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"Skripsi ini membahas tentang pengembangan sistem face recognition yang diaplikasikan pada aplikasi ujian berbasis Android yang diberi nama AyoTest menggunakan FaceNet. Tujuan dari dikembangkannya AyoTest sendiri adalah untuk membantu tenaga pengajar dalam meningkatkan efektivitas pengawasan ujian yang dilakukan secara daring. Penelitian ini diharapkan dapat membantu dalam meningkatkan efektivitas pengawasan ujian daring dengan menggunakan face recognition untuk mengotomatisasi sebagian besar dari kegiatan pengawasan yang sebelumnya harus dilakukan secara manual oleh tenaga pengajar. Berdasarkan hasil penelitian, didapatkan bahwa implementasi sistem face recognition dari aplikasi AyoTest dapat digunakan untuk meningkatkan efektivitas pengawasan ujian, di mana pada proses face authentication akurasi yang didapatkan adalah sebesar 100% bahkan ketika peserta ujian hanya memiliki 1 foto pada basis data wajah dan nilai false negative dan false positive pada proses face monitoring yang tercatat hanya sebesar 16,67% dan 22,22% untuk 18 partisipan yang berhasil melaksanakan ujian.

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This bachelor thesis discusses the system development of face recognition applied to an Android-based examination application called AyoTest using FaceNet. The purpose of the development of AyoTest itself is to assist teaching staff in increasing the effectiveness of conducting online examinations. This research is hoped to assist in increasing the effectiveness of examination proctoring with face recognition to automate most of the supervisions that previously had to be conducted manually by teaching staff. Based on the results of the research, it was found that the implementation of the face recognition system from the AyoTest application can be used to increase the effectiveness of examination proctoring, where the accuracy score obtained in the face authentication process is 100% even if the examinee only has 1 photo in the face database and the false negative and false positive scores in the face monitoring process were recorded at only 16.67% and 22,22% for 18 participants who successfully carried out the examination."

"Penelitian ini bertujuan untuk mendapatkan kombinasi parameter yang optimal dalam simulasi pemeriksaan kranial, toraks, dan abdomen menggunakan sistem digital radiography (DR). Optimasi dilakukan menggunakan phantom in-house dengan objek kontras pada DR Siemens Luminos Agile Max. Pasien pediatrik dipisahkan menjadi empat kelompok usia; grup A (0-1 tahun), grup B (1-5 tahun), grup C (5-10 tahun), dan grup D (10-15 tahun). Kombinasi lapisan PMMA dan cork dengan ketebalan total yang berbeda digunakan untuk mensimulasikan pasien yang termasuk dalam setiap kelompok usia untuk wilayah anatomis yang berbeda (kranial, toraks, dan abdomen). Optimasi dilakukan dalam tiga langkah; kVp, diikuti oleh mAs, dan kemudian optimasi filter tambahan. Semua langkah optimasi dilakukan berdasarkan nilai FOM (figure of merit) yang dihitung sebagai rasio SDNR (signal difference to noise ratio) kuadrat dan entrance surface dose dengan FOM tertinggi yang mewakili kondisi optimum. Hasil dari optimasi ini dievaluasi berdasarkan FOM tertinggi yang dihasilkan dari setiap eksposi. Adapun MTF dan CV digunakan sebagai parameter pembanding terhadap nilai FOM yang rancu. Dalam pemeriksaan kranial, FOM tertinggi dihasilkan oleh faktor eksposi 44 kV, 3.2 mAs, dan 0 mmCu atau tanpa filter (A), 46 kV, 5.6 mAs, dan 0.1 mmCu (B), 49 kV, 7.1 mAs, dan 0.2 mmCu (C) dan 50 kV, 9 mAs, dan 0.1 mmCu (D). Untuk pemeriksaan toraks, nilai FOM tertinggi dihasilkan oleh faktor eksposi 45 kV, 2,5 mAs, dan 0,2 mmCu (A), 45 kV, 4 mAs, dan 0.2 mmCu (B), 46 kV, 5.6 mAs, dan 0.2 mmCu (C), dan 47 kV, 6.3 mAs, dan 0.2 mmCu (D). Untuk pemeriksaan abdomen, nilai FOM tertinggi dihasilkan oleh faktor eksposi 48 kV, 4 mAs, dan 0.1 mmCu (A), 50 kV, 6.3 mAs, dan 0.2 mmCu (B), 53.5 kV, 8 mAs, dan 0 mmCu (C), dan 58.5 kV, 8 mAs, dan 0 mmCu (D).

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This study was aimed to obtain optimum parameter combination in simulated cranial, thorax, and abdominal examinations using digital radiography (DR) systems. Optimization was performed using in-house phantom with contrast objects on Siemens Luminos Agile Max DR. Paediatric patients were separated into four age groups; group A (0-1 year), group B (1-5 years), group C (5-10 years), and group D (10-15 years). Slab phantoms consisted of PMMA and cork with different total thickness were used to simulate patients belonging to each age group for different anatomical region (cranial, thorax, and abdomen). Optimization were performed in three steps; first kVp, followed by mAs, and then additional filter optimization. All the steps of optimization were performed based on FOM (figure of merit) values calculated as ratio of squared SDNR (signal difference to noise ratio) and entrance surface dose with the highest FOM representing the optimum condition.

