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dc.contributor.advisorAlatas, Husin
dc.contributor.advisorIrzaman
dc.contributor.authorMeilani, Nida Mustika
dc.date.accessioned2022-01-24T04:29:07Z
dc.date.available2022-01-24T04:29:07Z
dc.date.issued2022
dc.identifier.urihttp://repository.ipb.ac.id/handle/123456789/110771
dc.description.abstractPengukuran kadar hemoglobin darah sampai saat ini kebanyakan masih menggunakan metode invasif tetapi sudah banyak peneliti yang mengembangkan pengukuran secara noninvasif. Dari kedua metode ini pengukuran kadar Hb secara noninvasif dianggap lebih akurat, data lebih cepat diperoleh, serta data yang diperoleh kontinyu. Sehingga penelitian ini bertujuan menguji metode Artificial Neural Network (ANN) untuk menduga kadar hemoglobin darah tanpa menggunakan reagen serta menguji performa metode pendekatan pola dengan variabel perhitungan akurasi berupa Root Mean Square Error (RMSE), sensitivitas, spesifisitas, diagnosis akurasi, Number Needed to Diagnose (NND), serta menentukan korelasi antara serapan panjang gelombang kadar hemoglobin darah dengan akurasi pendugaan. Hasil penelitian ini diperoleh kandidat panjang gelombang dengan rentang 300 nm-500 nm dan nilai akurasinya mencapai 90%. Sedangkan untuk nilai pearson terbaik terdapat pada panjang gelombang 590 nm sebesar 0,52id
dc.description.abstractThe measurement of blood hemoglobin levels today is mostly using invasive methods, but many researchers have developed non-invasive measurements. From these two methods, non-invasive measurement of Hb levels is considered more accurate, the data obtained faster and continuously. With that result this study aim to test method Artificial Neural Network (ANN) to estimate blood hemoglobin levels without using reagents and to test the performance of the pattern approach method with the calculation variables of accuracy such as Root Mean Square Error (RMSE), sensitivity, specificity, diagnosis accuracy, Number Needed to Diagnose (NND), and to determine correlation between absorption wavelength blood hemoglobin level with estimation accuracy. From this research obtained the result that the percentage of candidates with wavelengths a range of 300 nm-500 nm and its accuracy value can reach until 90%. While for the best Pearson value is found at a wavelength 590 nm of 0.52.id
dc.language.isoidid
dc.publisherIPB Universityid
dc.titleInferensi Hasil Karakterisasi UV-Vis Kadar Hemoglobin Darah Manusia Menggunakan Artificial Neural Network (ANN)id
dc.title.alternativeInference of UV-VIS Characterization Results of Human Blood Hemoglobin Levels using Artificial Neural Network (ANN)id
dc.typeUndergraduate Thesisid
dc.subject.keywordArtificial Neural Network (ANN)id
dc.subject.keywordHemoglobinid
dc.subject.keywordNoninvasifid
dc.subject.keywordNon invasiveid


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