IMPLEMENTASI DEEP LEARNING UNTUK DETEKSI JENIS OBAT MENGGUNAKAN ALGORITMA CNN BERBASIS WEBSITE
DOI:
https://doi.org/10.31000/jika.v7i4.9751Abstrak
Obat merujuk pada substansi atau campuran bahan termasuk produk biologi yang digunakan untuk mempengaruhi atau menyelediki sistem fisiologi dengan tujuan mendiagnosis, mencegah, menyembuhkan, memulihkan, meningkatkan kesehatan. Terdapat beberapa permasalahan terkait semakin maraknya merk dan jenis obat yang beredar tersebut, dimana tiap merk memiliki bahan dasar dan indikasi yang sama ataupun berbeda. Dalam lingkungan farmasi yang berkembang pesat, kebutuhan untuk automatisasi dalam pengelolaan obat sangat penting. Ini disebabkan oleh berbagai faktor, termasuk meningkatnya volume obat, kompleksitas produk farmasi, serta tujuan untuk meningkatkan efisiensi dan akurasi dalam manajemen stok obat. Deep learning memainkan peran kunci dalam memenuhi kebutuhan tersebut, pada penelitian ini dibuat sistem dengan tujuan untuk mengklasifikasi jenis obat secara otomatis. Deep Learning merupakan area pembelajaran mesin yang menggunakan jaringan syaraf tiruan untuk menyelesaikan masalah dengan kumpulan data besar. Algoritma yang digunakan dalam sistem deep learning yaitu Convulutional Neural Network (CNN). Proses klasifikasi jenis bentuk sediaan obat tablet dan kapsul dengan tahap pengumpulan data, preprocessing data membagi data dengan jumlah data train 70% dan data test 30% mendapatkan hasil tingkat accuracy tertinggi yaitu 99% dan val accuracy 99%, serta memperoleh hasil akurasi model dengan menggunakan model f1- Score tertinggi yaitu dengan skor 100%. Hal tersebut menjelaskan bahwa algoritma Convolutuinal Neural Network (CNN) dipengaruhi oleh data training yang jumlahnya besar, semakin besar data yang digunakan maka semakin tinggi juga akurasi yang didapatkan.Referensi
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