PREDIKSI KADAR GULA DARAH BERBASIS SPEKTROSKOPI MENGGUNAKAN MACHINE LEARNING

Riandra Putra Pratama, . (2024) PREDIKSI KADAR GULA DARAH BERBASIS SPEKTROSKOPI MENGGUNAKAN MACHINE LEARNING. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

In general, the tool used to measure blood sugar levels is a blood sample-based glucometer to be tested in a chemical sensor with the enzyme glucose oxidase as the activeingredient. Blood sugar sampling on a glucometer uses an invasive method using a needle to be injected into the fingertip so that blood is removed which will be the sample for checking. When checking blood sugar, it often causes pain because a needle is inserted into the fingertip. The pain can cause trauma and discomfort, therefore it is necessary to develop a non-invasive blood sugar level monitoring tool (does not hurt the body). The Non-Invasive Method is carried out by measuring glucose levels in the body using light wavelengths placed on the fingers of the hand so that it can detect glucose molecules in the blood. This study aims to implement a non-invasive method of checking blood sugar using wavelengths taken using AS7265X Sensor spectroscopy data so that it can predict blood sugar levels. To be able to predict blood sugar levels, the method used in this study is to develop a Machine Learning method using the Random Forest Regression algorithm. This study is expected to provide another method in checking blood sugar that is able to effectively and accurately predict blood sugar levels in the body and does not cause pain as occurs in conventional blood sugar measurement.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 2010511051] [Pembimbing 1: Ridwan Raafiudin] [Pembimbing 2: Nindy Irzavika] [Penguji 1: Ruth Mariana Bunga Wadu] [Penguji 2: Nurul Afifah Arifuddin]
Uncontrolled Keywords: Blood Sugar Level, Prediction, Machine Learning, Sensor AS726X
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: RIANDRA PUTRA PRATAMA
Date Deposited: 30 Aug 2024 03:46
Last Modified: 30 Aug 2024 03:46
URI: http://repository.upnvj.ac.id/id/eprint/31684

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