SLR Analisis Sentimen E-Commerce Indonesia Menggunakan LSTM Dan Penanganan Imbalanced Data
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Abstract
Online marketplaces in Indonesia generate massive unstructured consumer reviews laden with slang and informal language, posing challenges for standard sentiment analysis systems. This Systematic Literature Review (SLR), following PRISMA 2020 guidelines, synthesizes 32 primary studies from Google Scholar, Scopus, and IEEE Xplore (2014–2025) to address four analytical research questions on slang normalization effectiveness, LSTM performance relative to dataset characteristics, trade-offs of imbalanced data handling methods, and contexts where F1-Score outperforms accuracy. Results show dictionary-based normalization achieves 87.3% accuracy versus 59% for automated methods. LSTM consistently outperforms traditional models on large, long-text datasets but remains sensitive to preprocessing quality. SMOTE is the most adopted technique yet underperforms at imbalance ratios exceeding 10:1. F1-Score is confirmed as the most representative metric for skewed datasets. This study proposes an integrated conceptual framework and identifies future research gaps including sarcasm detection, code-mixing, and Transformer-based approaches.
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References
F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, “IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP,” COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference, pp. 757–770, 2020, doi: 10.18653/V1/2020.COLING-MAIN.66.
A. N. A. Saputra, R. E. Saputro, and D. I. S. Saputra, “Enhancing Sentiment Analysis Accuracy Using SVM and Slang Word Normalization on YouTube Comments,” Sinkron, vol. 9, pp. 687–699, Apr. 2025, doi: 10.33395/sinkron.v9i2.14613.
Y. Hu, S. Zhang, V. Sathy, A. T. Panter, and M. Bansal, “SETSUM: Summarization and Visualization of Student Evaluations of Teaching,” in NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Demonstrations Session, Association for Computational Linguistics (ACL), 2022, pp. 71–89. doi: 10.18653/v1/2022.naacl-demo.9.
K. L. Tan, C. P. Lee, K. S. M. Anbananthen, and K. M. Lim, “RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network,” IEEE Access, vol. 10, pp. 21517–21525, 2022, doi: 10.1109/ACCESS.2022.3152828.
N. Nurzaenab et al., “EVALUASI PERFORMA METODE LONG SHORT TERM MEMORY (LSTM) DAN RECURRENT NEURAL NETWORK (RNN) PADA ANALISIS SENTIMEN KOMENTAR PENGGUNA APLIKASI KITALULUS,” JTRISTE, vol. 12, pp. 86–92, Sep. 2025, doi: 10.55645/jtriste.v12i1.614.
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, pp. 1735–1780, Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
B. Wilie et al., “IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding,” in Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, AACL-IJCNLP 2020, Association for Computational Linguistics (ACL), 2020, pp. 843–857. doi: 10.18653/v1/2020.aacl-main.85.
S. Aulia Natasya, N. Muzahra, A. Baihaqi, R. Lesmana, and Nurbaiti Nurbaiti, “Persepsi Konsumen terhadap Consumer Review pada Platform E-Commerce Shopee,” Jurnal Nuansa : Publikasi Ilmu Manajemen dan Ekonomi Syariah, vol. 3, pp. 196–207, Dec. 2025, doi: 10.61132/nuansa.v3i4.2406.
A. Ahmad, Aswadi Ramli, and H. Hajerah, “Penggunaan Bahasa Gaul di Kalangan Remaja terhadap Kelestarian Bahasa Indonesia di Era Digital,” Jurnal Onoma: Pendidikan, Bahasa, dan Sastra, vol. 11, pp. 980–990, Feb. 2025, doi: 10.30605/onoma.v11i1.5018.
Pande sindu, Agus Aan Jiwa Permana, and I Nyoman Saputra Wahyu Wijaya, “IDENTIFIKASI DAN NORMALISASI TEKS SLANG DENGAN FASTTEXT PADA TWITTER DALAM BAHASA INDONESIA,” Jurnal Pendidikan Teknologi dan Kejuruan, vol. 21, pp. 33–44, Jan. 2024, doi: 10.23887/jptkundiksha.v21i1.66381.
