Santoso, Aldy Budi (2020) IMPLEMENTASI METODE GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) UNTUK IDENTIFIKASI JENIS DAUN TUMBUHAN OBAT. Undergraduate thesis, Universitas Stikubank.
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Abstract
ndonesia merupakan negara tropis yang memiliki jutaan jenis flora. Beberapa jenis flora seperti bidara, kelor, katuk , mint dan pecut kuda dapat dijadikan obat alternatif dari penggunaan obat-obatan kimia. Namun dewasa ini, kurangnya pengetahuan masyarakat modern dalam mengenali jenis dan khasiat tanaman tersebut membuat masyarakat lebih memilih menggunakan obat kimia dari pada obat herbal yang alami. Dari persoalan tersebut dibuat penelitian untuk mengidentifikasi jenis tanaman obat secara otomatis. Dalam penelitian ini, yang menjadi tantangan adalah bentuk dari ke 5 daun tanaman obat diatas hampir mirip satu sama lain, namun memiliki tekstur atau ciri yang agak berbeda. Maka dari itu digunakan fitur ekstraksi GLCM yang dapat menghasilkan fitur ciri berupa nilai Kontras, Homogenitas, Energi dan Korelasi. Fitur tersebut kemudian digunakan untuk pengenalan tanaman obat menggunakan algoritma KNN yang dapat melakukan klasifikasi secara cepat dan efisien. Dalam penelitian tersebut mampu menghasilkan akurasi pengenalan yang cukup baik mencapai 82%. Indonesia is a tropical country that has millions of types of flora. Some types of flora, such as bidara, moringa, katuk, mint and pecut kuda, can be used as alternative medicines from the use of chemical drugs. But today, the lack of knowledge of modern society in recognizing the types and properties of these plants makes people prefer to use chemical drugs rather than natural herbal medicines. From this problem, research was made to identify the types of medicinal plants automatically. In this study, the challenge is the shape of the 5 leaves of the medicinal plants above is almost similar to each other, but has a somewhat different texture or characteristics. Therefore the GLCM extraction feature is used which can produce features such as Contrast, Homogeneity, Energy and Correlation values. The feature is then used for the introduction of medicinal plants using the KNN algorithm which can make classification quickly and efficiently. In that study, it was able to produce a fairly good recognition accuracy of 82%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | NIM16.01.53.0014 SKR.I.05.01.1877 |
| Uncontrolled Keywords: | Tanaman Obat, GLCM, KNN Medicinal plants, GLCM, KNN |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty / Institution: | Fakultas Teknologi Informasi > Program Studi Teknik Informatika |
| Depositing User: | H Hayati |
| Date Deposited: | 09 Sep 2020 04:11 |
| Last Modified: | 09 Sep 2020 04:11 |
| URI: | https://eprints.unisbank.ac.id/id/eprint/7039 |
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