Klasifikasi Daun Herbal Berbasis Integrasi Fitur LBP dan Bentuk Menggunakan Random Forest

AMALIA PUTRI UTAMI; DOLLY INDRA; FITRIYANI UMAR

doi:10.26760/mindjournal.v11i1.44-59

Klasifikasi Daun Herbal Berbasis Integrasi Fitur LBP dan Bentuk Menggunakan Random Forest

AMALIA PUTRI UTAMI, DOLLY INDRA, FITRIYANI UMAR

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Abstrak

Identifikasi daun herbal secara manual sering mengalami kendala akibat kemiripan visual antarspesies yang berpotensi menimbulkan kesalahan pemanfaatan. Penelitian ini bertujuan mengklasifikasikan sepuluh jenis daun herbal menggunakan kombinasi fitur tekstur Local Binary Pattern (LBP) dan fitur bentuk, yaitu aspect ratio, eccentricity, circularity, dan convexity. Dataset terdiri dari 500 citra yang dibagi menjadi 400 data latih dan 100 data uji dengan rasio 80:20. Tahap pre-processing meliputi resize, konversi ke grayscale, dan Gaussian Blur untuk mengurangi noise. Segmentasi dilakukan menggunakan Otsu thresholding untuk memperoleh objek daun dan Canny Edge Detection untuk menonjolkan struktur tekstur. Proses klasifikasi menerapkan algoritma Random Forest dengan pengujian beberapa kombinasi parameter guna memperoleh model optimal. Hasil terbaik diperoleh pada model dengan n_estimators=200, max_features=2, max_depth=none, dan min_samples_leaf=2, yang menghasilkan akurasi 92%, precision 92%, recall 92%, dan F1-score 92%.

Kata kunci: Daun Tanaman Herbal; Fitur Bentuk; Fitur Tekstur; Klasifikasi Citra; Local Binary Pattern; Random Forest

Abstract

Manual identification of herbal leaves often encounters challenges due to visual similarities among species, which can potentially lead to errors in their utilization. This study aims to classify ten types of herbal leaves using a combination of Local Binary Pattern (LBP) texture features and shape features, namely aspect ratio, eccentricity, circularity, and convexity. The dataset consists of 500 images divided into 400 training data and 100 testing data with an 80:20 ratio. The preprocessing stage includes resizing, grayscale conversion, and Gaussian Blur to reduce noise. Segmentation was performed using Otsu thresholding to extract the leaf object and Canny Edge Detection to enhance texture structures. The classification process applies the Random Forest algorithm with testing of several parameter combinations to obtain the optimal model. The best results were achieved with a model using n_estimators=200, max_features=2, max_depth=none, and min_samples_leaf=2, yielding an accuracy of 92%, precision of 92%, recall of 92%, and F1-score of 92%.

Keywords: Herbal Plant Leaves, Texture Features, Shape Features, Image Classification, Local Binary Pattern; Random Forest

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Referensi

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DOI: https://doi.org/10.26760/mindjournal.v11i1.44-59