D. A. Kristiyanti, S. A. Sanjaya, V. C. Tjokro, and J. Suhali, “Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia,” IAES International Journal of Artificial Intelligence, vol. 13, pp. 2058–2070, Jun. 2024, doi: 10.11591/ijai.v13.i2.pp2060-2072.
S. Riyanto, I. S. Sitanggang, T. Djatna, and T. D. Atikah, “Comparative Analysis using Various Performance Metrics in Imbalanced Data for Multi-class Text Classification,” International Journal of Advanced Computer Science and Applications, vol. 14, pp. 1082–1090, 2023, doi: 10.14569/IJACSA.2023.01406116.
M. Idris, A. Rifai, and K. D. Tania, “Sentiment Analysis of Tokopedia App Reviews using Machine Learning and Word Embeddings,” sinkron, vol. 9, pp. 210–219, Jan. 2025, doi: 10.33395/sinkron.v9i1.14278.
F. M. Bianchi, E. Maiorino, M. C. Kampffmeyer, A. Rizzi, and R. Jenssen, “An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting,” Jul. 2018, doi: 10.1007/978-3-319-70338-1.
I. Muis, “Pemetaan Literatur tentang Layanan Prima di Perguruan Tinggi: Analisis Tematik Berbasis PRISMA,” Jurnal Siber Multi Disiplin, vol. 3, pp. 12–24, May 2025, doi: 10.38035/jsmd.v3i1.418.
D. Ariffadillah, R. R. P. C. -, S. M. R. -, R. L. Pratiwi, and R. I. Handayani, “KOMPARASI METODE SVM DAN BILSTM PADA KLASIFIKASI SENTIMEN APLIKASI PLAY STORE DENGAN TEKNIK HYBRID IMBALANCE HANDLING,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 14, Jan. 2026, doi: 10.23960/jitet.v14i1.8313.
M. J. Page et al., “PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews,” Mar. 2021, BMJ Publishing Group. doi: 10.1136/bmj.n160.
B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, “Systematic literature reviews in software engineering - A systematic literature review,” Jan. 2009. doi: 10.1016/j.infsof.2008.09.009.
D. Moher et al., “Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement,” Jul. 2009, Public Library of Science. doi: 10.1371/journal.pmed.1000097.
J. Cohen, “A Coefficient of Agreement for Nominal Scales,” Educ. Psychol. Meas., vol. 20, pp. 37–46, 1960, doi: 10.1177/001316446002000104.
B. Hakim, “Analisa Sentimen Data Text Preprocessing Pada Data Mining Dengan Menggunakan Machine Learning,” JBASE - Journal of Business and Audit Information Systems, vol. 4, no. 2, Aug. 2021, doi: 10.30813/jbase.v4i2.3000.
M. I. Raif, N. N. Hidayati, and T. Matulatan, “Otomatisasi Pendeteksi Kata Baku Dan Tidak Baku Pada Data Twitter Berbasis KBBI,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, pp. 337–348, Apr. 2024, doi: 10.25126/jtiik.20241127404.
B. A. Haryono and A. Prasetya, “Identifikasi Kuantitas Berbasis Rule Pada Masalah Text-to-SQL,” Jurnal Borneo Informatika dan Teknik Komputer, vol. 5, pp. 26–39, Sep. 2025, doi: 10.35334/jbit.v5i1.6819.
M. I. Raif, N. N. Hidayati, and T. Matulatan, “Otomatisasi Pendeteksi Kata Baku Dan Tidak Baku Pada Data Twitter Berbasis KBBI,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, pp. 337–348, Apr. 2024, doi: 10.25126/jtiik.20241127404.
M. T. Ramadhan, C. Sobarna, and A. S. Afsari, “Code Mixing of Slang and Sundanese on TikTok,” SUAR BETANG, vol. 18, no. 2, pp. 265–276, Dec. 2023, doi: 10.26499/surbet.v18i2.13751.
