Prediksi Teks Wawancara Medis Berdasarkan Nilai Kemiripan Menggunakan Cosine Similarity

Suirta, I Kadek Ricky (2023) Prediksi Teks Wawancara Medis Berdasarkan Nilai Kemiripan Menggunakan Cosine Similarity. Project Report. [s.n.], [s.l.]. (Unpublished)

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Abstract

Konsultasi kesehatan online semakin banyak digunakan terlebih selama masa pandemi COVID19. Proses tanya jawab yang menjadi bagian dari interview dokter dan pasien sebelum dilakukan diagnosis penyakit dimungkinkan tidak terjadi timbal balik pada media online tersebut. Masyarakat pada umumnya akan menuliskan lebih dari satu pertanyaan kepada dokter terkait satu kategori penyakit secara luas. Redaksi kalimat pada teks tersebut sering tidak terstruktur dan bercampur antara pertanyaan serta pernyataan. Banyaknya jumlah penanya dengan pertanyaan yang mungkin masih memiliki kemiripan membuat data konsultasi kesehatan terlihat tidak tertata dengan rapi. Pada Tugas Akhir ini akan dilakukan tahapan menggunakan pendekatan segmentasi teks yang dapat membantu pengguna atau pengunjung media konsultasi kesehatan online tersebut dalam mencari data pertanyaan terdahulu.
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Online health consultations are increasingly being used, especially during the COVID19 pandemic. The question and answer process which is part of the doctor and patient interview before a disease diagnosis is made is possible that there will be no reciprocity in the online media. The general public will generally write more than one question to a doctor regarding one broad category of disease. Sentences in the text are often unstructured and mixed up between questions and statements. The large number of questioners with questions that may still have similarities makes the health consultation data look unorganized. This Final Project will carry out stages using a text segmentation approach that can help users or visitors to the online health consultation media in finding previous question data.

Item Type: Monograph (Project Report)
Uncontrolled Keywords: Konsultasi Kesehatan, Cosine Similarity, Segmentasi Teks,Health Consultation, Cosine Similarity, Text Segmentation
Subjects: Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines.
Divisions: Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User: I Kadek Ricky Suirta
Date Deposited: 07 Aug 2023 04:32
Last Modified: 07 Aug 2023 04:32
URI: http://repository.its.ac.id/id/eprint/103952

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