Penerapan Metode Pembelajaran Mesin Berbasis Fuzzy Logic untuk Prediksi Kualitas Layanan Jaringan IoT (Internet of Things)
DOI:
https://doi.org/10.32493/informatika.v8i2.30572Keywords:
Jaringan IoT, Logika fuzzy, Optimasi parameter, Pembelajaran mesinAbstract
Masalah yang dihadapi dalam pengelolaan kualitas layanan jaringan Internet of Things (IoT) adalah ketidakpastian dan kompleksitas data yang ambigu. Dalam penelitian ini, kami bertujuan untuk menerapkan metode pembelajaran mesin berbasis logika fuzzy guna memprediksi kualitas layanan pada jaringan IoT. Metode pembelajaran mesin berbasis logika fuzzy digunakan dengan melatih model menggunakan algoritma fuzzy c-means dan melakukan optimasi parameter melalui validasi silang. Kami melakukan evaluasi kinerja model dengan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian kami menunjukkan bahwa metode pembelajaran mesin berbasis logika fuzzy mampu mengatasi tantangan ketidakpastian dan kompleksitas data pada jaringan IoT. Model yang dikembangkan mampu memprediksi kualitas layanan dengan tingkat akurasi yang tinggi. Atribut masukan seperti kecepatan transfer data, latensi, kestabilan koneksi, dan kehilangan paket dapat diklasifikasikan ke dalam kategori kualitas layanan yang sesuai. Evaluasi kinerja model juga menunjukkan tingkat akurasi, presisi, recall, dan F1-score yang seimbang. Penelitian ini memiliki implikasi penting dalam pengembangan dan pengelolaan jaringan IoT. Metode pembelajaran mesin berbasis logika fuzzy dapat memberikan pemahaman yang lebih baik, mendukung pengambilan keputusan, dan meningkatkan kualitas layanan pada jaringan IoT. Penelitian selanjutnya dapat melibatkan pengembangan model fuzzy logic yang lebih kompleks, penggunaan dataset yang lebih besar, serta mempertimbangkan faktor-faktor lain yang mempengaruhi kualitas layanan pada jaringan IoT.References
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