M. F. R. Khairul and R. S. Perdana, “Arsitektur Sistem Percakapan Otomatis Berbahasa Indonesia dengan Normalisasi Bahasa Informal Menjadi Baku,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, pp. 1009–1016, Oct. 2024, doi: 10.25126/jtiik.2024117984.
Asnawiyah and R. Eka Putra, “Perbandingan Algoritma LSTM dan BiLSTM Untuk Analisis Sentimen Multi-Class Media Sosial Twitter,” Journal of Informatics and Computer Science (JINACS), vol. 6, pp. 778–786, Jan. 2025, doi: 10.26740/jinacs.v6n03.p778-786.
H. Setiawan and D. Ariatmanto, “Analisis Perbandingan Algoritma Machine Learning Dan Deep Learning Untuk Sentimen Analisis Teks Umpan Balik Tentang Evaluasi Pengajaran Dosen,” JSAI (Journal Scientific and Applied Informatics), vol. 7, pp. 379–385, Jun. 2024, doi: 10.36085/jsai.v7i2.6572.
A. R. Isnain, H. Sulistiani, B. M. Hurohman, A. Nurkholis, and S. Styawati, “Analisis Perbandingan Algoritma LSTM dan Naive Bayes untuk Analisis Sentimen,” Jurnal Edukasi dan Penelitian Informatika (JEPIN), vol. 8, p. 299, Aug. 2022, doi: 10.26418/jp.v8i2.54704.
Yuyun, A. D. Latief, T. Sampurno, Hazriani, A. O. Arisha, and Mushaf, “Next Sentence Prediction: The Impact of Preprocessing Techniques in Deep Learning,” in Proceedings - 2023 10th International Conference on Computer, Control, Informatics and its Applications: Exploring the Power of Data: Leveraging Information to Drive Digital Innovation, IC3INA 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 274–278. doi: 10.1109/IC3INA60834.2023.10285805.
D. Purnamasari, A. B. Aji, S. Madenda, I. M. Wiryana, and S. Harmanto, “SENTIMENT ANALYSIS METHODS FOR CUSTOMER REVIEW OF INDONESIA E-COMMERCE,” International Journal of Innovative Computing, Information and Control, vol. 20, no. 1, pp. 47–60, Feb. 2024, doi: 10.24507/ijicic.20.01.47.
A. Romadhony, S. Al Faraby, R. Rismala, U. N. Wisesti, and A. Arifianto, “Sentiment Analysis on a Large Indonesian Product Review Dataset,” Journal of Information Systems Engineering and Business Intelligence, vol. 10, pp. 167–178, 2024, doi: 10.20473/jisebi.10.1.167-178.
N. G. Ramadhan, “Comparative Analysis of ADASYN-SVM and SMOTE-SVM Methods on the Detection of Type 2 Diabetes Mellitus,” Scientific Journal of Informatics, vol. 8, pp. 276–282, Nov. 2021, doi: 10.15294/sji.v8i2.32484.
T. I. Z. M. Putra, S. Suprapto, and A. F. Bukhori, “Model Klasifikasi Berbasis Multiclass Classification dengan Kombinasi Indobert Embedding dan Long Short-Term Memory untuk Tweet Berbahasa Indonesia,” Jurnal Ilmu Siber dan Teknologi Digital, vol. 1, pp. 1–28, Nov. 2022, doi: 10.35912/jisted.v1i1.1509.
M. I. Alfarizi, L. Syafaah, and M. Lestandy, “Emotional Text Classification Using TF-IDF (Term Frequency-Inverse Document Frequency) And LSTM (Long Short-Term Memory),” JUITA : Jurnal Informatika, vol. 10, p. 225, Nov. 2022, doi: 10.30595/juita.v10i2.13262.
H. Hairani, T. Widiyaningtyas, and D. D. Prasetya, “Addressing Class Imbalance of Health Data: a Systematic Literature Review on Modified Synthetic Minority Oversampling Technique (SMOTE) Strategies,” International Journal on Informatics Visualization, vol. 8, pp. 1310–1318, 2024, doi: 10.62527/joiv.8.3.2283.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002, doi: 10.1613/jair.953.
