Public Sentiment Analysis of the Agrarian Conflict between PT TPL and the Toba Simalungun Indigenous Community Using the SVM Method

Penulis

  • Dian Yusri Andira STIKOM Tunas Bangsa
  • Deswita Maharani Harahap STIKOM Tunas Bangsa
  • Vibiola Br Damanik STIKOM Tunas Bangsa
  • Indah Frian Sari STIKOM Tunas Bangsa
  • Victor Asido Elyakim P STIKOM Tunas Bangsa

DOI:

https://doi.org/10.55123/jomlai.v4i3.6116

Kata Kunci:

Sentiment Analysis, Agrarian, Tiktok , Support Vector Machine, TF-IDF

Abstrak

The agrarian conflict between PT Toba Pulp Lestari and the Toba Simalungun indigenous community has generated diverse public opinions on social media. This study aims to analyze public sentiment regarding the conflict using the Support Vector Machine (SVM) method based on TikTok comment data. A total of 1,751 comments were collected via the TikTok API and processed through cleaning, normalization, stopword removal, and stemming. Sentiment labeling was performed automatically with a lexical-based approach, followed by feature weighting using Term Frequency-Inverse Document Frequency (TF-IDF). The SVM model was used to classify public sentiment into two main categories, namely positive and negative. The results of the testing showed that the SVM model was able to achieve an accuracy of 80%, with excellent performance in detecting negative sentiment. Additional analysis through wordcloud visualization shows the dominant words in each sentiment category, which reinforces the model's classification results. The findingsof this study provide an objective picture of public opinion patterns on social media, while also demonstrating the potential application of machine learning-based sentiment analysis methods to understand public perceptions of other social issues in the future.

Referensi

A. Alfian, K. Kudussisara, N. S. Maimunah, and I. Susana, “Masyarakat Adat dan Toba PULP Lestari: Pemetaan Aktor dan Analisis Konflik Agraria,” Aceh Anthropological Journal, vol. 9, no. 1, pp. 53–69, May 2025, doi: 10.29103/aaj.v9i1.20332.

F. Tobing, “SENGKETA TANAH ANTARA MASYARAKAT ADAT BATAK DENGAN PT. TOBA PULP LESTARI DAN PELANGGARAN PERBUATAN-PERBUATAN YANG MENCIDERAI ATURAN KEHUTANAN DI WILAYAH SUMATERA UTARA,” Jurnal Ilmu Hukum Sui Generis, vol. 2, no. 2, pp. 77–81, 2022, doi: 10.23887/jih.v2i2.1014.

N. Norlaila, W. W. Winarno, and E. T. Luthfi, “Analisis Sentimen Masyarakat Tentang Tambang Di Indonesia Pada Twitter Menggunakan Data Mining,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 3, pp. 1091–1099, Aug. 2024, doi: 10.29100/jipi.v9i3.5402.

R. Darman, “ANALISIS SENTIMEN RESPONS TWITTER TERHADAP PERSYARATAN BADAN PENYELENGGARA JAMINAN SOSIAL (BPJS) DI KANTOR PERTANAHAN,” JURNAL WIDYA BHUMI, vol. 3, pp. 113–136, 2023, doi: 10.31292/wb.v3i2.61.

D. Ratna Rohmania and R. Abidin, “LogicLink : Journal of Artificial Intelligence and Multimedia in Informatics Analisis Sentimen Masyarakat Indonesia Terhadap Kebijakan Program Tapera Menggunakan Brand24,” LogicLink : Journal of Artificial Intelligence and Multimedia in Informatics, vol. 1, no. 2, pp. 120–131, 2024, doi: 10.28918/logiclink.v1i2.8736.

A. P. Putra and A. F. Syafira, “Analisis Sentimen Data Twitter Topik Politik Dengan Metode Naive Bayes Dan Convolutional Neural Networks (Cnn),” Jurnal Ilmiah Wahana Pendidikan, Oktober, vol. 9, no. 20, pp. 36–41, 2023, doi: 10.5281/zenodo.8396579.

K. X. Han, W. Chien, C. C. Chiu, and Y. T. Cheng, “Application of support vector machine (SVM) in the sentiment analysis of twitter dataset,” Applied Sciences (Switzerland), vol. 10, no. 3, Feb. 2020, doi: 10.3390/app10031125.

F. Es-sabery, I. Es-sabery, J. Qadir, B. Sainz-de-Abajo, and B. Garcia-Zapirain, “A hybrid Hadoop-based sentiment analysis classifier for tweets associated with COVID-19 utilizing two machine learning algorithms: CNN, and fuzzy C4.5,” J Big Data, vol. 11, no. 1, Dec. 2024, doi: 10.1186/s40537-024-01014-4.

