PATRIANA, ARI (2020) KLASIFIKASI UMUR SELADA BERBASIS ANALISA TEKSTUR DENGAN GLCM (GRAY LEVEL CO- OCCURRENCE MATRIX) DAN PENGKLASIFIKASI KNN (K-NEAREST NEIGHBOR). S2 thesis, Universitas Mercu Buana Jakarta-Menteng.
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Abstract
Pada sistem hidroponik dibutuhkan kontrol dan pemberian nutrisi yang teratur sesuai dengan umur tanaman. Petani melakukannya dengan cara manual yang berpotensi pada ketidakseimbangan nutrisi yang berakibat pada hasil yang tidak maksimal, sehingga pemberian nutrisi otomatis berdasarkan umur tanaman perlu dilakukan. Pada penelitian ini, dilakukan klasifikasi umur selada pada hidroponik berbasis analisa tekstur atau pola yang terdapat pada citra selada. Penelitian ini menggunakan metode Gray Level Co-occurrence Matrix (GLCM) yang dilakukan pada empat (4) sudut GLCM dengan fitur-fitur yang dianalisa terdiri dari disimilaritas, homogenitas, energi, dan ASM. KNN digunakan sebagai pengklasifikasi untuk mendapatkan akurasi terhadap fitur yang dihasilkan oleh GLCM. Hasil penelitian menunjukan akurasi sebesar 65% diperoleh pada sudut dengan tingkat kesalahan dari sudut pandang selisih umur diperoleh paling tinggi sebesar 5% pada selisih dua hari. Penelitian ini menunjukan GLCM menghasilkan parameter-parameter terkait dengan perubahan tekstur tanaman sehingga dapat digunakan sebagai parameter untuk klasifikasi umur tanaman. Kata Kunci : Pengolahan Citra, Gray Level Co-occurrence Matrix, K- Nearest Neighbor. In the hydroponic system, it is important to control and supply nutrients according to plant age. The farmer adjust it manually which potential to unappropriate nutrient measures so that lead to not optimal result, automatic control and supply nutrient respect to plant age is required indeed. In this study a texture analysis to be able to classify the plant age on hydroponic has been done. The study used the method of Gray Level Co-occurrence Matrix (GLCM) which is conducted at four (4) GLCM angle with various features calculated consisting of dissimilarity, homogeneity, energy, and ASM. KNN is used as a classification algorithm to obtain accuracy of the features produced by GLCM with respect to plant age. The result showed that accuracy of 65% obtained at an angle of , with the error rate at the point of view of the age difference result the error maximum of 5% at a difference of two days. This study shows that the GLCM produces parameters related to changes in the texture of the plant so that can be used as a parameter for the classification of plant age. Keywords : Image Processing, Gray Level Co-occurrence Matrix, K-Nearest Neighbor.
| Item Type: | Thesis (S2) |
|---|---|
| Call Number CD: | CDT-554-20-032 |
| Call Number: | T-54-MTE-20-029 |
| NIM/NIDN Creators: | 55417120005 |
| Uncontrolled Keywords: | Pengolahan Citra, Gray Level Co-occurrence Matrix, K- Nearest Neighbor, Image Processing, Gray Level Co-occurrence Matrix, K-Nearest Neighbor |
| Subjects: | 600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan > 621 Applied Physics/Fisika terapan |
| Divisions: | Pascasarjana > Magister Teknik Elektro |
| Depositing User: | ORYZA LUVITA |
| Date Deposited: | 21 Feb 2022 06:17 |
| Last Modified: | 18 Jun 2022 06:31 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/56450 |
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