Implementasi Machine Learning untuk Klasifikasi Email Spam Menggunakan Indobert, Hugging Face Transfomers dan Streamlit
DOI:
https://doi.org/10.59188/jurnalsostech.v6i1.32659Keywords:
deteksi email spam, klasifikasi teks, IndoBERT, deep learning, keamanan digitalAbstract
Perkembangan teknologi informasi menjadikan email sebagai media utama komunikasi modern, namun dominasi ini juga memunculkan tantangan serius berupa peningkatan email spam yang mengganggu produktivitas dan mengancam keamanan digital. Metode deteksi spam konvensional berbasis kata kunci dan aturan sederhana semakin tidak efektif karena tidak mampu mengikuti perkembangan pola bahasa spam yang dinamis dan kontekstual. Keterbatasan utama metode tersebut terletak pada ketidakmampuannya memahami makna semantik dalam teks email. Penelitian ini bertujuan merancang dan mengimplementasikan sistem deteksi email spam otomatis dan akurat menggunakan model bahasa modern. Metode yang diterapkan adalah klasifikasi teks biner berbasis deep learning. Proses penelitian meliputi pra-pemrosesan data untuk membersihkan dan menstandarkan teks email, tokenisasi menggunakan tokenizer IndoBERT, serta tahap klasifikasi dengan model IndoBERT yang di-fine-tune pada dataset email berbahasa Indonesia. Dataset dibagi ke dalam data latih, validasi, dan uji guna memastikan validitas serta kemampuan generalisasi model. Hasil evaluasi menunjukkan kinerja yang sangat baik, dengan akurasi sebesar 97% pada data uji, presisi 98%, dan recall 95% untuk kelas spam. Pengujian sistem secara end-to-end juga membuktikan keberhasilan implementasi model dalam skenario penggunaan nyata. Penelitian ini menyimpulkan bahwa pemanfaatan model bahasa lokal seperti IndoBERT merupakan pendekatan yang efektif dan andal untuk deteksi email spam, serta berpotensi menjadi dasar pengembangan sistem keamanan digital yang lebih canggih di masa mendatang.
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