KLASIFIKASI SENTIMEN ULASAN PENGGUNA APLIKASI DIGITALENT MENGGUNAKAN ALGORITMA NAIVE BAYES BERBASIS TF-IDF

Anggun Awalia; Rudi Kurniawan; Bani Nurhakim; Raditya Danar Dana

Authors

Anggun Awalia STMIK IKMI Cirebon, Indonesia
Rudi Kurniawan STMIK IKMI Cirebon, Indonesia
Bani Nurhakim STMIK IKMI Cirebon, Indonesia
Raditya Danar Dana STMIK IKMI Cirebon, Indonesia

Keywords:

Analisis Sentimen, Ulasan Pengguna, Naive Bayes, TF-IDF, Digitalent

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi otomatis yang dapat mengidentifikasi sentimen pengguna terhadap aplikasi Digitalent. Aplikasi ini merupakan bagian dari upaya transformasi digital nasional di bidang pelatihan kompetensi. Tantangan utama dalam menganalisis ulasan pengguna terletak pada keberagaman gaya bahasa dan tingginya volume data, yang sulit ditangani secara manual. Oleh karena itu, digunakan pendekatan berbasis Machine Learning dengan algoritma Naive Bayes dan representasi fitur TF-IDF. Penelitian ini dimulai dengan pengumpulan ulasan pengguna dari platform resmi, dilanjutkan dengan tahap preprocessing teks seperti tokenisasi, stopword removal, dan stemming. Hasil penelitian menunjukkan bahwa model Naive Bayes berbasis TF-IDF mampu mengklasifikasikan sentimen ke dalam kategori positif, negatif, dan netral dengan performa yang cukup baik berdasarkan metrik evaluasi seperti akurasi, presisi, recall, dan F1-score. Temuan ini menunjukkan bahwa pendekatan yang digunakan cukup efektif untuk memahami persepsi pengguna dan dapat diimplementasikan untuk mendukung pengembangan layanan digital berbasis umpan balik pengguna secara real-time. Implikasi dari penelitian ini tidak hanya meningkatkan efisiensi analisis sentimen, tetapi juga memperkuat literatur tentang pengolahan bahasa alami dalam bahasa Indonesia serta pemanfaatannya dalam pengembangan aplikasi digital di sektor publik.

References

L. Zhou, “Research on Quantitative Model of Brand Recognition Based on Sentiment Analysis of Big Data,” Front. Psychol., vol. 13, 2022, doi: 10.3389/fpsyg.2022.915443.

M. Zhang, V. Palade, Y. Wang, and Z. Ji, “Attention-Based Word Embeddings Using Artificial Bee Colony Algorithm for Aspect-Level Sentiment Classification,” Inf. Sci. (N Y)., vol. 545, pp. 713–738, 2021, doi: 10.1016/j.ins.2020.09.038.

T. Vaiyapuri et al., “Sustainable Artificial Intelligence-Based Twitter Sentiment Analysis on COVID-19 Pandemic,” Sustainability, vol. 15, no. 8, p. 6404, 2023, doi: 10.3390/su15086404.

L. Alzubaidi et al., “Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions,” J. Big Data, vol. 8, no. 1, 2021, doi: 10.1186/s40537-021-00444-8.

G. A. Buntoro, R. Arifin, G. N. Syaifuddiin, A. Selamat, O. Krejcar, and H. Fujita, “The Implementation of the Machine Learning Algorithm for the Sentiment Analysis of Indonesia’s 2019 Presidential Election,” Iium Engineering Journal, vol. 22, no. 1, pp. 78–92, 2021, doi: 10.31436/iiumej.v22i1.1532.

P. Theerthagiri and J. Vidya, “Cardiovascular Disease Prediction Using Recursive Feature Elimination and Gradient Boosting Classification Techniques,” Expert Syst., vol. 39, no. 9, 2022, doi: 10.1111/exsy.13064.

J. Zhang, D. Li, H. Lan, L. Tang, and M. Guo, “Research on Hotel Reservation Scheme Based on Random Forest Model Prediction,” Advances in Computer and Communication, vol. 4, no. 6, pp. 358–362, 2024, doi: 10.26855/acc.2023.12.003.

M. S. Hossain and M. F. Rahman, “Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches,” Fiib Business Review, vol. 12, no. 4, pp. 386–402, 2022, doi: 10.1177/23197145221115793.

K. Chan and S. Im, “Sentiment Analysis by Using Naïve‐Bayes Classifier With Stacked CARU,” Electron. Lett., vol. 58, no. 10, pp. 411–413, 2022, doi: 10.1049/ell2.12478.

D. Alghazzawi, A. G. A. Alquraishee, S. Badri, and S. H. Hasan, “ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review,” Sustainability, vol. 15, no. 9, p. 7076, 2023, doi: 10.3390/su15097076.

