Klasifikasi Sentimen Pada Dataset yang Terbatas Menggunakan Algoritma Convolutional Neural Network
DOI:
https://doi.org/10.47065/bulletincsr.v5i4.613Keywords:
Sentiment Classification; Convolutional Neural Network; Kaesang Pangarep; Limited Dataset; Indonesian Solidarity PartyAbstract
This study aims to analyze public responses to the appointment of Kaesang Pangarep as the Chairman of the Indonesian Solidarity Party (PSI) using a sentiment classification approach based on the Convolutional Neural Network (CNN) algorithm. The primary dataset consists of 300 Indonesian-language tweets categorized into three sentiment classes: positive, negative, and neutral. The limited size of the training data presents a major challenge, as it can hinder the model's ability to generalize. To address this issue, data augmentation was carried out by incorporating external datasets with Covid-19 and Open Topic themes. The preprocessing stages include text cleaning, normalization, and tokenization. The developed CNN model utilizes a layered architecture and applies regularization techniques such as L2 and dropout to reduce the risk of overfitting. Accuracy, F1-score, precision, and recall were used as performance evaluation metrics. Experimental results show that the best performance was achieved when the Kaesang and Covid-19 datasets were combined, yielding an F1-score of 0.62 on the validation set and 0.51 on the test set. These findings indicate that adding external data can improve classification accuracy, even under limited data conditions. This study contributes to the development of deep learning-based sentiment classification methods for Indonesian-language texts.
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T. S. Sabrila, V. R. Sari, and A. E. Minarno, “Analisis Sentimen Pada Tweet Tentang Penanganan Covid-19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K-Nearest Neighbor,” Fountain of Informatics Journal, vol. 6, no. 2, p. 69, Jul. 2021, doi: 10.21111/fij.v6i2.5536.
T. Spinde, E. Richter, M. Wessel, J. Kulshrestha, and K. Donnay, “What do Twitter comments tell about news article bias? Assessing the impact of news article bias on its perception on Twitter,” Online Soc Netw Media, vol. 37–38, Sep. 2023, doi: 10.1016/j.osnem.2023.100264.
N. Rohman, “Peran Partai Solidaritas Indonesia (PSI) dalam Pemilihan Presiden 2024: Analisis Terhadap Pemilih Pemula,” JPW (Jurnal Politik Walisongo), vol. 5, no. 1, pp. 85–102, 2023, doi: 10.21580/jpw.v5i1.18330.
K. Tsabitah and I. Suryawati, “Analisis Wacana Kritis Pidato Pertama Kaesang Pangarep sebagai Ketua Umum Partai Solidaritas Indonesia,” CARAKA?: Indonesia Journal of Communication, vol. 5, no. 1, pp. 27–38, 2024, doi: 10.25008/caraka.v5i1.109.
S. Agustian et al., “Arah Baru Penelitian Klasifikasi Teks: Memaksimalkan Kinerja Klasifikasi Sentimen dari Data Terbatas,” arXiv preprint, pp. 1–9, Jul. 2024, doi: https://doi.org/10.48550/arXiv.2407.05627.
M. Ravil, S. Agustian, M. Fikry, and F. Insani, “Peningkatan Performa Klasifikasi Sentimen Tweet Kaesang Menggunakan Naïve Bayes dengan PSO pada Dataset Kecil,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 6, pp. 2909–2917, 2024, doi: 10.30865/klik.v4i6.1939.
P. Zahwa, S. Agustian, F. Yanto, and S. Baru, “Implementasi Bi-Directional Long Short Term Memory Terhadap Klasifikasi Sentimen di Twitter Pada Dataset Terbatas,” ZONAsi: Jurnal Sistem Informasi, vol. 7, no. 1, pp. 11–24, 2023, doi: https://doi.org/10.31849.
Y. El Saputra, S. Agustian, and S. Ramadhani, “Klasifikasi Sentimen SVM Dengan Dataset yang Kecil Pada Kasus Kaesang Sebagai Ketua Umum PSI,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 6, pp. 2902–2908, 2024, doi: 10.30865/klik.v4i6.1944.