H. He, Y. Bai, E. A. Garcia, and S. Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” in Proceedings of the International Joint Conference on Neural Networks, 2008, pp. 1322–1328. doi: 10.1109/IJCNN.2008.4633969.
Cosmas Haryawan and Yosef Muria Kusuma Ardhana, “ANALISA PERBANDINGAN TEKNIK OVERSAMPLING SMOTE PADA IMBALANCED DATA,” Jurnal Informatika dan Rekayasa Elektronik, vol. 6, pp. 73–78, Apr. 2023, doi: 10.36595/jire.v6i1.834.
R. Waluyo and A. S. Munir, “Optimasi Prediksi Kematian pada Gagal Jantung Analisis Perbandingan Algoritma Pembelajaran Ensemble dan Teknik Penyeimbangan Data pada Dataset,” Jurnal Sistem dan Teknologi Informasi (JustIN), vol. 12, p. 365, Apr. 2024, doi: 10.26418/justin.v12i2.75158.
I. G. Ayu Nandia Lestari, D. G. Hendra Divayana, and K. Y. Ernada Aryanto, “A Concentration Selection In Study Programs Using SMOTE Techniques With Ensemble Learning Algorithms,” in 2023 5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ICORIS60118.2023.10352192.
P. Branco, L. Torgo, and R. P. Ribeiro, “A survey of predictive modeling on imbalanced domains,” Jun. 2017, Association for Computing Machinery. doi: 10.1145/2907070.
M. A. Hermawan, A. Faqih, and G. Dwilestari, “IMPLEMENTASI AKURASI MODEL NAIVE BAYES MENGGUNAKAN SMOTE DALAM ANALISIS SENTIMEN PENGGUNA APLIKASI BRIMO,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 13, Jan. 2025, doi: 10.23960/jitet.v13i1.5748.
D. Ariffadillah, R. R. P. C. -, S. M. R. -, R. L. Pratiwi, and R. I. Handayani, “KOMPARASI METODE SVM DAN BILSTM PADA KLASIFIKASI SENTIMEN APLIKASI PLAY STORE DENGAN TEKNIK HYBRID IMBALANCE HANDLING,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 14, Jan. 2026, doi: 10.23960/jitet.v14i1.8313.
J. Heaton, “Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning,” Genet. Program. Evolvable Mach., vol. 19, pp. 305–307, Jun. 2018, doi: 10.1007/s10710-017-9314-z.
C. Sintiya, G. H. Hutagaol, D. Bate`e, and S. Irviantina, “Evaluasi Teknik Resampling untuk Class Balancing dalam Analisis Sentimen Kesehatan Mental Berbasis Bi-LSTM,” Jurnal Sifo Mikroskil, vol. 26, Oct. 2025, doi: 10.55601/jsm.v26i2.1799.
J. P. A. Ioannidis, “Why most published research findings are false,” in Getting to Good: Research Integrity in the Biomedical Sciences, Springer International Publishing, 2018, pp. 2–8. doi: 10.1371/journal.pmed.0020124.
I. Maharani, I. Alifiar, and Y. P. Sukmawan, “Analisis Bibliometrik Terkait Tren Teratogenik,” Pharmacy Genius, vol. 4, pp. 127–134, Oct. 2025, doi: 10.56359/pharmgen.v4i3.912.
I. B. Saputro and T. J. Ai, “PENGGUNAAN ANALISIS SENTIMEN UNTUK PERANCANGAN PRODUK: SEBUAH TINJAUAN PUSTAKA,” Jurnal Rekavasi, vol. 12, pp. 14–22, Jun. 2024, doi: 10.34151/rekavasi.v12i1.4660.
M. Harsanto and E. Sudarmilah, “TINJAUAN LITERATUR ANALISIS SENTIMEN PRODUK E-COMMERCE: DATASET, PENDEKATAN, METODE, DAN PERFORMA,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 10, pp. 2290–2303, Aug. 2025, doi: 10.29100/jipi.v10i3.8217.