Komparasi Algoritma Naive Bayes dan Random Forest untuk Identifikasi Kata Berpotensi Spam
DOI:
https://doi.org/10.29407/5v86qx08Keywords:
NLP, Machine Learning, Klasifikasi Teks, Email spam, Naive BayesAbstract
Penelitian ini mengeksplorasi penerapan machine learning untuk mendeteksi kata berpotensi spam pada Ling-Spam Dataset, yang menghadapi tantangan distribusi kelas tidak seimbang (imbalanced) dalam penyaringan pesan elektronik. Metode Naive Bayes dan Random Forest digunakan untuk mengatasi masalah inefisiensi filter berbasis aturan statis yang sering gagal mengenali pola promosi dinamis. Naive Bayes bekerja sebagai baseline probabilistik, sementara Random Forest menerapkan mekanisme ensemble untuk meningkatkan stabilitas prediksi pada fitur leksikal yang kompleks. Dataset terdiri dari 2.893 email yang diproses melalui ekstraksi fitur TF-IDF, dengan skema pembagian data terstratifikasi (80% pelatihan, 20% pengujian) guna menjaga proporsi kelas. Hasil eksperimen menunjukkan Naive Bayes mencatatkan akurasi global tertinggi sebesar 99,47%, sedangkan Random Forest menghasilkan akurasi 98,42% dengan keunggulan nilai presisi sempurna mencapai 100%. Temuan ini membuktikan bahwa pendekatan ensemble lebih efektif dalam meminimalisir kesalahan positif palsu (false positive), memberikan solusi filtrasi yang objektif dan andal untuk meningkatkan keamanan email tanpa memblokir pesan yang sah
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