Sentiment Analysis of YouTube Comments on Dedi Mulyadi's Policies Using Machine Learning Methods (Case Study: Juvenile Delinquency)
Analisis Sentimen Komentar Youtube Terhadap Kebijakan Dedi Mulyadi Dengan Metode Machine Learning (Studi Kasus : Kenakalan Remaja)
DOI:
https://doi.org/10.21070/ups.10439Keywords:
Analysis Sentiment, Ensemble Machine Learning, YoutubeAbstract
Juvenile delinquency prompted Dedi Mulyadi’s controversial policy of sending troubled youth to military barracks, generating diverse YouTube reactions. This study aims to accurately classify these sentiments and evaluate ensemble machine learning against single algorithms. Using 7,875 comments gathered via YouTube Data API, the research employed InSet Lexicon for labeling, TF-IDF for weighting, and Naïve Bayes, KNN, and Logistic Regression models. These were further combined using Ensemble Majority Voting. Results indicate that public sentiment is slightly positive (52.4%) compared to negative (47.6%). Logistic Regression achieved the highest individual accuracy (0.84), while the ensemble model maintained a stable performance of 0.82. These findings demonstrate that ensemble methods effectively enhance classification stability when analyzing public policy discourse on social media, providing a more robust framework for mapping public opinion dynamics.
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References
A. Mahesha, D. Anggraeni, and M. I. Adriansyah, “Mengungkap Kenakalan Remaja: Penyebab, Dampak, dan Solusi,” Prim. J. Ilm. Multidisiplin, vol. 2, no. 1, pp. 16–26, 2024, doi: 10.55681/primer.v2i1.278.
F. Afrita and F. Yusri, “Faktor-Faktor Yang Mempengaruhi Kenakalan Remaja,” Educ. J. Pendidik., vol. 2, no. 1, pp. 14–26, Dec. 2022, doi: 10.56248/educativo.v2i1.101.
Musta’a and R. Mesra, “Faktor Penyebab Kenakalan Remaja dan Cara Mengatasinya di KelurahanMentaya Seberang Seranau Kotawaringin Timur Kalimantan Tengah,” COMTE J. Sociol. Res. Educ., vol. 1, no. 5, pp. 239–249, 2024.
F. Bobyanti, “Kenakalan Remaja,” JERUMI J. Educ. Relig. Humanit. Multidiciplinary, vol. 1, no. 2, pp. 476–481, 2023, doi: 10.57235/jerumi.v1i2.1402.
N. Elfemi, Y. Yuhelna, D. K. Anggreta, I. Isnaini, E. Erningsih, and S. Sarbaitinil, “Sosialisasi Penanggulangan Kenakalan Remaja: Upaya Preventif pada Remaja Awal,” INTAN CENDEKIA J. Pengabdi. Masy., vol. 4, no. 1, pp. 30–36, 2023, doi: 10.47165/intancendekia.v4i1.638.
S. Azzahra, Z. Safardi, R. Adawiyyah, and B. Militer, “Membangun karakter disiplin melalui barak militer: analisis stakeholder dalam inovasi pendidikan,” vol. 9, pp. 105–118, 2025.
D. Marganingsih, H. Oktavianto, and G. Abdurrahman, “Analisis Sentimen Komentar Youtube Masterchef Indonesia Menggunakan Algoritma Support Vector Machine dan Gaussian Naïve Bayes,” J. Inform. dan Teknol. Pendidik., vol. 5, no. 1, pp. 16–26, 2025, doi: 10.59395/jitp.v5i1.117.
M. P. Munthe, A. S. R. Ansori, and ..., “Analisis Sentimen Komentar Pada Saluran Youtube Food Vlogger Berbahasa Indonesia Menggunakan Algoritma Naïve Bayes,” eProceedings Eng., vol. 8, no. 6, pp. 11909–11916, 2021, [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/16897
F. R. Dewi, “Klasifikasi Pola Peminjam Buku Berdasarkan Profesi Menggunakan Algoritma Naïve Bayes,” vol. 15, no. 2, pp. 278–291, 2025.
M. Ujaran et al., “Implementasi Convolutional Neural Network ( C NN ) Untuk,” vol. 14, no. 2, pp. 314–325, 2024.
N. A. Laia and S. P. Barus, “Analisis Sentimen Pengguna Youtube Pada Video Berjudul ‘10 Tahun Jokowi Jadi Presiden,’” JIKA (Jurnal Inform., vol. 9, no. 2, p. 169, 2025, doi: 10.31000/jika.v9i2.13470.
M. Rizky Herdiansyah and A. Yuliana, “Analisis Sentimen Kebijakan Kampus Merdeka Menggunakan Naïve Bayes Berdasarkan Komentar Pada Youtube,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 6, pp. 12454–12459, 2024, doi: 10.36040/jati.v8i6.11963.
D. N. Larasakti, A. Aziz, and D. Aditya, “Analisis Sentimen Komentar Video Youtube Dengan Metode K-Nearest Neighbor,” J. Ilm. Wahana Pendidik., vol. 2023, no. 5, pp. 132–142, 2023, [Online]. Available: https://doi.org/10.5281/zenodo.7728573
H. S. Mulyono and U. Saprudin, “Efektivitas Logistic Regression dalam Analisis Sentimen Berbahasa Indonesia pada Komentar YouTube tentang Isu,” vol. 6, no. 3, pp. 1547–1555, 2025.
M. F. Fachrudin, C. V. Angkoso, and D. A. Fatah, “ANALISIS SENTIMEN PADA SOSIAL MEDIA TWITTER TERHADAP KUALITAS JARINGAN INTERNET TELKOMSEL MENGGUNAKAN ENSEMBLE K-NEAREST NEIGHBOUR -SUPPORT VECTOR MACHINE SENTIMENT ANALYSIS ON TWITTER SOCIAL MEDIA ON TELKOMSEL ’ S INTERNET NETWORK QUALITY USING ENSEMBLE K-NEAREST NEIGHBOR -,” vol. 11, no. 6, pp. 1253–1264, 2024, doi: 10.25126/jtiik.2024118713.
M. I. Ahmadi, D. Gustian, and F. Sembiring, “Analisis Sentiment Masyarakat terhadap Kasus Covid-19 pada Media Sosial Youtube dengan Metode Naive bayes,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 807–814, 2021.
F. Ahluna, C. J. Tutuarima, and I. Santoso, “Metode K-Nearest Neighbor Untuk Analisis Sentimen Tentang Penghapusan Ujian Nasional,” J. Ikraith-Informatika, vol. 7, no. 2, pp. 1–6, 2023.
E. G. Radjah and A. C. Talakua, “Analisis Sentimen Komentar Terhadap Konten Tenun NTT di Youtube Menggunakan Metode SMOTE dan Logistic Regression,” vol. XIII, no. November, pp. 84–94, 2024.
K. Nandini and M. Rahardi, “Sentiment Analysis of Economic Policy Comments on YouTube Using Ensemble Machine Learning,” vol. 9, no. 5, 2025.
Z. Y. Burnama, M. A. Rosid, and N. L. Azizah, “Analisis Sentimen Pada Komentar Youtube Dalam Turnamen MPL Season 13 Dengan Metode Ensemble Machine Learning Sentiment Analysis on YouTube Comments in MPL Season 13 Tournament Using Ensemble Machine Learning Method”.
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