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Lampung memiliki satu-satunya penangkaran kupu-kupu in situ rekayasa di Indonesia yang bernama Taman Kupu-kupu Gita Persada yang memiliki kurang lebih 211 spesies kupu-kupu. Kupu-kupu dapat diklasifikasi berdasarkan corak yang terdapat pada sayap kupu-kupu. Spesies kupu-kupu memiliki corak yang berbeda-beda berdasarkan pigmen, struktur sisik dan jatuhnya sinar matahari. Kelemahan mata manusia dalam membedakan corak pada kupu-kupu merupakan suatu landasan dalam membangun identifikasi kupu-kupu berbasis pengenalan pola. Penelitian ini menggunakan 6 spesies kupu-kupu yaitu Papilio memnon, Troides helena, Papilio nephelus, Cethosia penthesilea, Papilio peranthus, dan Pachliopta aristolochiae. Dataset kupu-kupu yang digunakan berjumlah 600 citra berupa sisi sayap bagian atas. Tahap preprocessing menggunakan metode scaling, segmentation, dan grayscale. Tahap ekstraksi fitur menggunakan metode canny edge detection dengan menerapkan smoothing, edge strength, edge direction, nonmaximum suppression, dan hyterisis thresholding. Fitur yang digunakan yaitu eccentricity, perimeter, dan area. Tahap klasifikasi menggunakan metode K-Nearest Neighbor (KNN) dengan nilai k = 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, dan 23 yang didapatkan berdasarkan aturan Rule of Thumb. Sistem identifikasi kupu-kupu ini membutuhkan waktu 8 detik dalam klasifikasi citra. Akurasi tertinggi didapatkan pada pengujian dengan nilai k = 5 sebesar 80%. Keenam kelas kupu-kupu dapat dikenali oleh sistem namun kelas Cethosia penthesilea adalah yang paling akurat. Kesalahan identifikasi dapat terjadi dikarenakan fitur yang dihasilkan canny edge detection tidak sempurna yang dapat mempengaruhi hasil klasifikasi. Kata kunci : canny edge detection, Gita Persada, KNN, kupu-kupu, Lampung, pengenalan pola  Lampung has the only breeding of in situ butterflies engineered in Indonesia namely Gita Persada Butterfly Park, which has approximately 211 butterfly species. Butterflies can be classified according to patterns found on the wings of a butterfly. Butterfly species have different patterns based on pigment, the structure of the scales and the fall of sunlight. The weakness of the human eye in distinguishing patterns on butterflies is the foundation in building butterfly identification based on pattern recognition. This study uses 6 species of butterflies: Papilio memnon, Troides helena, Papilio nephelus, Cethosia penthesilea, Papilio peranthus, and Pachliopta aristolochiae. The butterfly dataset used is 600 images form of the upper wing side. The pre-processing stage uses the method of scaling, segmentation, and grayscale. The feature extraction stage uses the canny edge detection method by applying smoothing, edge strength, edge direction, nonmaximum suppression, and hysteresis thresholding. The features used is eccentricity, perimeter, and area. The classification phase uses the K-Nearest Neighbor (KNN) method with values k = 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 and 23 obtained under the Rule of Thumb. The identification of butterfly requires a classification time of 8 seconds. The highest accuracy is obtained from testing with a value of k = 5 by 80%. All classes can be classification but Cethosia penthesilea is the most accurate. Identification errors occurred because of imperfect features produced by canny edge detection, which can be affected by the result of classification. Keywords: butterflies, canny edge detection, Gita Persada, KNN, Lampung, pattern recognition

Identifikasi Kupu-Kupu Menggunakan Ekstraksi Fitur Deteksi Tepi (Edge Detection) dan Klasifikasi K-Nearest Neighbor (KNN)