Analisis Sentimen Terhadap Kinerja Hukum Di Indonesia Berdasarkan Tanggapan Komentar Twitter Menggunakan Algoritma Naive Bayes
DOI:
https://doi.org/10.62951/ijies.v2i2.84Keywords:
Sentiment analysis, Legal Performance, Naïve Bayes, PythonAbstract
The development of social media, especially TWITTER, has become one of the main means for people to express opinions and criticism on various issues, including the performance of law in Indonesia. This study aims to analyze public sentiment towards the performance of law based on TWITTER user comments using the Naïve Bayes algorithm. The research data consists of 1004 comments collected from several videos related to legal topics. The analysis process includes the stages of data crawling, pre- processing (text cleaning, normalization, and tokenization), labeling sentiment into positive, negative, and neutral, and testing the Naïve Bayes model. The results show that the Naïve Bayes algorithm is able to classify sentiment with an accuracy level of 93.73%. The distribution of sentiment from 1004 comments shows that the majority of public opinion is (negative/positive/neutral), which indicates that public perception of the performance of law is still (critical/positive). These findings are expected to be input for related parties to understand public opinion and improve the quality of legal performance in
References
P. Y. Saputra, D. H. Subhi, F. Zain, and A. Winatama, “IMPLEMENTASI SENTIMEN ANALISIS KOMENTAR CHANNEL VIDEO PELAYANAN PEMERINTAH DI YOUTUBE MENGGUNAKAN ALGORITMA NAÏVE BAYES”.
D. Darwis, N. Siskawati, and Z. Abidin, “Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Review Data Twitter BMKG Nasional,” vol. 15, no. 1.
R. Fajar, S. Program, P. Rekayasa, N. Lunak, and R. Bengkalis, “Implementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Film Pada Twitter,” vol. 3, no. 1.
“Volume 3 Nomor 2 Juli 2021”, [Online]. Available: https://t.co/TIk5mK5bwS
A. N. Badri, N. Noviandi, F. Anastya, and M. Roland, “SENTIMENT ANALISIS UNTUK IDENTIFIKASI KEPUASAN MASYARAKAT TERHADAP
KENAIKAN BBM MENGGUNAKAN ALGORITMA NAÏVE BAYES,” JIKO
(Jurnal Informatika dan Komputer), vol. 7, no. 2, p. 287, Sep. 2023, doi: 10.26798/jiko.v7i2.873.
A. V. Sudiantoro and E. Zuliarso, “Analisis Sentimen Twitter Menggunakan Text Mining Dengan Algoritma Naïve Bayes Classifier ANALISIS SENTIMEN TWITTER MENGGUNAKAN TEXT MINING DENGAN ALGORITMA NAÏVE BAYES CLASSIFIER,” vol. 10, no. 2, pp. 69–73, 2018.
B. Gunawan, H. Sasty, P. #2, E. Esyudha, and P. #3, “JEPIN (Jurnal Edukasi dan Penelitian Informatika) Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes,” vol. 4, no. 2, pp. 17–29, 2018, [Online].
Available: www.femaledaily.com
F. Amaliah, I. Kadek, and D. Nuryana, “Perbandingan Akurasi Metode Lexicon Based Dan Naive Bayes Classifier Pada Analisis Sentimen Pendapat Masyarakat Terhadap Aplikasi Investasi Pada Media Twitter,” Journal of Informatics and Computer Science, vol. 03, 2022.
F. Khoirunnisa and S. Topiq, “ANALISIS SENTIMEN
TERHDAP KEPERCAYAAN MASYARAKAT PADA PROSES PENEGAK HUKUM DI
INDONESIA DENGAN MENGGUNAKAN ALGORITMA NAÏVE BAYES,”
Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3, Aug. 2024, doi: 10.23960/jitet.v12i3.4683.
A. Safira, A. S. Masyarakat… , and F. N. Hasan, “ANALISIS SENTIMEN MASYARAKAT TERHADAP PAYLATER MENGGUNAKAN METODE
NAIVE BAYES CLASSIFIER,” Jurnal Sistem Informasi, vol. 5, no. 1, 2023.
G. A. Buntoro, “ANALISIS SENTIMEN HATESPEECH PADA TWITTER DENGAN METODE NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR
MACHINE,” 2016. [Online]. Available:
https://www.researchgate.net/publication/309322787
P. Catur et al., “Moderasi Beragama Dalam Wacana Digital: Analisis Sentimen Twitter Terhadap Kasus Penolakan Rumah Ibadah Di Cilegon.”
A. Fauzi, R. Y. Fa’rifah, and E. N. Alam, “Analisis Sentimen Trend Makanan Dan Minuman dengan Support Vector Machine Sebagai Rekomendasi Peluang Bisnis Bagi UMKM.”
