Multiple Sequence Alignment Untuk Mengidentifikasi Mutasi Pada Sekuen Dna Covid-19 Menggunakan Reinforcement Learning

Chofsoh, Zanuba Hilla Qudrotu (2022) Multiple Sequence Alignment Untuk Mengidentifikasi Mutasi Pada Sekuen Dna Covid-19 Menggunakan Reinforcement Learning. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

COVID-19 merupakan virus yang mampu melakukan mutasi dengan cepat dan mengakibatkan banyak varian baru COVID-19. Mutasi dari COVID-19 dapat diketahui melalui letak nukleotida dari DNA yang diproses dengan penjajaran sekuen DNA. Penjajaran sekuen DNA dengan Multiple Sequence Alignment (MSA) menjajarkan banyak sekuen, kemudian akan diperoleh mutasi baru. Multiple sequence alignment dilakukan dengan reinforcement learning yang dapat menghasilkan optimal alignment. Tahapan penjajaran dengan MSA dilakukan dengan beberapa tahapan, yaitu pre processing data untuk menghasilkan data yang baik, penjajaran sekuen-sekuen DNA COVID-19 dengan reinforcement learning, dan analisis data untuk memperoleh mutasi dari sekuen DNA COVID-19. Hasil menunjukkan bahwa pada proses pairwise alignment dengan metode reinforcement learning diperoleh sekuen yang mengalami mutasi dengan presentase kecocokan > 85%. Serta diperoleh sub sekuen lestari pada proses multiple sequence alignment antar varian sebanyak 10.8%.
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COVID-19 is a virus that is able to mutate quickly and cause many new variants of COVID-19. Mutations from COVID-19 can be identified through the location of the nucleotides of DNA processed by DNA sequence alignment. DNA sequence alignment with Multiple Sequence Alignment (MSA) aligns many sequences, then new mutations will be obtained. Multiple sequence alignment is carried out with reinforcement learning which can produce optimal alignment. The alignment steps with MSA are carried out in several stages are pre-processing data to produce the right data, aligning COVID-19 DNA sequences with reinforcement learning, and analyzing data to obtain mutations from COVID-19 DNA sequences. The results show that in the pairwise alignment process with the reinforcement learning method, sequences that experience mutations are obtained with a match percentage > 85%. And obtained sustainable sub sequences in the process of multiple sequence alignment between variants as much as 10.8%.

Item Type:	Thesis (Other)
Additional Information:	RSMa 005.1 Cho m-1 2022
Uncontrolled Keywords:	Multiple sequence alignment. reinforcement learning. q-learning. covid-19. dna sequence.
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Mathematics and Science > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	05 Jun 2026 04:44
Last Modified:	05 Jun 2026 04:44
URI:	http://repository.its.ac.id/id/eprint/133605

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