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All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Fitria, Denisa
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Computer Science & IT Decision Sciences, Operations Research & Management

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Pemilihan Algoritma Terbaik Untuk Klasifikasi Jenis E-Mail dengan Metode TF-IDF Fitria, Denisa; Cahyana, Yana; Sulistya, Dwi; Baihaqi, Kiki Ahmad
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 1 (2024): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i1.747

Abstract

Spam emails, sent en masse to numerous addresses, are a major annoyance. To combat this, effective filters are necessary, such as classification to separate spam from non-spam. This can be achieved through an anti-spam model utilizing text mining like TF-IDF. Using the KDD process, a study analyzed a dataset of 6046 entries, split 77.2% non-spam and 22.8% spam. Logistic Regression showed the best accuracy at 98%, outperforming Decision Tree (59%) and Support Vector Machine (95%). Thus, Logistic Regression emerged as the optimal algorithm for email classification.
Co-Authors Baihaqi, Kiki Ahmad Sulistya, Dwi Yana Cahyana