Analisis Gray Level Difference Method Dan Metode Naive Bayes Mengindentifikasi Penyakit Lidah Manusia

Fahmi, Riekal (2016) Analisis Gray Level Difference Method Dan Metode Naive Bayes Mengindentifikasi Penyakit Lidah Manusia. Other thesis, Universitas Komputer Indonesia.

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Abstract

Lidah adalah kumpulan otot rangka pada bagian lantai mulut yang dapat membantu pencernaan makanan dengan mengunyah dan menelan. Lidah dikenal sebagai indera pengecap yang banyak memiliki struktur tunas pengecap. Jika lidah ini tak berfungsi, dengan sendirinya akan berpengaruh terhadap rasa makanan ataupun selera makan kita. Tidak berfungsinya lidah sebagaimana mestinya disebabkan adanya kelainan atau penyakit. Salah satu cara agar dapat membedakan ciri tersebut ialah dengan cara mengenali perbedaan tekstur pada citra Terdapat beberapa metode untuk memperoleh ciri-ciri tekstur dalam suatu citra, Salah satu metode untuk memperoleh ciri-ciri citra tekstur adalah Gray level difference atau bisa di singkat (GLDM). Ciri-ciri tekstur yang didapat dari metode ini diantaranya adalah kontras, Angular Singular Moment, energi, invers different moment dan Mean. Dari hasil ciri-ciri tersebut kemudian digunakan untuk klasifikasi dengan menggunakan Naïve Bayes yang menentukan hasil klasifikasi berdasarkan nilai probabilitas terbesar. Objek yang diuji adalah citra jenis Penyakit lidah manusia. Dari penelitian yang telah dilakukan, dapat ditarik kesimpulan sebagai berikut : naïve bayes dapat melakukan klasifikasi citra berdasarkan tekstur yang diekstraksi dengan metode gray level difference moment. Dikarenakan data hasil ekstraksi ciri adalah berupa data continue, atau biasa disebut data nominal, sehingga saat proses klasifikasi data hasil ekstraksi ciri tersebut dapat langsung digunakan sebagai inputan dalam klasifikasi naïve bayes. Berdasarkan hasil pengujian, kesimpulan yang didapatkan adalah naïve bayes dapat mengklasifikasi citra dengan baik, dikarenakan data hasil ekstraksi ciri tekstur penyakit dengan metode Gray level difference moment memiliki interval jarak yang berjauhan antar kelasnya. Sehingga klasifikasi naïve bayes dapat berjalan dengan baik saat melakukan klasifikasi. Sehingga didapatkan hasil nilai ke akuratan 85%.

The tongue is a collection of skeletal muscle on the floor of the mouth that can help the digestion of food by chewing and swallowing. Aloe is known as the sense of taste buds that have more structure taster. If the tongue is not functioning, by itself will affect the taste of the food or our appetite. Malfunction of the tongue as it should be due to abnormalities or disease. One way to differentiate these traits is a way to recognize the difference of texture in the image are several methods to obtain the characteristics of texture in an image, one method for obtaining the characteristics of image texture is Gray level difference or can be short (GLDM). The characteristics of texture obtained from this method include contrast, Angular Singular Moment, energy, inverse different moment and Mean. From the results of these characteristics are then used to using Naïve Bayes classification that determines the classification results based on the greatest probability. The object being tested is the image of the kind of human tongue disease. From the research that has been done, it can be deduced as follows: naïve Bayes can perform image classification based on the texture extracted by the method of gray level difference the moment. Due to the feature extraction result data is in the form of data continue, or so-called nominal data, so that the process of data classification feature extraction results can be directly used as input in the naïve Bayes classification. based on test results, the conclusions obtained are naïve Bayes can classify the image well, because data from the texture feature extraction method illness Gray level difference the moment has a remote distance interval between class. So naïve Bayes classification can work well when performing classification. And then you will receive accuracy score 85%.

Item Type: Thesis (Other)
Additional Information: Migrasi dari GDL. Identifier: jbptunikompp-gdl-riekalfahm-34835. Folder: S1-Final_Project / Fakultas_Teknik_dan_Ilmu_Komputer / Teknik_Informatika / 2016
Uncontrolled Keywords: tekstur citra, ekstraksi ciri, gray level difference method, gldm, Naïve bayesThe tongue is a collection of skeletal muscle on the floor of the mouth that can help the digestion of food by chewing and swallowing. Aloe is known as the sense of taste buds that have more structure taster. If the tongue is not functioning, by itself will affect the taste of the food or our appetite. Malfunction of the tongue as it should be due to abnormalities or disease. One way to differentiate these traits is a way to recognize the difference of texture in the image are several methods to obtain the characteristics of texture in an image, one method for obtaining the characteristics of image texture is Gray level difference or can be short (GLDM). The characteristics of texture obtained from this method include contrast, Angular Singular Moment, energy, inverse different moment and Mean. From the results of these characteristics are then used to using Naïve Bayes classification that determines the classification results based on the greatest probability. The object being tested is the image of the kind of human tongue disease. From the research that has been done, it can be deduced as follows: naïve Bayes can perform image classification based on the texture extracted by the method of gray level difference the moment. Due to the feature extraction result data is in the form of data continue, or so-called nominal data, so that the process of data classification feature extraction results can be directly used as input in the naïve Bayes classification. based on test results, the conclusions obtained are naïve Bayes can classify the image well, because data from the texture feature extraction method illness Gray level difference the moment has a remote distance interval between class. So naïve Bayes classification can work well when performing classification. And then you will receive accuracy score 85%. Keywords: texture images, feature extraction, gray level difference method, GLDM, Naïve Bayes
Divisions: S1 Skripsi > Teknik Informatika
Depositing User: Robot Migration
Date Deposited: 05 Nov 2026 04:50
Last Modified: 05 Nov 2026 04:50
URI: https://elibrary.unikom.ac.id/id/eprint/22746

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