Deteksi Dini Penyakit Cacar Monyet Menggunakan Genetic Algorithm - Classification Based Association (GA - CBA)

Pratama, Qoria Yudi (2024) Deteksi Dini Penyakit Cacar Monyet Menggunakan Genetic Algorithm - Classification Based Association (GA - CBA). Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Cacar monyet atau monkey pox merupakan penyakit zoonosis yang menular dari hewan ke manusia. Cacar monyet disebabkan oleh virus Monkeypox, yang termasuk dalam keluarga virus Orthopoxvirus. Cacar monyet penyakit menular namun tingkat kematiannya tidak setinggi COVID-19 akan tetapi jika wabah ini memburuk mungkin virus cacar monyet dapat menyebabkan pandemi global berikutnya. Oleh karena itu, penting untuk melakukan pengawasan dan pencegahan yang tepat untuk mencegah penyebaran penyakit ini. Pada Tesis ini peneliti mengembangkan metode alternatif lain untuk deteksi dini penyakit cacar monyet berdasakan gejala-gejalanya dengan metode pengklasifikasi Classification Based Association (CBA) dan optimasi menggunakan Genetic Algorithm (GA). Di dalam CBA terdapat Class Association Rules (CAR) yaitu aturan asosiasi dengan konsekuennya dibatasi dengan atribut kelas atau target. CAR digunakan untuk membangun aturan pengklasifikasi model. Banyaknya CAR dipengaruhi oleh jumlah atribut pada dataset. Dengan bertambahnya jumlah aturan, risiko redundansi meningkat. Peneliti menggunakan GA untuk mengatasi keterbatasan CBA dengan mengoptimalkan aturan-aturan dan mengurangi redundansi serta menghasilkan aturan yang lebih akurat guna meningkatkan tingkat akurasi CBA. Berdasarkan hasil percobaan pada penelitian ini, GA mampu meningkatkan performa CBA biasa dengan diperoleh akurasi 71,29%, akurasi meningkat 2% dibandingkan dengan CBA biasa.
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Monkeypox is a zoonotic disease that is transmitted from animals to humans. Monkeypox is caused by the Monkeypox virus, which belongs to the Orthopoxvirus family of viruses. Monkeypox is an infectious disease but the mortality rate is not as high as COVID-19, but if this outbreak worsens, the monkeypox virus may cause the next global pandemic. Therefore, it is important to carry out proper surveillance and prevention to prevent the spread of this disease. In this thesis, author develop another alternative method for early detection of monkeypox disease based on its symptoms using the Classification Based Association (CBA) classifier method and optimization using the Genetic Algorithm (GA). In CBA there are Class Association Rules (CAR), namely association rules with consequences limited by class or target attributes. CAR is used to build model classifier rules. The number of CARs is influenced by the number of attributes in the dataset. With the increasing number of rules, the risk of redundancy increases. Author use GA to overcome the limitations of CBA by optimizing the rules and reducing redundancy and producing more accurate rules to increase the accuracy level of CBA. Based on the experimental results in this study, GA is able to improve the performance of ordinary CBA by obtaining an accuracy of 71.29%, the accuracy increased by 2% compared to ordinary CBA.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Cacar Monyet, Klasifikasi, Klasifikasi Assosiatif, CBA, GA, Monkey Pox, Classification, Associative Classification, CBA, GA
Subjects:	Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. R Medicine > RB Pathology
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44101-(S2) Master Thesis
Depositing User:	Qoria Yudi Pratama
Date Deposited:	08 Aug 2024 04:00
Last Modified:	08 Aug 2024 04:00
URI:	http://repository.its.ac.id/id/eprint/113702

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