AbstrakJerawat sering dialami oleh kaum wanita maupun pria dari usia remaja hingga dewasa. Banyak rumah sakit dan klinik kecantikan yang dapat di datangi oleh para penderita untuk memeriksakan jerawat tersebut. Penelitian ini merupakan implementasi dari pendeteksian jerawat menggunakan image processing dan secara realtime, lalu sistem akan mengklasifikasikan jerawat yang ada pada wajah. Jerawat yang dapat dikenali oleh sistem ini yaitu jerawat, bekas, dan pus. Sistem deteksi dan klasifikasi ini dibuat dengan metode deep learning dengan menggunakan bahasa pemrograman Python, yang dibantu dengan menggunakan framework TensorFlow dengan model Faster R-CNN. Sistem ini hanya dapat berjalan di laptop dengan memiliki Python versi 3.6 di dalamnya dan telah memliki library Numpy, TkInter, Matplotlib, dan OpenCV dan juga memiliki kamera pada laptop yang digunakan agar dapat menjalankan sistem secara realtime yang didukung dengan GPU yang memadai. Perancangan alur aplikasi menggunakan flowchart diagram. Hasil uji terhadap sistem menggunakan perbandingan objek yang terdeteksi dengan yang seharusnya lalu dibagi dan dikalikan dengan seratus persen. Hasil yang didapat dari pengujian cukup baik menggunakan metode deep learning. AbstractAcne is often experienced by women and men from adolescence to adulthood. Many hospitals and beauty clinics can be visited by sufferers to check for acne. This research is an implementation of acne detection using image processing and in realtime, and the system will classify the acne on the face. The type of acne which can be recognized by this system is acne, scars, and pus. This detection and classification system is made with the deep learning method using Python for programming language, which is assisted by using the TensorFlow framework with the Faster R-CNN model. This system can only run on laptops with Python version 3.6 or above and has Numpy, TkInter, Matplotlib, and OpenCV libraries and also has a camera on the laptop that is used to be able to run the system in realtime supported by an adequate GPU. The application's flow designed by using the flowchart diagram. The results on the system use a comparison of detected objects with what should be seen with eyes then divided it and then multiplied by one hundred percent. The results obtained from testing are quite good using the deep learning method. Jurnal Ilmiah Teknologi dan Rekayasa Volume 23 No. 2 Agustus 2018 90 PENDAHULUAN Kulit merupakan organ terluar dari tubuh yang melapisi seluruh tubuh manusia, yang

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In this paper, we propose a quantized YCbCr color space (QYCbCr) technique which is employed in standard JPEG. The objective of this work is to accelerate computational time of the standard JPEG image compression algorithm. This is a development of the standard JPEG which is named QYCBCr algorithm. It merges two processes i.e., YCbCr color space conversion and Q quantization in which in the standar JPEG they were performed separately. The merger forms a new single integrated process of color conversion which is employed prior to DCT process by subsequently eliminating the quantization process. The equation formula of QYCbCr color coversion is built based on the chrominance and luminance properties of the human visual system which derived from quatization matrices. Experiment results performed on images of different sizes show that the computational running time of QYCbCr algorithm gives 4 up to 8 times faster than JPEG standard, and also provides higher compression ratio and better image quality.

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The Indonesian economy suffered a recession due to the COVID-19 pandemic, the first case of which was announced on March 3, 2020. In response to this disaster, various economic sectors carried out digital transformation, including the banking sector. This research is a case study of how Indonesian banking responded to the pandemic through various central bank policies complemented by literature review on the impact of the pandemic on the banking sector in the world, as well as the development of electronic payment systems before, during and after the COVID-19 pandemic. The central bank issued several regulations of relaxation to reduce the impact of the pandemic on the banking sector. From the perspective of society, the pandemic has actually accelerated the use of digital payment systems in Indonesia. The most widely used electronic payment instrument is electronic money, followed by RTGS transactions with data periods ranging from 2016 to 2021. ATM/Debit card transactions show relatively no fluctuating increases, while credit card transactions show a decline during the same observation period. The trend of increasing electronic transactions is expected to continue after the pandemic and will become a necessity in the Indonesian economy onwards