Pengenalan sidik jari merupakan bagian dari teknologi biometrik. Klasifikasi sidik jari yang paling popular adalah Henry classification system. Henry membagi sidik jari berdasarkan garis polanya menjadi lima kelas yaitu arch (A), tented arch (T), left loop (L), right loop (R), dan whorl (W). Penelitian ini menggunakan Convolutional Neural Network (CNN) dengan model arsitektur Residual Network-50 (ResNet-50) untuk mengembangkan sistem klasifikasi sidik jari. Dataset yang digunakan diperoleh dari website National Institute of Standards and Technology (NIST) berupa citra sidik jari grayscale 8-bit. Hasil pengujian menunjukkan bahwa pemrosesan awal Contrast Limited Adaptive Histogram Equalization (CLAHE) dalam model CNN dapat meningkatkan performa akurasi dari sistem klasifikasi sidik jari sebesar 11,79%. Pada citra tanpa CLAHE diperoleh akurasi validasi 83,26%, sedangkan citra dengan CLAHE diperoleh akurasi validasi 95,05%.

One way to prevent image duplication is by applying watermarking techniques. In this work, the watermarking process is applied to medical images using the Fast Discrete Curvelet Transforms (FDCuT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) methods. The medical image of the host is transformed using FDCuT so that three subbands are obtained. High Frequency (HF) subband selected for DCT and SVD applications. Meanwhile, SVD was also applied to the watermark image. The singular value on the host image is exchanged with the singular value on the watermark. Insertion of tears by exchanging singular values does not cause the quality of medical images to decrease significantly. The experimental results prove that the proposed FDCuT-DCT-SVD algorithm produces good imperceptibility. The proposed algorithm is also resistant to various types of attacks, including JPEG compression, noise enhancement attacks, filtering attacks, and other common attacks.

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<p class="Default">Presensi merupakan hal utama dalam suatu kegiatan, karena menjadi bukti dari laporan pelaksanaan. Umumnya, presensi kehadiran dilakukan secara manual, yaitu siswa membubuhkan tanda tangan pada daftar hadir yang diedarkan, atau guru memanggil siswa satu persatu. Namun metode tersebut mengakibatkan terjadinya pemborosan waktu dan sumber daya. Hadirnya teknologi QR-Code berbasis android memberikan solusi agar presensi dapat berjalan dengan efisien. Penelitian ini memiliki tiga konfigurasi sistem, diantaranya sistem encoder, sistem hardware, dan sistem decoder. Sistem encoder melakukan proses encode data berupa Nomor Induk Siswa Nasional (NISN) menjadi QR-Code menggunakan kode Bose, Chaudhuri, Hocquenghem (BCH). Sistem hardware terdiri dari perangkat android dan kartu pelajar. Sistem decoder melakukan proses deteksi QR-Code dengan aplikasi Smart Presence. Sistem diuji dengan pengujian black box, pengujian jarak deteksi, pengujian deteksi berdasarkan cahaya, serta pengujian kartu pelajar bernoda dan rusak. Sistem presensi mampu mendeteksi QR-Code dengan jarak minimal sebesar 3 cm dan jarak maksimal sebesar 45 cm dengan tingkat akurasi sebesar 98 % dan rata-rata waktu komputasi sebesar 1,3 detik.</p><p class="Default"> </p><p class="Default"><em><strong>Abstract</strong></em></p><p class="Default"><em>Presence is the main thing in an activity because it becomes evidence of the implementation report. Generally, attendance is done manually, i.e. students sign on the circulated attendance list, or the teacher calls students one by one. However, this method resulted in a waste of time and resources. The presence of Android-based QR-Code technology provides a solution so that the presence can run efficiently. This research has three system configurations, including the encoder system, hardware system, and decoder system. The encoder system encodes data in the form of a National Student Number (NISN) into a QR-Code using the Bose, Chaudhuri, Hocquenghem (BCH) codes. The hardware system consists of an Android device and a student card. The decoder system carries out the QR-Code detection process with the Smart Presence application. The system was tested with black-box testing, detection distance testing, light-based detection testing, and stained and damaged student card testing. The presence system is able to detect QR-Code with a minimum distance of 3 cm and a maximum distance of 45 cm with an accuracy rate of 98% and an average computing time of 1.3 seconds.</em></p><p class="Default"><em><strong><br /></strong></em></p>

This paper proposes novel compressed sensing (CS) of colored iris images using three RGB iterations of basis pursuit (BP) with sparsity averaging (SA), called RGB-BPSA. In RGB-BPSA, a sparsity basis is performed using the average of multiple coherent dictionaries to improve the BP reconstruction. In the experiment, first, the decomposition level of wavelet is studied to analyze the best reconstruction result. Second, the effect of compression rate (CR) is considered. Third, the effect of resolution is investigated. Last, the sparse basis of SA is compared to existing basis, i.e., curvelet, Daubechies-1 or haar, and Daubechies-8. The superior RGB-BPSA over existing CS is shown by better visual quality with higher signal to noise ratio (SNR) and structural similarity (SSIM) index in the same CR. In addition, reconstruction time also investigated where RGB-BPSA outperforms curvelet and two times longer than haar and Daubechies-8. INDEX TERMSCompressed sampling, basis pursuit (BP), sparsity averaging, iris images.

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