1Dian Nuswantoro University, Indonesia
2Universitas Dian Nuswantoro, Indonesia
BibTex Citation Data :
@article{JMASIF42222, author = {Christy Atika Sari and Eko Hari Rachmawanto}, title = {Fitur Esktraksi LBP dan Naive Bayes dalam Klasifikasi Jenis Pepaya Berdasarkan Citra Daun}, journal = {Jurnal Masyarakat Informatika}, volume = {12}, number = {2}, year = {2021}, keywords = {Naïve Bayes, Local Binary Pattern, Klasifikasi Daun, Ekstraksi Fitur}, abstract = { Tanaman merupakan bagian terpenting dalam kehidupan makhluk hidup sebagai oksigen untuk bernafas, selain itu juga digunakan sumber makanan, bahan bakar, obat-obatan dan masih banyak lagi manfaatnya. Salah satunya tanaman buah pepaya, bisa digunakan untuk bahan makanan maupun obat-obatan. Tanaman buah pepaya ini memiliki banyak jenis dan bisa diklasifikasikan berdasarkan bentuk daunnya. Jenis daun buah papaya yang digunakan dalam penelitian ini, yaitu : daun buah pepaya Sumatera, daun buah pepaya California, daun buah pepaya Hawai, daun buah pepaya cibinong dan daun buah pepaya Bangkok. Jumlah dataset yang digunakan adalah 150 citra dan akan dibagi menjadi 5 kelas yang terdiri dari 25 data training dan 5 data testing masing-masing kelas. Proses klasifikasi ini menggunakan metode Local Binary Pattern untuk ektraksi fitur dan metode Naïve Bayes Classifier sebagai metode klasifikasinya. Metode Local Binary Pattern operator sederhana dan efisien untuk menggambarkan pola gambar local dan mendapatkan hasil yang baik dalam tekstur pengambilan gambar. Sedangkan metode Naïve Bayes Classifier adalah metode yang paling sederhana dengan menggunakan peluang yang ada, dimana tempatnya mengasumsikan bahwa setiap variabel adalah independensi. Berdasarkan hasil pengujian yang dilakukan, penggunaan Naïve Bayes Classifier ditambah dengan ekstraksi fitur Local Binary Pattern didapatkan nilai akurasi 96% pada percobaan pertama dan 93% pada percobaan kedua. }, issn = {2777-0648}, pages = {102--113} doi = {10.14710/jmasif.12.2.42222}, url = {https://ejournal.undip.ac.id/index.php/jmasif/article/view/42222} }
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