PENERAPAN DATA MINING UNTUK DETEKSI GEJALA COVID-19 MENGGUNAKAN METODE DECISION TREE ALGORITMA C4.5

HAMAMI, MUAMMAR (2022) PENERAPAN DATA MINING UNTUK DETEKSI GEJALA COVID-19 MENGGUNAKAN METODE DECISION TREE ALGORITMA C4.5. Undergraduate thesis, Universitas Stikubank.

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Abstract

Covid-19 merupakan virus yang masih sulit untuk di deteksi, karena memiliki gejala yang mirip dengan penyakit lain. Sebagian besar orang yang terpapar virus ini akan mengalami batuk, demam, sesak nafas, kehilangan rasa dan bau. Ada 2 test yang dapat dilakukan untuk dapat mendiagnosa infeksi virus covid-19 yaitu rapid test dan swab test. Awamnya mansyarakat tentang gejala covid-19 dan mahalnya biaya tes covid-19 menjadi permasalahan tersendiri untuk dapat mendiagnosa virus covid-19. Penelitian ini bertujuan untuk membantu masyarakat agar dapat melakukan deteksi mandiri. Dalam melakukan deteksi gejala covid-19 dibutuhkan perhitungan yang akurat. Teknik penggalian pola dalam data yang berukuran besar menjadi kumpulan-kumpulan data yang lebih kecil untuk membangun pohon keputusan menggunakan algoritma C4.5. Pada algoritma C4.5 proses pengelompokan data dimulai dari akar ke daun secara berurutan. Proses ini akan terus berlanjut hingga mencapai node yang tidak bisa dibagi lagi. Dari hasil penelitian pada Deteksi Gejala Covid-19 menggunakan metode Decision Tree Algoritma C4.5 disimpulkan bahwa telah berhasil dibangun sebuah sistem yang dapat digunakan untuk melakukan deteksi awal terhadap gejala yang ditimbulkan oleh covid-19, pada akhirnya sistem akan melakukan deteksi menggunakan rule atau aturan pohon keputusan yang telah terbentuk dan mengelompokkannya menjadi 2 status yaitu Covid-19 dan Bukan Covid-19. Covid-19 is a virus that is still difficult to detect, because it has symptoms similar to other diseases. Most people who are exposed to this virus will experience cough, fever, shortness of breath, loss of taste and smell. There are 2 tests that can be done to be able to diagnose covid-19 virus infection, namely rapid tests and swab tests. The general public about the symptoms of covid-19 and the high cost of covid-19 tests are separate problems to be able to diagnose the covid-19 virus. This study aims to help the community to be able to carry out independent detection. In detecting COVID-19 symptoms, accurate calculations are needed. The technique of extracting patterns in large data into smaller data sets to build a decision tree using the C4.5 algorithm. In the C4.5 algorithm, the data grouping process starts from the roots to the leaves sequentially. This process will continue until it reaches a node that cannot be divided anymore. From the results of research on the Detection of Covid-19 Symptoms using the Decision Tree Algorithm C4.5 method, it is concluded that a system has been successfully built that can be used to perform early detection of symptoms caused by Covid-19, in the end the system will detect using rules or decision tree rules that have been formed and group them into 2 statuses, namely Covid-19 and Not Covid-19.

Item Type:	Thesis (Undergraduate)
Additional Information:	SKR.I.05.01.2176 NIM.17.01.53.0190
Uncontrolled Keywords:	Covid-19, Deteksi covid-19, Algoritma C4.5, Pohon Keputusan. Covid-19, Covid-19 detection, C4.5 Algorithm, Decision Tree.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Institution:	Fakultas Teknologi Informasi > Program Studi Teknik Informatika
Depositing User:	Teteh Hayati
Date Deposited:	27 Sep 2022 01:34
Last Modified:	27 Sep 2022 01:34
URI:	https://eprints.unisbank.ac.id/id/eprint/8715

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