I. M. Parapat, M. T. Furqon, and Sutrisno, “Penerapan Metode Support Vector Machine (SVM) Pada Klasifikasi Penyimpangan Tumbuh Kembang Anak,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 10, pp. 3163–3169, 2018.

M. Muchtar and R. A. Muchtar, “PERBANDINGAN METODE KNN DAN SVM DALAM KLASIFIKASI KEMATANGAN BUAH MANGGA BERDASARKAN CITRA HSV DAN FITUR STATISTIK,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 2, Apr. 2024, doi: 10.23960/jitet.v12i2.4010.

F. C. Onwuegbuche, J. M. Wafula, and J. K. Mung’atu, “Support Vector Machine for Sentiment Analysis of Nigerian Banks Financial Tweets,” Journal of Data Analysis and Information Processing, vol. 07, no. 04, pp. 153–173, 2019, doi: 10.4236/jdaip.2019.74010.

N. Hendrastuty, A. Rahman Isnain, and A. Yanti Rahmadhani, “Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine,” Jurnal Informatika: Jurnal pengembangan IT (JPIT), vol. 6, no. 3, pp. 150–55, 2021, doi: 10.30591/jpit.v6i3.2870.

M. R. Fahlevvi, “Analisis Sentimen Terhadap Ulasan Aplikasi Pejabat Pengelola Informasi dan Dokumentasi Kementerian Dalam Negeri Republik Indonesia di Google Playstore Menggunakan Metode Support Vector Machine,” Jurnal Teknologi dan Komunikasi Pemerintahan, vol. 4, no. 1, pp. 1–13, 2022, [Online]. Available: http://ejournal.ipdn.ac.id/JTKP,

V. Fitriyana et al., “Analisis Sentimen Ulasan Aplikasi Jamsostek Mobile Menggunakan Metode Support Vector Machine,” 2023.

S. D. Simamora, F. Irwiensyah, and F. N. Hasan, “Analisis Sentimen Terkait Konflik Palestina-Israel Pada Media Sosial X Menggunakan Algoritma Naïve Bayes Classifier,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 1, Jun. 2024, doi: 10.47065/bits.v6i1.5344.

M. Dehghani and Z. Yazdanparast, “Political Sentiment Analysis of Persian Tweets Using CNN-LSTM Model,” 2023. doi: 10.48550/arXiv.2307.07740.

G. Z. Rahma and M. Rosyda, “Perbandingan Kinerja Algoritma Convolutional Neural Network (CNN) dan Recurrent Neural Network (RNN) pada Analisis Sentimen Pemilu Presiden 2024,” Jurnal Pendidikan dan Teknologi Indonesia, vol. 5, no. 2, Feb. 2025, doi: 10.52436/1.jpti.652.

M. Hadi Arfian et al., “Analisis Sentimen Pada Media Sosial Menggunakan Metode Support Vector Machine,” Jurnal Ilmu Teknik dan Komputer, vol. 09, no. 01, pp. 1–6, 2025, doi: 10.22441/jitkom.v9i1.001.

A. Liawati, R. Narasati, D. Solihudin, C. Lukman Rohmat, and S. Eka Permana, “ANALISIS SENTIMEN KOMENTAR POLITIK DI MEDIA SOSIAL X DENGAN PENDEKATAAN DEEP LEARNING,” 2023.

Z. Alhaq, A. Mustopa, S. Mulyatun, and J. D. Santoso, “PENERAPAN METODE SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN PENGGUNA TWITTER,” JOISM : JURNAL OF INFORMATION SYSTEM MANAGEMENT, vol. 3, no. 1, pp. 16–1, 2021, doi: 10.24076/joism.2021v3i2.558.

M. Rahardi, A. Aminuddin, F. F. Abdulloh, and R. A. Nugroho, “Sentiment Analysis of Covid-19 Vaccination using Support Vector Machine in Indonesia,” (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 13, no. 6, pp. 534–539, 2022, doi: 10.14569/IJACSA.2022.0130665.

Unduhan

Diterbitkan

2025-09-15

Cara Mengutip

Dian Yusri Andira, Deswita Maharani Harahap, Vibiola Br Damanik, Indah Frian Sari, & Victor Asido Elyakim P. (2025). Public Sentiment Analysis of the Agrarian Conflict between PT TPL and the Toba Simalungun Indigenous Community Using the SVM Method. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 4(3), 171–179. https://doi.org/10.55123/jomlai.v4i3.6116

Terbitan

Vol 4 No 3 (2025): September 2025

Bagian

Articles

Lisensi

Hak Cipta (c) 2025 Dian Yusri Andira, Deswita Maharani Harahap, Vibiola Br Damanik, Indah Frian Sari, Victor Asido Elyakim P

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