol. 9, no. 1, pp. 42–48, 2023.

F. A. Larasati, D. E. Ratnawati, and B. T. Hanggara, “Analisis Sentimen Ulasan Aplikasi Dana dengan Metode Random Forest,” 2022.

M. Y. Aldean, P. Paradise, and N. A. Setya Nugraha, “Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 di Twitter Menggunakan Metode Random Forest Classifier (Studi Kasus: Vaksin Sinovac),” J. Inform. Inf. Syst. Softw. Eng. Appl. INISTA, vol. 4, no. 2, Art. no. 2, Jun. 2022, doi: 10.20895/inista.v4i2.575.

H. Yuliana et al., “Hyperparameter Optimization of Random Forest Algorithm to Enhance Performance Metric Evaluation of 5G Coverage Prediction,” Bul. Pos Dan Telekomun., vol. 22, no. 1, Art. no. 1, Jun. 2024, doi: 10.17933/bpostel.v22i1.390.

B. Z. Ramadhan, R. I. Adam, and I. Maulana, “Analisis Sentimen Ulasan pada Aplikasi E-Commerce dengan Menggunakan Algoritma Naïve Bayes,” J. Appl. Inform. Comput., vol. 6, no. 2, Art. no. 2, Dec. 2022, doi: 10.30871/jaic.v6i2.4725.

A. Widianto, E. Pebriyanto, F. Fitriyanti, and M. Marna, “Document Similarity using Term Frequency-Inverse Document Frequency Representation and Cosine Similarity,” J. Dinda Data Sci. Inf. Technol. Data Anal., vol. 4, no. 2, pp. 149–153, 2024.

A. Kautsar and M. Syafrullah, “Implementasi Algoritme Multinomial Naïve Bayes Pada Analisis Sentimen Terhadap Isu Presiden 3 Periode,” in Prosiding Seminar Nasional Mahasiswa Fakultas Teknologi Informasi (SENAFTI), 2022, pp. 675–682. Accessed: Mar. 01, 2024. [Online]. Available: http://senafti.budiluhur.ac.id/index.php/senafti/article/view/77

J. H. Joloudari, A. Marefat, M. A. Nematollahi, S. S. Oyelere, and S. Hussain, “Effective class-imbalance learning based on SMOTE and convolutional neural networks,” Appl. Sci., vol. 13, no. 6, p. 4006, 2023.

M. Story and R. G. Congalton, “Accuracy assessment: a user’s perspective,” Photogramm. Eng. Remote Sens., vol. 52, no. 3, Art. no. 3, 1986.

L. Breiman, “Random forests,” Mach. Learn., vol. 45, pp. 5–32, 2001.

O. Chamorro-Atalaya et al., “K-Fold Cross-Validation through Identification of the Opinion Classification Algorithm for the Satisfaction of University Students.,” Int. J. Online Biomed. Eng., vol. 19, no. 11, 2023, Accessed: Feb. 22, 2025. [Online]. Available: https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=26268493&AN=169957647&h=mvLfH5TxZM4cCGt5xauQqplvWeca%2FazN9k4Mk2nWIuexjJy3PJFYQ8vzsBJhzVcDuXIkxZQxo5nmgzpIth8Tjw%3D%3D&crl=c

S. Sathyanarayanan and B. R. Tantri, “Confusion matrix-based performance evaluation metrics,” Afr. J. Biomed. Res., pp. 4023–4031, 2024.

R. Aryanti, T. Misriati, and A. Sagiyanto, “Analisis Sentimen Aplikasi Primaku Menggunakan Algoritma Random Forest dan SMOTE untuk Mengatasi Ketidakseimbangan Data,” J. Comput. Syst. Inform. JoSYC, vol. 5, no. 1, pp. 218–227, 2023.




DOI: http://dx.doi.org/10.38101/sisfotek.v15i1.15788

Refbacks

  • There are currently no refbacks.


 

JURNAL SISFOTEK GLOBAL

Organized by: Research Center and Community Development
Published by: Institut Teknologi dan Bisnis Bina Sarana Global
Jl. Aria Santika No.43A, Margasari, Kec. Karawaci, Kota Tangerang, Banten 15114
Phone. +62 552 2727
Email: lppm@global.ac.id

INDEXED BY:

   


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License..
Based on a work at https://journal.global.ac.id/index.php/sisfotek/index.