Butterfly species identification using glcm features and edge detection using KNN (K-Nearest Neighbor) and decision tree algorithm (C.45)
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Abstract
Butterflies are insects come from the kingdom Animalia, which are the Insecta class, the Lepidoptera order, and the sub-order of Rhopalocera. Butterflies can classified according to the patterns found on the butterfly's wings. Butterfly species have different patterns based on pigment, scale structure, and sunlight fall structure. The weakness of the human eye in specific the patterns in butterflies is the foundation in basis butterfly identification based on pattern recognition. This study used 3 butterfly species: Adonis, Black Hairstreak, and Gray Hairstreak. The butterfly dataset used was 150 which were obtained online. The pre-processing stage used segmentation and edge detection methods. The feature extraction stage used the Gray-level Co-occurrence Matrix (GLCM) method which extracted 8 shape and texture features including area, perimeter, metric, eccentricity, contrast, correlation, energy, and homogeneity. Classification phase used K-Nearest Neighbor (KNN) method with the values of k = 3, 5, 7, 9, 11, 13, 15, 17, and 19 as well as the Decision Tree method (C.45). The results of the identification of butterflies with the highest accuracy were obtained by the KNN Algorithm on the testing with a value of k = 3 of 93.33%, and the accuracy results using the Decision Tree method (C.45) is 84.44% while the results of identification using an application made using the GUI Matlab2017 with the KNN algorithm obtained an accuracy of 93.33% with a value of k= 3.
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Hasan, M., Riana, D., & Merlina, N. (2026). Butterfly species identification using glcm features and edge detection using KNN (K-Nearest Neighbor) and decision tree algorithm (C.45). Journal of Intelligent Decision Support System (IDSS), 9(1), 10-18. https://doi.org/10.35335/idss.v9i1.341
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Bertsimas, Dimitris, and Jack Dunn. 2017. “Optimal Classification Trees.” Machine Learning 106(7):1039–82. doi: 10.1007/s10994-017-5633-9.
Feng, Linan, Bir Bhanu, and John Heraty. 2016. “A Software System for Automated Identification and Retrieval of Moth Images Based on Wing Attributes.” Pattern Recognition 51:225–41. doi: 10.1016/j.patcog.2015.09.012.
Ghazanfar, Mobeen, Muhammad Faheem Malik, Mubashar Hussain, Razia Iqbal, and Misbah Younas. 2016. “Butterflies and Their Contribution in Ecosystem: A Review.” Journal of Entomology and Zoology Studies 4(2):115–18.
Ihsan, Ihsanuddin, Eka Wahyu Hidayat, and Alam Rahmatulloh. 2020. “Identification of Bacterial Leaf Blight and Brown Spot Disease In Rice Plants With Image Processing Approach.” Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika 5(2):59. doi: 10.26555/jiteki.v5i2.14136.
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Kang, Seung Ho, Jung Hee Cho, and Sang Hee Lee. 2014. “Identification of Butterfly Based on Their Shapes When Viewed from Different Angles Using an Artificial Neural Network.” Journal of Asia-Pacific Entomology 17(2):143–49. doi: 10.1016/j.aspen.2013.12.004.
Kaya, Yilmaz, Lokman Kayci, and Murat Uyar. 2015. “Automatic Identification of Butterfly Species Based on Local Binary Patterns and Artificial Neural Network.” Applied Soft Computing Journal 28:132–37. doi: 10.1016/j.asoc.2014.11.046.
Kayci, Lokman, and Ylmaz Kaya. 2014. “A Vision System for Automatic Identification of Butterfly Species Using a Grey-Level Co-Occurrence Matrix and Multinomial Logistic Regression.” Zoology in the Middle East 60(1):57–64. doi: 10.1080/09397140.2014.892340.
Mardhotillah, Atika, Anditya Atfianto, and Kurniawan Nur Rahmadhani. 2018. “Menghitung Jumlah Buah Cabe Berwarna Hijau Menggunakan Metode Transformasi Ruang Warna RGB.” E-Proceeding of Engineering 5(2):3641–48.
Meng, Yingchao, Zhongping Zhang, Huaqiang Yin, and Tao Ma. 2018. “Automatic Detection of Particle Size Distribution by Image Analysis Based on Local Adaptive Canny Edge Detection and Modified Circular Hough Transform.” Micron 106(December 2017):34–41. doi: 10.1016/j.micron.2017.12.002.
Öztürk, Şaban, and Bayram Akdemir. 2018. “Application of Feature Extraction and Classification Methods for Histopathological Image Using GLCM, LBP, LBGLCM, GLRLM and SFTA.” Procedia Computer Science 132(Iccids):40–46. doi: 10.1016/j.procs.2018.05.057.
Peggie, P. Djunijanti. 2017. “Aplikasi Pengenalan Jenis Kupu-Kupu Langka Berbasis Augmented Reality.” Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) 5(3):12. doi: 10.24843/jim.2017.v05.i03.p06.
Purwowidodo. 2015. “Studi Keanekaragaman Hayati Kupu-Kupu (Sub Ordo Rhopalocera) Dan Peranan Ekologisnya Di Area Hutan Lindung Kaki Gunung Prau Kabupaten Kendal Jawa Tengah.” Ilmu Pengetahuan Alam 1–230.
Rani, Kittur, Correspondence B. Manasa, SL Jagadeesh, and N. Thammaiah. 2019. “Colour Measurement of Ripening Mango Fruits as Influenced by Pre-Harvest Treatments Using L* A* B* Coordinates.” ~ 2466 ~ Journal of Pharmacognosy and Phytochemistry 8(1):2466–70.
Saputra, Kana, and Sri Wahyuni. 2018. “IDENTIFIKASI JENIS TANAMAN BERDASARKAN EKSTRAKSI FITUR MORFOLOGI DAUN MENGGUNAKAN K - NEAREST NEIGHBOR.” Jurnal Teknik Dan Informatika 5(1):24–29.
Tahmid, Taqi, and Eklas Hossain. 2017. “Density Based Smart Traffic Control System Using Canny Edge Detection Algorithm for Congregating Traffic Information.” 3rd International Conference on Electrical Information and Communication Technology, EICT 2017 2018-January(December):1–5. doi: 10.1109/EICT.2017.8275131.
Wasule, Vijay, and Poonam Sonar. 2017. “Classification of Brain MRI Using SVM and KNN Classifier.” Proceedings of 2017 3rd IEEE International Conference on Sensing, Signal Processing and Security, ICSSS 2017 218–23. doi: 10.1109/SSPS.2017.8071594.
Xiao, K., J. M. Yates, F. Zardawi, S. Sueeprasan, N. Liao, L. Gill, C. Li, and S. Wuerger. 2017. “Characterising the Variations in Ethnic Skin Colours: A New Calibrated Data Base for Human Skin.” Skin Research and Technology 23(1):21–29. doi: 10.1111/srt.12295.
Zhang, Shanwen, Haoxiang Wang, Wenzhun Huang, and Zhuhong You. 2018. “Plant Diseased Leaf Segmentation and Recognition by Fusion of Superpixel, K-Means and PHOG.” Optik 157:866–72. doi: 10.1016/j.ijleo.2017.11.190.
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