SMPN 43 Kota Bandar Lampung tidak luput dari penyelenggaraan pembelajaran secara daring akibat adanya pandemi Covid-19. Program pelatihan optimalisasi Google Apps ini diharapkan dapat mendukung proses belajar mengajar agar bisa berjalan efektif dan optimal. Tujuan pengabdian ini adalah untuk mengenalkan Google Apps (Forms, Docs, Sheets, Slides), Google Meet dan Google Classroom kepada para guru SMPN 43 Kota Bandar Lampung sehingga bisa proses belajar mengajar menjadi lebih optimal. Peserta pelatihan adalah 19 guru SMP Negeri 43 Bandar Lampung dengan berbagai background pendidikan. Pelatihan dilaksanakan secara daring melalui Google Meet. Survei dilakukan kepada peserta untuk mendapatkan kondisi sebelum dan setelah pelatihan. Evaluasi terhadap peserta dilakukan berdasarkan respon peserta terhadap pertanyaan dalam angket sebelum dan sesudah pelatihan. Hasil menunjukkan bahwa 100% guru merasa terbantu dengan adanya pelatihan ini. Tingkat keberhasilan sebesar 100% terlihat pada pengetahuan guru tentang Google Apps yang meliputi Google Form, Google Meet, Google Classroom, Google Docs, Google Sheet, dan Google Slide bertambah setelah mengikuti pelatihan.
Face recognition is currently widely used as a security component. In facial recognition, the image used will be converted into a grayish image and subsequently converted into a binary image. The binary image obtained in the next process will be analyzed. The analysis was carried out by calculating the similarity distance between the training data and the test data. In the process of measuring the distance of similarity between data sets, there are often obstacles to the implementation of complex algorithm formulas. This study solves this problem by analyzing the distance functions of Euclidean, Manhattan, Canberra, and the Squared Chord to perform facial recognition. Based on the research that has been carried out, the Euclidean distance function gets an accuracy of 58%, the Manhattan distance function gets an accuracy of 70%, the Canberra distance function gets an accuracy of 92%, and the Squared Chord distance function gets an accuracy of 66%. Based on these results, it can be concluded that Canberra's distance function with a highest accuracy result compared to the other three distance functions is better and more suitable for facial recognition.
In face recognition, the input image used will be converted into a simple image, which will then be analyzed. The analysis was carried out by calculating the distance of data similarity. In the process of measuring data similarity distances, they often experience problems implementing complex algorithm formulas. This research will solve this problem by implementing the Manhattan method as a method of measuring data similarity distances. In this study, it is hoped that the Manhattan method can be used properly in the process of matching test images and training images by calculating the proximity distance between the two variables. The distance sought is the shortest distance; the smaller the distance obtained, the higher the level of data compatibility. The image used in this study was converted into grayscale to facilitate the facial recognition process by thresholding, namely the process of converting a grayscale image into a binary image. The binary image of the test data is compared with the binary image of the training data. The image used in this study is in the Joint Photographic Experts Group (JPEG) format. Testing was carried out with 20 respondents, with each having two training images and two test images. The research was conducted by conducting experiments as many as 20 times. Facial recognition research using the Manhattan method obtains an accuracy of 70%. The image lighting used as the dataset influenced the accuracy results obtained in this study. Based on the results of this study, it can be concluded that the Manhattan method is not good for use in facial recognition research with poor lighting.
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