Herdi Rizky Pratama, - (2024) DIAGNOSA AWAL PENYAKIT IKAN KAKAP PUTIH MENGGUNAKAN ALGORITMA NAIVE BAYES BERBASIS WEB. S1 thesis, Universitas Pendidikan Indonesia.
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Abstract
Ikan kakap putih merupakan salah satu jenis komoditas ikan pangan yang banyak diminati oleh masyarakat terutama di daerah Asia. Disamping itu, pengembangbiakan ikan kakap putih masih memiliki beberapa tantangan contohnya seperti serangan penyakit. Untuk dari itu tujuan dari penelitian ini adalah memudahkan para peternak ikan kakap putih dengan bantuan teknologi, yaitu pembuatan website diagnosa penyakit ikan kakap. Sebuah website yang dibuat untuk mendiagnosa penyakit ikan kakap putih berdasarkan ciri - ciri gejalanya. Website ini dibangun dengan menggabungkan model machine learning algoritma gaussian naive bayes dan web development yang didukung oleh flask. Model machine learning dibangun dengan arsitektur bahasa pemrograman python, serta web development menggunakan HTML dan CSS. Validasi model diagnosa dilakukan dengan memvalidasi beberapa pertanyaan seputar penyakit ikan ke 8 orang pakar menggunakan angket google forms. Hasil evaluasi model machine learning ini mendapatkan nilai 80% pada akurasi, 100% pada recall, dan 100% pada presisi. ----- White snapper is one of the types of fish commodities highly sought after by the community, especially in the Asian region. Additionally, the cultivation of white snapper still faces several challenges, such as disease outbreaks. Therefore, the aim of this research is to assist white snapper fish farmers with the help of technology, specifically by creating a website for diagnosing diseases in white snapper fish. The website is designed to diagnose diseases in white snapper fish based on their characteristic symptoms. It is constructed by combining a machine learning model using the Gaussian Naive Bayes algorithm and web development supported by Flask. The machine learning model is built with the Python programming language, while web development uses HTML and CSS. The diagnostic model is validated by surveying 8 experts with a Google Forms questionnaire regarding fish diseases. The evaluation results of this machine learning model show an accuracy of 80%, recall of 100%, and precision of 100%.
| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | https://scholar.google.com/citations?hl=id&user=evCiBDQAAAAJ&scilu=&scisig=AM0yFCkAAAAAZauCObiqEEWJbCNvHe1D7WUrfpM&gmla=AH70aAVrTbR-H0IvHbhbIOBtByhqPTf0SXy_JvX2vk5HmaKdVoZRKINc0QpdOZGkCYHdrV4MVsgTNQxQjpPotfVMHFIhQs1YA6D828APvnn-bCEaVe-akO2_Wi8&sciund=17429551385320116339 ID SINTA Dosen Pembimbing HAFIYYAN PUTRA PRATAMA : 6681148 ENDAH SETYOWATI : 6681149 |
| Uncontrolled Keywords: | Kakap Putih, Diagnosa, Naive Bayes, Machine Learning, Website |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | UPI Kampus Purwakarta > S1 Sistem Telekomunikasi |
| Depositing User: | Herdi Rizky Pratama |
| Date Deposited: | 29 Apr 2024 04:59 |
| Last Modified: | 29 Apr 2024 04:59 |
| URI: | http://repository.upi.edu/id/eprint/114538 |
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