Astagina, Shania Eriadhani (2021) Preservasi Struktur XML dari Dokumen Medis Berstandar dalam Big Data. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Salah satu standar dokumen medis elektronik yang banyak digunakan di dunia adalah dokumen CDA® milik HL7. CDA® merupakan standar dokumen medis dengan struktur dokumen XML. Tugas Akhir ini mengangkat permasalahan penyimpanan dokumen medis berstandar khususnya dokumen CDA® dengan menggunakan Hadoop. Hadoop menyimpan dokumen dengan memotong dokumen dalam beberapa partisi. Hal tersebut dapat menimbulkan risiko terjadinya perpotongan dokumen yang tidak tepat sehingga dapat menghilangkan informasi dokumen.
Tugas Akhir ini mengimplementasikan metode penyelesaian masalah tersebut dengan menggunakan MapReduce dalam tiga tahapan yaitu, analisis batasan penting dokumen medis, identifikasi bagian dokumen yang mengalami konflik, dan tahap resolusi konflik.
Implementasi metode berhasil dilakukan dengan sempurna sampai pada tahap kedua, tetapi pada tahap ketiga mengalami penurunan keberhasilan seiring bertambahnya dokumen dengan pasangan ganda atau tanpa pasangan serta mengecilnya ukuran blok data HDFS. Persentase keberhasilan tertinggi pada blok data ukuran 128mb adalah 100% dengan data masukan 500MB – 2GB, dan persentase terendah 86.4% dengan data masukan 8GB. Sedangkan blok data dengan persentase keberhasilan tertinggi ada pada blok data 256mb dan terendah pada 64mb.
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Most of electronic medical documents use a document standard that have complex structures. These documents are used in medical organizations and produces daily in large quantity, thus needs big data technology. One of the most used standards for medical documents is HL7’s CDA® which has XML structures. This thesis raises the issue of storing standardized documents that use CDA® documents in Hadoop. Hadoop stores documents in partitions, so can pose a risk of losing the documents information if the document is partitioned without minding the document’s constraint. This thesis is discussed the implementation methods to solve the problem by using MapReduce with three phases. First, CDA®
document constraint analysis phase. Second, the conflict identification phase. Lastly, conflict resolution phase. The result is the methods are successfully implemented until the second phase but experience a decrease in the success percentage as the documents that have more than one pair or have no pair increase and the HDFS block size decrease. The highest success percentage in 128mb block size is 100% with 500MB – 2GB size of input document, and the lowest is 86.4% with 8GB size of input document. HDFS block that have the highest percentage of success is at 256mb and the lowest is 64mb.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | CDA®, MapReduce, XML, Hadoop |
| Subjects: | T Technology > T Technology (General) > T57.5 Data Processing |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering |
| Depositing User: | Shania Eriadhani Astagina |
| Date Deposited: | 14 Aug 2021 09:29 |
| Last Modified: | 15 Sep 2024 01:29 |
| URI: | http://repository.its.ac.id/id/eprint/86464 |
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