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Gita Persada Butterfly Park is the only breeding of engineered in situ butterflies in Indonesia. It is located in Lampung and has approximately 211 species of breeding butterflies. Each species of Butterflies has a different texture on its wings. The Limited ability of the human eye to distinguishing typical textures on butterfly species is the reason for conducting a research on butterfly identification based on pattern recognition. The dataset consists of 600 images of butterfly’s upper wing from six species: Centhosia penthesilea, Papilio memnon, Papilio nephelus, Pachliopta aristolochiae, Papilio peranthus and Troides helena. The pre-processing stage is conducted using scaling, segmentation and grayscale methods. The GLCM method is used to recognize the characteristics of butterfly images using pixel distance  and Angular direction 0o, 45o, 90o and 135o. The features used is angular second moment, contrast, homogeneity and correlation. KNN classification method in this study uses k values1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 and 23 based on the Rule of Thumb. The result of this study indicate that Centhosia penthesilea and Papilio nephelus classes can be classified properly compared to the other 4 classes and require a classification time of 2 seconds at each angular orientation. The highest accuracy is 91.1% with a value of  in the angle of 90o and error rate8.9%. Classification error occured because the value of the test data features is more dominant with the value of the training image features in different classes than the supposed class.  Another reason is because of imperfect test data.

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Masyarakat modern cenderung memakai listrik secara kontinyu. Dan, tergantung dari pemakaian, besaran listrik yang dipakai berubah seiring waktu. Nilai besaran listrik harus berada dalam batasan tertentu agar sistem yang bekerja menggunakan listrik dapat berfungsi dengan baik. Selain itu, teknologi hijau mendorong pemakaian listrik optimal sesuai dengan keperluannya. Tetapi, pengukuran konvensional untuk nilai besaran listrik terpakai tidak efisien, pengecekan dilakukan berkala dan harus ditera langsung pada perangkat pengukur seperti meteran listrik. Dan, pada sistem tenaga listrik yang lebih kompleks seperti sistem tiga fasa dapat terjadi fluktuasi perubahan beban, ketidakseimbangan beban pada tiap fasa, mau pun keadaan abnormal yang terjadi dalam rentang yang singkat. Sehingga, kebutuhan informasi pemakaian besaran listrik waktu nyata diperlukan. Penelitian untuk Dashboard Pengawasan Besaran Listrik Waktu Nyata dilakukan dengan tujuan melakukan pengawasan terhadap besaran listrik terpakai. IoT (Internet of Things) dimanfaatkan untuk mendapatkan data mengenai besaran listrik seperti Tegangan, Arus, Daya, Faktor Daya, dan Energi. IoT kemudian mengirimkan data besaran listrik ke server basis data yang kemudian diakses oleh Dashboard untuk disajikan dalam grafik yang mudah dipahami.

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. The weakness of the human eye in distinguishing patterns on butterflies is a 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. The butterfly image used is in the 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, non maximum suppression, and hyterisis thresholding. 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 require a classification time of 8 seconds. The highest accuracy is obtained from testing with a value of k = 5 by 80%.