R. Hakiki, A. Pambudi, and Asriyanik, “Classification of Public Sentiment Toward 2024 Presidential Candidates on Social Media Platform X Using Naïve Bayes Algorithm,” J. Of Artif. Intell. And Eng. Appl., vol. 3, no. 2, pp. 551–556, 2024, doi: 10.59934/jaiea.v3i2.422.

Y. S. Mehanna and M. Mahmuddin, “The Effect of Pre-Processing Techniques on the Accuracy of Sentiment Analysis Using Bag-of-Concepts Text Representation,” SN Comput. Sci., vol. 2, no. 4, 2021, doi: 10.1007/s42979-021-00453-7.

J. A. Wahid et al., “Topic2features: A Novel Framework to Classify Noisy and Sparse Textual Data Using LDA Topic Distributions,” PeerJ Comput. Sci., vol. 7, p. e677, 2021, doi: 10.7717/peerj-cs.677.

P. A. Aritonang, M. E. Johan, and I. Prasetiawan, “Aspect-Based Sentiment Analysis on Application Review Using Convolutional Neural Network,” Ultima Infosys Jurnal Ilmu Sistem Informasi, vol. 13, no. 1, pp. 54–61, 2022, doi: 10.31937/si.v13i1.2684.

Y. Hou and J. Huang, “Natural Language Processing for Social Science Research: A Comprehensive Review,” Chin. J. Sociol., vol. 11, no. 1, pp. 121–157, 2025, doi: 10.1177/2057150x241306780.

M.-S. Sung and Y. Lee, “Examining How Preschool Teachers’ Positive Psychological Capital Impacts Digital Education Innovation: A Moderated Moderation Analysis of Effort Expectancy and Behavioral Intention,” Behavioral Sciences, vol. 15, no. 7, p. 952, 2025, doi: 10.3390/bs15070952.

M. F. Fakhrezi, A. F. Rochim, and D. M. K. Nugraheni, “Comparison of Sentiment Analysis Methods Based on Accuracy Value Case Study: Twitter Mentions of Academic Article,” Jurnal Resti (Rekayasa Sistem Dan Teknologi Informasi), vol. 7, no. 1, pp. 161–167, 2023, doi: 10.29207/resti.v7i1.4767.

B. Cardone, F. D. Martino, and S. Senatore, “Improving the Emotion‐based Classification by Exploiting the Fuzzy Entropy in FCM Clustering,” International Journal of Intelligent Systems, vol. 36, no. 11, pp. 6944–6967, 2021, doi: 10.1002/int.22575.

A. Haber and Z. Waks, “Classification and Geotemporal Analysis of Quality-of-Life Issues in Tenant Reviews,” 2021, doi: 10.18653/v1/2021.findings-emnlp.217.

C. Raj, A. Agarwal, G. Bharathy, B. Narayan, and M. Prasad, “Cyberbullying Detection: Hybrid Models Based on Machine Learning and Natural Language Processing Techniques,” Electronics (Basel)., vol. 10, no. 22, p. 2810, 2021, doi: 10.3390/electronics10222810.

L. Zhang, “Features Extraction Based on Naive Bayes Algorithm and TF-IDF for News Classification,” PLoS One, vol. 20, no. 7, p. e0327347, 2025, doi: 10.1371/journal.pone.0327347.

S. Agarwal, M. Varun, and S. Prabakeran, “Interactive Web App for Fake News Detection,” Itm Web of Conferences, vol. 53, p. 03003, 2023, doi: 10.1051/itmconf/20235303003.

R. Elnadree, A. B. El-Sisi, and W. Atwa, “Performance Investigation of Features Extraction and Classification Approaches for Sentiment Analysis Systems,” Ijci International Journal of Computers and Information, vol. 0, no. 0, pp. 0–0, 2021, doi: 10.21608/ijci.2021.65578.1044.

Q. Xie, J. Huang, P. Du, M. Peng, and J. Nie, “Inductive Topic Variational Graph Auto-Encoder for Text Classification,” 2021, doi: 10.18653/v1/2021.naacl-main.333.

F. Anzum and M. L. Gavrilova, “Emotion Detection From Micro-Blogs Using Novel Input Representation,” Ieee Access, vol. 11, pp. 19512–19522, 2023, doi: 10.1109/access.2023.3248506.

H. Zhao, J. Xie, and H. Wang, “Graph Convolutional Network Based on Multi-Head Pooling for Short Text Classification,” Ieee Access, vol. 10, pp. 11947–11956, 2022, doi: 10.1109/access.2022.3146303.

A. Fan, S. Wang, and Y. Wang, “Legal Document Similarity Matching Based on Ensemble Learning,” Ieee Access, vol. 12, pp. 33910–33922, 2024, doi: 10.1109/access.2024.3371262.

A. R. W. Rapsanjani and E. Junianto, “Implementasi Probabilistic Neural Network Dan Word Embedding Untuk Analisis Sentimen Vaksin Sinovac,”