Sistem Deteksi dan Penyaringan Pesan Spam SMS Berbasis Android
DOI:
https://doi.org/10.29407/r8pjjg86Keywords:
Android, Klasifikasi Teks, Logistic Regression, SMS Spam, Pembelajaran MesinAbstract
Pesan spam pada layanan Short Message Service (SMS) masih sering dimanfaatkan untuk penipuan dan penyalahgunaan data pribadi sehingga diperlukan sistem yang mampu melakukan penyaringan pesan secara otomatis pada perangkat seluler. Penelitian ini mengembangkan aplikasi penyaringan SMS spam berbasis Android dengan mengintegrasikan antarmuka Flutter dan mesin klasifikasi berbasis Python melalui plugin Chaquopy. Metode klasifikasi yang digunakan adalah Logistic Regression dengan tahapan prapemrosesan teks, ekstraksi fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF), serta pelatihan dan pengujian model pada dataset SMS berbahasa Indonesia yang telah dilabeli ke dalam kelas spam dan non-spam. Hasil evaluasi menunjukkan bahwa model menghasilkan akurasi sebesar 96%, dengan nilai precision 0,94 dan recall 0,97 pada kelas non-spam, serta precision 0,97 dan recall 0,94 pada kelas spam. Confusion matrix yang diperoleh adalah [[111, 3], [7, 108]], yang menunjukkan bahwa sebagian besar pesan berhasil diklasifikasikan secara tepat pada kedua kelas. Berdasarkan hasil tersebut, Logistic Regression dapat diterapkan secara efektif untuk klasifikasi teks pendek pada pesan SMS dan sesuai digunakan sebagai sistem penyaringan spam yang berjalan langsung pada perangkat Android.
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