BibTex Citation Data :
@article{JMASIF10134, author = {Amalia Yanti and Sukmawati Endah}, title = {Aplikasi Deteksi Dini Gangguan Sistem Pernafasan Menggunakan Metode Learning Vector Quantization (LVQ) Berbasis Web}, journal = {JURNAL MASYARAKAT INFORMATIKA}, volume = {7}, number = {1}, year = {2017}, keywords = {Aplikasi deteksi dini gangguan sistem pernafasan, Jaringan syaraf tiruan (JST), Learning vector quantization (LVQ),K-Fold Cross Validation}, abstract = { Gangguan sistem pernafasan merupakan gangguan yang menjadi masalah besar di dunia khususnya di Indonesia. Banyaknya jumlah penderita yang ada di Indonesia tiap tahunnya terus bertambah. Salah satu penyebabnya adalah kurangnya kesadaran masyarakat akan gejala yang dialami sehingga menyebabkan keterlambaatan dalam pengobatan dan susah untuk disembuhkan. Melihat kondisi tersebut, perlu adanya cara untuk mendeteksi dini adanya gangguan sistem pernafasan melalui gejala yang dialami, sehingga dari gejala tersebut dapat dilakukan sebuah klasifikasi jenis gangguan sistem pernafasan yang dialami. Learning vector quantization (LVQ) adalah metode dalam jaringan syaraf tiruan (JST) yang paling banyak digunakan untuk proses klasifikasi sehingga sangat cocok untuk kasus tersebut. Penelitian ini menghasilkan aplikasi deteksi dini gangguan sistem pernafasan yang dapat mengklasifikasikan gangguan sistem pernafasan apa yang dialami pengguna dengan input berupa data gejala yang dialami. Data yang digunakan pada penelitian ini sebanyak 80 data, 72 data digunakan untuk pelatihan, dan 8 data untuk pengujian. Pengujian dilakukan dengan menggunakan K-Fold Cross Validation dengan nilai k = 10. Aplikasi ini menggunakan pilihan arsitektur jaringan terbaik berdasarkan hasil pengujian, yaitu dengan inisialisasi bobot awal dari dataset yang diambil secara random, learning rate (α) 0.01, error minimum (eps) 0.01 dan maksimum epoch sebanyak 100 epoch dengan tingkat akurasi sebesar 80%. }, issn = {2777-0648}, pages = {55--65} doi = {10.14710/jmasif.7.1.10134}, url = {https://ejournal.undip.ac.id/index.php/jmasif/article/view/10134} }
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