F. R. B. Kahi, A. Talakua, and R. Reynaldi, “Analisis Sentimen Masyarakat Di Twitter Terhadap Pemerintahan Anies Baswedan Menggunakan Metode Naive Bayes Classifier,” Jurnal Minfo Polgan, vol. 13, no. 1, pp. 324–336, Apr. 2024, doi: 10.33395/jmp.v13i1.13636.
P. A. Permatasari, L. Linawati, and L. Jasa, “Survei Tentang Analisis Sentimen Pada Media Sosial,” Majalah Ilmiah Teknologi Elektro, vol. 20, no. 2, p. 177, Dec. 2021, doi: 10.24843/mite.2021.v20i02.p01.
S. Suryono, D. Emha, and T. Luthfi, “Analisis sentimen pada twitter dengan menggunakan metode Naïve Bayes Classifier.” [17] “Kriminalisasi_dalam_Hukum_Pidana”.
F. A. Wenando, “Analisis Opini Publik Terhadap Undang-Undang KUHP Tahun 2022 Menggunakan Algoritma Naïve Bayes”.
A. Deviyanto, M. R. Didik Wahyudi, and T. Informatika UIN Sunan Kalijaga Yogyakarta Jl Marsda Adi Sucipto No, “PENERAPAN ANALISIS SENTIMEN PADA PENGGUNA TWITTER MENGGUNAKAN METODE K-NEAREST
NEIGHBOR,” Jurnal Informatika Sunan Kalijaga), vol. 3, no. 1, pp. 1–13, 2018, [Online]. Available:
https://twitter.com/search?l=id&q=AHY%20since%3A2017-01-
%20until%3A2017-01-
D. Duei Putri, G. F. Nama, and W. E. Sulistiono, “Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 10, no. 1, Jan. 2022, doi: 10.23960/jitet.v10i1.2262.
A. A. Soebroto, “Buku Ajar AI, Machine Learning & Deep Learning,” 2019. [Online]. Available: https://www.researchgate.net/publication/348003841
MACHINE LEARNING ALGORITHMS and their Use Cases. [Online]. Available: www.freepik.com
F. Adiwidya Pradana, “Penggunaan Artificial Intelligence dan Machine Learning dalam Pembelajaran Ilmu Eksakta”, doi: 10.13140/RG.2.2.21555.95527.
P. Santoso, H. Abijono, and N. L. Anggreini, “ALGORITMA SUPERVISED LEARNING DAN UNSUPERVISED LEARNING DALAM PENGOLAHAN
DATA,” Unira Malang |, vol. 4, no. 2, 2021.
“LEMBAR HAK ATAS KEKAYAAN INTELEKTUAL.” [26]
“Buku_Ajar_Kecerdasan_Buatan”. [27] “Buku_Ajar_Machine_Learning”.
“Konsep_Dasar_Natural_Language_Processing”.
“Buku_ajar_kecerdasan_buatan_disertai_pra”.
“Text_Mining”.
“Text_Mining_with_Machine_Learning”.
“Text_Mining_of_Web_Based_Medical_Content”.
E. Manalu, F. A. Sianturi, and M. R. Manalu, “(),” Jl. Iskandar Muda No.1 Medan, vol. 1, no. 2, p. 1, 2015.
“TikTok”.
A. Zulzila, Latifah Jayatri Febiola, and Dodi Vionanda, “Analisis Sentimen Masyarakat Terhadap Korupsi Berdasarkan Tweet Menggunakan Klasifikasi Naive Bayes,” UNP Journal of Statistics and Data Science, vol. 3, no. 1, pp. 72– 78, Feb. 2025, doi: 10.24036/ujsds/vol3-iss1/345.
S. Aminah and S. Muyassaroh, “Analisis Konten Tiktok War Takjil Ramadhan dalam menumbuhkan Sikap Toleransi antar Umat Beragama,” Jurnal Bisnis dan Komunikasi Digital, vol. 2, no. 1, p. 11, Sep. 2024, doi: 10.47134/jbkd.v2i1.3186.
S. K. Khotimah, I. D. Savitri, N. Darari, and S. R. Aqiqi, “Takjil War di Media Sosial: Ruang Simbolik Budaya Populer, Integrasi Sosial, dan Toleransi Beragama,” Jurnal Kawistara, vol. 15, no. 1, p. 118, Jun. 2025, doi: 10.22146/kawistara.102800.
Friska Aditia Indriyani, Ahmad Fauzi, and Sutan Faisal, “Analisis sentimen aplikasi tiktok menggunakan algoritma naïve bayes dan support vector machine,” TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika, vol. 10, no. 2, pp. 176– 184, Jul. 2023, doi: 10.37373/tekno.v10i2.419.
“Penerapan_Metode_Naive_Bayes_dan_Skala_L”.
F. Adelia Irawan, A. Rialdy Atmadja, A. Wahana, T. Informatika, and U. Sunan Gunung Djati Bandung, “Analisis Sentimen Ulasan Aplikasi Bank Digital Menggunakan Algoritma Naive Bayes,” 2024.
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