Aplikasi Discrete Wavelets Transform pada Analisis Regresi Spektrum Tumpang Tindih Senyawa Parasetamol dan Kafein

Mulyaningtias, Nadia (2021) Aplikasi Discrete Wavelets Transform pada Analisis Regresi Spektrum Tumpang Tindih Senyawa Parasetamol dan Kafein. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Obat umumnya berisi kombinasi dari beberapa senyawa aktif. Parasetamol sering digunakan sebagai obat analgesik dan anti piretik. Kafein adalah stimulan sistem saraf pusat (SSP). Dalam pemasaran obat sakit kepala di masyarakat, pemeriksaan mutu obat diperlukan untuk menjamin bahwa obat menggandung bahan aktif dengan mutu dan jumlah sesuai dengan kandungan yang tertera pada label obat. Sehingga diperlukan metode yang efisien tanpa pemisahan, dengan bantuan spektrofotometer UV-Vis, metode Discrete Wavelets Transform dan analisis multikomponen. Sebanyak 25 larutan training set disiapkan dan dianalisis absorbansinya menggunakan metode Multiple Linier Regression, metode Support Vector Regression (SVR), metode Partial Least Square (PLS) Regression, metode AdaBoost Regression. Model yang didapat divalidasi dengan tes data sebelum diaplikasikan pada obat sakit kepala. Penentuan kadar obat yang sesuai dengan kadar dalam label obat, yaitu pada metode Support Vector Regression dimana rata-rata kadar obat prediksi parasetamol sebesar 512,5473 mg dan pada kafein sebesar 67,1091 mg. Sehingga metode berhasil digunakan untuk menetapkan kadar dalam tablet obat sakit kepala.
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Medicine generally contain active ingredients. Paracetamol is often used as an analgesic and anti-pyretic medicine. Caffeine is a central nervous system (CNS) stimulant. In marketing headache medicine in the community, quality inspection of medicine is needed to ensure that the medicine contains active ingredients with the quality and quantity according to the content stated on the medicine label. So an efficient method without separation is needed, with the help of a UV-Vis spectrophotometer, the Discrete Wavelets Transform method and multivariate analysis. A total of 25 training set solutions were prepared and their absorbance analyzed using the Multiple Linear Regression method, the Support Vector Regression (SVR) method, the Partial Least Square (PLS) Regression method, the AdaBoost Regression method. The model obtained was validated by testing the data before it was applied to headache medicine. Determination of medicine levels in accordance with the levels on the medicine label, namely the Support Vector Regression method where the average level of the predicted medicine parasetamol is 512.5473 mg and the caffeine is 67.1091 mg. So that the method was successfully used to determine the levels in headache medicine tablets.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Parasetamol, Kafein, Spektrofotometri UV-Vis, Discrete Wavelets Transform, regresi multivariat.Paracetamol, Caffeine, Spectrofotometry UV-Vis, Discrete Wavelets Transform, multivariate regression.
Subjects:	Q Science > QA Mathematics > QA278 Cluster Analysis. Multivariate analysis. Correspondence analysis (Statistics) Q Science > QD Chemistry > QD75.2 Chemistry, Analytic T Technology > T Technology (General) > T57.8 Nonlinear programming. Support vector machine. Wavelets. Hidden Markov models.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Chemistry > 47201-(S1) Undergraduate Thesis
Depositing User:	Nadia Mulyaningtias
Date Deposited:	23 Aug 2021 11:53
Last Modified:	23 Aug 2021 11:53
URI:	http://repository.its.ac.id/id/eprint/90046

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