IMPLEMENTASI TEXT MINING DALAM IDENTIFIKASI CYBER HATE SPEECH MENGGUNAKAN SUPPORT VECTOR MACHINE
DOI:
https://doi.org/10.31000/jika.v8i4.12581Abstrak
Media sosial sekarang menjadi tempat bagi orang untuk menyampaikan pendapat mereka dan ada banyak cara untuk menyampaikan pendapat tersebut, baik positif maupun negatif. Salah satu contohnya adalah ujaran kebencian dunia maya, juga dikenal sebagai ujaran kebencian dunia maya, yang merupakan bentuk ekspresi yang dilakukan untuk menyebarkan rasa kebecian seperti pencemaran nama baik, penistaan agama, rasisme, dan pelanggaran hak asasi manusia. Penelitian ini dilakukan untuk bisa mengidentifikasi cyber hate speech dalam bentuk teks pada X, yang dibagi 2 kelas yaitu pencemaran nama baik dan penistaan agama. metode yang digunakan adalah SVM (Support Vector Machine) pada penelitian ini dilakukasn beberapa proses seperti pengumpulan data, pelabelan secara manual, pra-proses data dengan text mining, pembobotan dengan tf-idf, klasifikasi dengan merancang model klasifikasi metode support vector machine dan klasifikasi menggunakan data latih dan data uji. Ada 800 data tweet yang digunakan untuk pencemaran nama baik dan penistaan agama dalam bahasa Indonesia, dengan 80% adalah 640 data latihan dan 20% adalah 160 data uji. Hasil pengujian Metode SVM menunjukkan akurasi 96%.Referensi
Amina, S. (2022). Ujaran Kebencian Melalui Media Sosial Dalam Undang-Undang Dan Hukum Islam. Skripsi. Repository Institut Agama Islam Negeri Palopo.
Casro, C., Purwati, Y., Setyaningsih, G., & Kuncoro, A. P. (2020). Rancang Bangun Aplikasi Pengaduan Pelanggan Berbasis Web Menggunakan Framework Codeigniter Di Indotechno Purwokerto. Jurnal Sains Dan Informatika, 6(2), 166–174. https://doi.org/10.34128/jsi.v6i2.244
Delvyan Putri Surya Ningrum, & Jamiatur Robekha. (2023). Analisa Yuridis Dalam Kasus Kejahatan Siber Terhadap Internet Banking di Indonesia. PESHUM : Jurnal Pendidikan, Sosial Dan Humaniora, 2(4), 765–776. https://doi.org/10.56799/peshum.v2i4.2115
Fahlevvi, M. R. (2022). 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, 4(1), 1–13. https://doi.org/10.33701/jtkp.v4i1.2701
Furqan, M., Ikhsan, M., & Aini, R. (2023). Algoritma Support Vector Machine Untuk Analisis Sentimen Masyarakat Indonesia Terhadap Pandemi Virus Corona Di Media Sosial. Kesatria : Jurnal Penerapan Sistem Informasi (Komputer Dan Manajemen), 4(4), 908–915. Retrieved from
Furqan, M., Kurniawan, R., & HP, K. (2020). Evaluasi Performa Support Vector Machine Classifier Terhadap Penyakit Mental. Jsinbis, 10(2), 203–210. https://doi.org/10.21456/vol10iss2pp203-210
Handayani, A., & Zufria, I. (2023). Analisis Sentimen Terhadap Bakal Capres RI 2024 di Twitter Menggunakan Algoritma SVM. Journal of Information System Research (JOSH), 5(1), 53–63. https://doi.org/10.47065/josh.v5i1.4379
Hermawan, A., Jowensen, I., Junaedi, J., & Edy. (2023). Implementasi Text-Mining untuk Analisis Sentimen pada Twitter dengan Algoritma Support Vector Machine. JST (Jurnal Sains Dan Teknologi), 12(1), 129–137. https://doi.org/10.23887/jstundiksha.v12i1.52358
Maturbongs, Y. H. (2019). Tantangan Era Globalisasi terhadap Manajemen Perguruan Tinggi. Jurnal Administrasi Dan Kesekretarisan, 4(2), 122–141. Retrieved from http://www.jurnal.stiks-tarakanita.ac.id/index.php/JAK/article/view/254/166
Ningrum, D. J., Suryadi, S., & Chandra Wardhana, D. E. (2019). Kajian Ujaran Kebencian Di Media Sosial. Jurnal Ilmiah KORPUS, 2(3), 241–252. https://doi.org/10.33369/jik.v2i3.6779
