Analisis Indikasi Phishing Pada Pesan Email Menggunakan Metode SVM
DOI:
https://doi.org/10.29407/ee7a1p78Keywords:
Email, Keamanan Siber, Phishing, Support Vector Machine (SVM), TF-IDFAbstract
Serangan phishing melalui layanan surat elektronik (email) merupakan salah satu ancaman keamanan siber terbesar saat ini yang bertujuan untuk mencuri informasi sensitif pengguna. Volume serangan yang terus meningkat menuntut adanya sistem deteksi otomatis yang akurat dan efisien. Penelitian ini bertujuan untuk membangun model deteksi phishing pada pesan email menggunakan algoritma Machine Learning, yaitu Support Vector Machine (SVM). Dataset yang digunakan adalah Phishing Validation Emails yang terdiri dari email kategori aman (ham) dan phishing. Tahapan penelitian meliputi pra-pemrosesan teks (preprocessing) seperti cleaning, stopword removal, dan stemming, serta ekstraksi fitur menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF) untuk mengonversi teks menjadi vektor numerik. Data dibagi menjadi data latih dan data uji untuk mengevaluasi kinerja model. Hasil eksperimen menunjukkan bahwa metode SVM dengan kernel Linear mampu memisahkan kelas email dengan sangat efektif. Berdasarkan pengujian menggunakan confusion matrix, model yang diusulkan berhasil mencapai performa sempurna dengan nilai Akurasi, Presisi, Recall, dan F1-Score masing-masing sebesar 100%. Hasil ini mengindikasikan bahwa kombinasi SVM dan TF-IDF sangat andal dalam mengidentifikasi pola serangan phishing pada dataset yang digunakan.
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References
[1] ahmadani, F., et al. (2023). Deteksi Phishing Email Menggunakan Algoritma Support Vector Machine. Aicoms Journal, Politap. [Online]
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