Analisa Klasifikasi Penyakit Diabetes dengan Algoritma Neural Network
DOI:
https://doi.org/10.32877/bt.v6i3.1161
Keywords:
Diabetes, Fine tunning , Hiden layer, Minmax.scaler, Neural Network
Abstract
Metode yang populer dan efektif untuk mengidentifikasi dan klasifikasi diabetes adalah algoritma deep learning untuk klasifikasi dataset diabetes. Algoritma deep learning, terutama jaringan saraf tiruan juga dikenal sebagai neural networks telah terbukti sangat efektif dalam menangani tugas klasifikasi data medis, seperti diabetes. Dalam penelitian sebelumnya, algoritma neural network digunakan untuk mengklasifikasikan penyakit diabetes , tetapi nilai akurasinya masih di bawah 80,5%. Karena nilai akurasi masih kurang maksimal, penelitian ini bertujuan untuk meningkatkannya. Penggunaan metode pemrosesan yang lebih akurat, fine tuning hyperparameter, untuk memastikan data sudah normal pada setiap fitur yaitu dengan metode normalisasi standard, kemudian menambahkan hiden layer sebanyak 2 layer dengan harapan mempelajari klasifikas yang tidak bisa dipisahkan secara linier. Dalam penelitian ini, beberapa langkah pembaharuan dilakukan selain besaran hiden layer juga besaran test size. Pembaruan ini bertujuan untuk meningkatkan akurasi yang lebih besar, serta hasil yang lebih baik untuk presisi, recall dan F1. Artikel ini menggunakan data umum atau sekunder dari laman Kaggle. Penelitian ini dilakukan untuk meningkatkan pengetahuan tentang cara mencegah diabetes. Gejala penyakit ini termasuk kadar gula sewaktu lebih dari 200 mg/dl dan kadar gula puasa lebih dari 126.mg/dl, antara tahun 1998 dan 2014, Badan Kesehatan Dunia melaporkan peningkatan dramatis dalam jumlah kasus diabetes di seluruh dunia, dari 108 juta menjadi 422 juta.
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