Klasifikasi Image Tumbuhan Obat Sirih Hijau dan Sirih Merah Menggunakan Metode Decision Tree
DOI:
https://doi.org/10.26594/teknologi.v13i1.3352Abstract
Tumbuhan obat atau lebih disingkat dengan sebutan TOGA merupakan tanaman hasil budidaya rumahan maupun tanaman yang tumbuh secara liar pada pekarangan rumah yang berguna sebagai pengganti obat-obatan kimia. Daun sirih merupakan salah satu jenis tumbuhan obat yang memiliki banyak jenis. Dengan perkembangan teknologi yang semakin pesat, tentunya dapat digunakan untuk mengklasifikasikan daun tumbuhan obat sirih hijau dan sirih merah. Data yang digunakan yaitu 1000 data image sirih hijau dan 1000 data image sirih merah dengan total keseluruhan data sebanyak 2000 data image. Model klasifikasi Decision Tree dilakukan dengan menggunakan 6 parameter feature ekstraksi tekstur Gray Level Co-Occurance Matric (GLCM) yaitu dissimiliarity, correlation, homogeneity, contrast, ASM, dan energy. Perbandingan persentasi data latih dan data uji yang digunakan ialah 60%:40%, 65%:35%, 70%:30%, 75%:25%, dan 80%:20%. Confusion matriks digunakan untuk mengukur hasil pengujian klasifikasi. Hasil pengujian dengan metode Decision Tree diperoleh nilai rata-rata akurasi sebesar 98%, presisi 98,3%. recall 97,1% dan f1-score 97,5%. Adapun hasil dari pengujian dengan metode Naïve Bayes didapatkan hasil rata-rata accuracy sebesar 92,1%, precission 87,5%. recall 97,8% dan F1 Score 92,3%. Dari hasil perbandingan tersebut dapat disimpulkan bahwa metode Decision Tree lebih unggul dibandingkan dengan metode Naïve Bayes.
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