Priyatno, A. M., Prasetya, M. R. A., Cholidhazia, P., & Sari, R. K. (2024). Comparison of Similarity Methods on New Student Admission Chatbots Using Retrieval-Based Concepts. Journal of Engineering and Science Application, 1(1), 32–40. https://doi.org/10.69693/jesa.v1i1.2
Rahayu, K., Fitria, V., Septhya, D., Rahmaddeni, R., & Efrizoni, L. (2023). Klasifikasi Teks untuk Mendeteksi Depresi dan Kecemasan pada Pengguna Twitter Berbasis Machine Learning. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(2), 108–114. https://doi.org/10.57152/malcom.v3i2.780
Ramadhan. (2021). Analisis Hukum Terhadap Penyebaran Berita Bohong/Hoax Sebagai Bentuk Cyber Crime Di Indonesia (Studi Putusan No. 3478/Pid. Sus/2019/Pn. Mdn) (Vol. 44). Universitas Medan Area.
Sari, H., Ginting, G. L., & Zebua, T. (2021). Penerapan Algoritma Text Mining dan TF-IDF Untuk Pengelompokan Topik Skripsi Pada Aplikasi Repository STMIK Budi Darma. 2(7), 414–432.
Simarmata, J., Iqbal, M., Hasibuan, M. S., Limbong, T., & Albra, W. (2019). Hoaks dan Media Sosial: Saring Sebelum Sharing. In Alex Rikki (Ed.), Serial Buku Saku. Medan: Yayasan Kita Menulis.
Siregar, M. R. S., Samsudin, & Putri, R. A. (2023). Sistem Informasi Geografis Dalam Monitoring Daerah Prioritas Penanganan Stunting Pada Anak Di Kota Medan. Journal of Science and Social Research, 6(3), 643–648.
Syakur, M. (2021). Ujaran Kebencian dalam Al-Qur’Än Hate speech in Al-Qur’Än. Hermeneutik : Jurnal Ilmu Al-Qur’an Dan Tafsir, 15, 335–358.
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with International Journal of Advances in Intelligent Informatics agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.Â
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Licensing for Data Publication
International Journal of Advances in Intelligent Informatics use a variety of waivers and licenses, that are specifically designed for and appropriate for the treatment of data:
Open Data Commons Attribution License, http://www.opendatacommons.org/licenses/by/1.0/ (default)
Creative Commons CC-Zero Waiver, http://creativecommons.org/publicdomain/zero/1.0/
Open Data Commons Public Domain Dedication and Licence, http://www.opendatacommons.org/licenses/pddl/1-0/
Other data publishing licenses may be allowed as exceptions (subject to approval by the editor on a case-by-case basis) and should be justified with a written statement from the author, which will be published with the article.
Open Data and Software Publishing and Sharing
The journal strives to maximize the replicability of the research published in it. Authors are thus required to share all data, code or protocols underlying the research reported in their articles. Exceptions are permitted but have to be justified in a written public statement accompanying the article.
Datasets and software should be deposited and permanently archived inappropriate, trusted, general, or domain-specific repositories (please consult http://service.re3data.org and/or software repositories such as GitHub, GitLab, Bioinformatics.org, or equivalent). The associated persistent identifiers (e.g. DOI, or others) of the dataset(s) must be included in the data or software resources section of the article. Reference(s) to datasets and software should also be included in the reference list of the article with DOIs (where available). Where no domain-specific data repository exists, authors should deposit their datasets in a general repository such as ZENODO, Dryad, Dataverse, or others.
Small data may also be published as data files or packages supplementary to a research article, however, the authors should prefer in all cases a deposition in data repositories.