S. N. Listyarini and D. A. Anggoro, “Analisis Sentimen Pilkada di Tengah Pandemi Covid-19 Menggunakan Convolution Neural Network (CNN),” Jurnal Pendidikan dan Teknologi Indonesia, vol. 1, no. 7, pp. 261–268, Jul. 2021, doi: 10.52436/1.jpti.60.
B. A. Yuniarossy et al., “Analisis Sentimen Terhadap Isu Feminisme di Twitter Menggunakan Model Convolutional Neural Network (Cnn),” Lebesgue: Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika, vol. 5, no. 1, 2024, doi: 10.46306/lb.v5i1.
F. A. Irawan and D. A. Rochmah, “Penerapan Algoritma CNN Untuk Mengetahui Sentimen Masyarakat Terhadap Kebijakan Vaksin Covid-19,” JURNAL INFORMATIKA, vol. 9, no. 2, 2022, doi: https://doi.org/10.31294.
N. Satya Marga, A. Rahman Isnain, and D. Alita, “Sentimen Analisis Tentang Kebijakan Pemerintah Terhadap Kasus Corona Menggunakan Metode Naive Bayes,” Jurnal Informatika dan Rekayasa Perangkat Lunak (JATIKA), vol. 4, no. 4, pp. 453–463, 2021, doi: https://doi.org/10.33365.
R. Illahi, S. Agustian, Jasril, and F. Yanto, “Klasifikasi Sentimen Menggunakan Bidirectional Lstm dan Indobert Dengan Dataset Terbatas,” ZONAsi: Jurnal Sistem Informasi, vol. 7, no. 1, 2025, doi: https://doi.org/10.31849.
L. Ashbaugh and Y. Zhang, “A Comparative Study of Sentiment Analysis on Customer Reviews Using Machine Learning and Deep Learning,” Computers, vol. 13, no. 12, Dec. 2024, doi: 10.3390/computers13120340.
E. Setia Budi, A. Nofriyaldi Chan, P. Priscillia Alda, and M. Arif Fauzi Idris, “Optimasi Model Machine Learning untuk Klasifikasi dan Prediksi Citra Menggunakan Algoritma Convolutional Neural Network,” RESOLUSI?: Rekayasa Teknik Informatika dan Informasi, vol. 4, no. 5, pp. 502–509, 2024, doi: https://doi.org/10.30865.
M. Haikal and U. Hayati, “Analisis Sentimen Terhadap Penggunaan Aplikasi Game Online Pubg Mobile Menggunakan Algoritma Naive Bayes,” Jurnal Mahasiswa Teknik Informatika, vol. 7, no. 6, 2023, doi: https://doi.org/10.36040.
I. Habib Kusuma and N. Cahyono, “Analisis Sentimen Masyarakat Terhadap Penggunaan E-Commerce Menggunakan Algoritma K-Nearest Neighbor,” Jurnal pengembangan IT (JPIT), vol. 8, no. 3, 2023, doi: https://doi.org/10.30591.
S. BAYAT and G. I?IK, “Evaluating the Effectiveness of Different Machine Learning Approaches for Sentiment Classification,” I?d?r Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 13, no. 3, pp. 1496–1510, Sep. 2023, doi: 10.21597/jist.1292050.
P. Yohana, S. Agustian, and S. Kurnia Gusti, “Klasifikasi Sentimen Masyarakat terhadap Kebijakan Vaksin Covid-19 pada Twitter dengan Imbalance Classes Menggunakan Naive Bayes,” Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI), vol. 26, pp. 69–80, Oct. 2022, [Online]. Available: https://ejournal.uin-suska.ac.id/index.php/SNTIKI/article/view/19012
J. Pranata, S. Agustian, J. Jasril, and E. Haerani, “Penggunaan Model Bahasa indoBERT pada metode Random Forest untuk Klasifikasi Sentimen dengan Dataset Terbatas,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 3, pp. 1668–1676, Dec. 2024, doi: 10.47065/bits.v6i3.6335.
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