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Journal : Jurnal Sistem informasi dan informatika (SIMIKA)

PERBANDINGAN METODE NAÏVE BAYES DAN SVM UNTUK SENTIMEN ANALISIS MASYARAKAT TERHADAP SERANGAN RANSOMWARE PADA DATA KIP-K Ramadan, Nabil Safiq; Darwis, Dedi
Jurnal Sistem Informasi dan Informatika (Simika) Vol 8 No 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3621

Abstract

This research examines ransomware attacks on KIP-K data by analyzing the opinions of Social Media X users, using the naïve bayes classifier (NBC) and support vector machine (SVM) methods. The rapid development of technology not only brings great benefits but also increases the risk of digital attacks by certain parties. One example is a ransomware attack that caused a KIP-K data leak. In this study, sentiment analysis was applied to identify public opinions or responses obtained from Social Media X, with the help of python programming and google colab. Of the total 2,648 raw data collected, pre-processing was carried out resulting in 1,738 cleaned data. The study compared two methods, namely Naïve Bayes and Support Vector Machine, to determine what method is more effective in analyzing public sentiment related to ransomware attacks on KIP-K data. The focus of this study is to understand the percentage of Social Media X users' comments and responses related to the KIP-K ransomware taken from media sosial X. The stages of sentiment analysis in this study include crawling, labeling, preprocessing, method classification, and visualization. Before the classification process was carried out, the data was divided into two parts, namely 30% for test data and 70% for training data. Data labeling resulted in 1,313 negative data, 957 positive data and 377 neutral data. The classification results show that the NBC method has an accuracy of 70%, while the SVsM achieves an accuracy of 88%. Based on these results, SVM is proven to be superior in data analysis compared to NBC, especially for big data.
Co-Authors . Yuniarwati ., Rusliyawati Abhishek R Mehta Abhishek R Mehta Ade Dwi Putra Ade Surahman Ade Surahman Adhie Thyo Priandika Aditia Yudhistira Agung Saputra Agus Wantoro Agustina, Intan Ahmad Ari Aldino Ahmad Ari Aldino Ahmad Suhendri Aidil Akbar Alita, Debby Andi Ilham Rahmansyah Andre Setiawan Andri Pramuditya An’ars, M. Ghufroni Aprian Nuriansah Ariany, Fenty Arie Qur’ania Aulia Mustika Sari Ayu Vidiasari Bambang Dwi Setyarto Bayu Dwi Juniansyah Budiawan, Aditia Chaswarina Nimas Maharani Cici Dian Paramita Damayanti, Damayanti Dartono Dartnono Dartono Dartono Dartono Dartono Depriansah Depriansah Dini Wahyuni Ditha Nurjayanti Dwi Andika Dwi Maila Pauristina Dwi Rahma Sari Eka Shintya Pratiwi Elvano Delisa Mega Endi Febrianto Fadila Shely Amalia Fahri Hanif Fatmawati Isnain Fernando, Yusra Fikri Hamidy Gunawan, Rakhmat Dedi Heni Sulistiani I Gede Heri Susanto Ichtiar Lazuardi Putra Ikbal Yasin Ilham Muhammad Ghoffar Ilham Utama Putra Imam Ahmad Ismail, Izudin Ismail, Izzudin Isnain, Auliya Rahman Kencono, Lintang Khoirunnisa, Yosi Kisworo KISWORO Kisworo Kisworo Lusiana Indawa M Joko Priono Maria Ainun Nazar Marzuki, Dwiki Hafizh Maulana, Nanda Arif Megawaty, Dyah Ayu Meylinda Meylinda Mirza Wijaya Putra Muhammad Bakri Muhammad Fauzan Ramadhani Muhammad Khotimul Anwar Nova Evrilia Novi Eka Wati Nugraha Ashari Nurhuda Budi Pamungkas Nurhuda Budi Pamungkas Nurul Hotimah Pasaribu, A. Ferico Octaviansyah Prabowo, Rizky Pramita, Galuh Prastowo, Agung Tri Priandika, Adhie Thyo Prita Dellia Purnomo Aji Putra, Ade Dwi Putri Lestari Rachmad Nugroho Ramadan, Nabil Safiq Rayin Biilmilah Rika Mersita Riskiono, Sampurna Dadi Ryan Randy Suryono Saefulloh Saefulloh Salsa Safhira Sampurna Dadi Riskiono Sanriomi Sintaro Saputra, Febrian Eko Sari, Priskila Lovika Setiawansyah Setiawansyah Sitna Hajar Hadad suaidah suaidah Surahman, Ade Surya Indra Gunawan Tika Yusiana Tithania Marta Putri Trisnawati, Fika Ummy Permata Hakim Vera Herlinda Very Hendra Very Hendra Saputra Very Hendra Saputra Very Hendra Saputra Wamiliana Wamiliana Wang, Junhai Waqas Arshad, Muhammad Wayan Kresna Yogi Swara yasin, ikbal Yuri Rahmanto Yusra Fernando Yusra Fernando Yusri Kusumayuda