The results of this optimization were evaluated based on the highest FOM generated from each exposure. For this DR, optimum parameters (i.e. highest FOM) are different for each age group and anatomical region. In cranial examination, the highest FOM are generated by exposure factors of 44 kV, 3.2 mAs, and 0 mmCu filter (A), 46 kV, 5.6 mAs, and 0.1 mmCu filter (B), 49 kV, 7.1 mAs, and 0.2 mmCu filter (C) and 50 kV, 9 mAs, and 0.1 mmCu filter (D). For thorax examination, the highest FOM value is generated by exposure factor 45 kV, 2.5 mAs, and 0.2 mmCu (A), 45 kV, 4 mAs, and 0.2 mmCu (B), 46 kV, 5.6 mAs, and 0.2 mmCu (C), and 47 kV, 6.3 mAs, and 0.2 mmCu (D). For abdominal examination, the highest FOM value is produced by exposure factor 48 kV, 4 mAs, and 0.1 mmCu (A), 50 kV, 6.3 mAs, and 0.2 mmCu (B), 53.5 kV, 8 mAs, and 0 mmCu (C), and 58.5 kV, 8 mAs, and 0 mmCu (D)."

"Penggunaan pin dan password bahkan token sudah dianggap ketinggalan zaman sehingga negara berkembang banyak mengembangkan metode transaksi berbasis biometrik. Biometrik yang merupakan karakterisitik biologis yang banyak digunakan saat ini adalah mata, wajah, dan sidik jari. Wajah dan sidik jari dalam kondisi tertentu dapat berubah dan tidak dapat dikenali oleh sebab itu mata atau tepatnya iris adalah pilihan yang tepat untuk digunakan untuk metode autentikasi mengingat mata manusia tidak mudah berubah. Tugas akhir ini berfokus pada pengembangan sistem yang sudah ada sebelumnya mengenai autentikasi menggunakan metode lokalisasi dan normalisasi half-polar pada iris mata. Pengembangan yang dilakukan adalah agar pengenalan dapat lebih akurat dan cepat menggunakan metode segementasi mata dan normalisasi yang berbeda dengan metode half-polar serta membuat pengenalan dapat dilakukan pada mata kiri dan kanan secara bersamaan mengingat Iris pattern pada kedua mata manusia berbeda. Metode-metode segmentasi iris yang diajukan adalah Zeta-v1, Zeta-v2, Zeta-v3, Zeta-v4, Zeta-v5, Zeta-v6 dan Zeta-v7. Hasil pengujian terbaik dari segi performa waktu ditunjukkan oleh metode Zeta-v7 dengan rata-rata 0.0138427 detik. Hasil Pengujian terbaik dari segi akurasi sistem adalah Zeta-v1, dengan persentase penolakan yang salah bernilai 100 dan persentase penerimaan yang benar bernilai 94,90.

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The use of pin and password and even tokens is considered outdated, so many countries develop biometric based transaction methods. Biometrics which are the most widely used biological characteristics are the eye, face, and fingerprint. The faces and fingerprints in certain conditions can change and can not be recognized. The eye or precisely the iris is the right choice to use for authentication methods considering that the human rsquo s eye is not easily changed.

This final assignment focuses on the development of previous systems of authentication using localization methods and half polar Normalization of the iris. Development is performed to make the recognition more accurate dan faster while using different eye segmentation and Normalization methods. The recognition methods can be used for left and right eyes considering both eyes in human have different iris pattern.

The proposed iris segmentation methods are Zeta v1, Zeta v2, Zeta v3, Zeta v4, Zeta v5, Zeta v6 dan Zeta v7. The best test result based on time performance presented by the Zeta v7 segmentation which shows the average time performance 0.0138427 seconds. The best result based on accuracy presented by Zeta v1 which show the percentage of wrong rejection 100 and percentage of right acceptance 94,90."

"This work proposes a complete sensor-independent visual system that provides robust target motion detection. First, the way sensors obtain images, in terms of resolution distribution and pixel neighbourhood, is studied. This allows a spatial analysis of motion to be carried out. Then, a novel background maintenance approach for robust target motion detection is implemented. Two different situations are considered, a fixed camera observing a constant background where objects are moving, and a still camera observing objects in movement within a dynamic background. This distinction lies on developing a surveillance mechanism without the constraint of observing a scene free of foreground elements for several seconds when a reliable initial background model is obtained, as that situation cannot be guaranteed when a robotic system works in an unknown environment. Other problems are also addressed to successfully deal with changes in illumination, and the distinction between foreground and background elements."

"This special issue contains five selected papers that were presented at the Workshop on Pattern Recognition for IT Security, held in Darmstadt, Germany, in September 2010, in conjunction with the 32nd Annual Symposium of the German Association for Pattern Recognition, DAGM 2010. It demonstrates the broad range of security-related topics that utilize graphical data. The contributions explore the security and reliability of biometric data, the power of machine learning methods to differentiate forged images from originals, the effectiveness of modern watermark embedding schemes and the use of information fusion in steganalysis."

"The two-volume set LNCS 7324/7325 constitutes the refereed proceedings of the 9th International Conference on Image and Recognition, ICIAR 2012, held in Aveiro, Portugal, in June 2012. The 107 revised full papers presented were carefully reviewed and selected from 207 submissions. The papers are organized in topical sections on clustering and classification, image processing, image analysis, motion analysis and tracking, shape representation, 3D imaging, applications, biometrics and face recognition, human activity recognition, biomedical image analysis, retinal image analysis, and call detection and modeling."

"This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics. It shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and unsupervised learning. Includes advances on unsupervised learning using natural computing techniquesReports on topics in emerging areas such as evolutionary multi-objective unsupervised learningFeatures natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms"

"This book constitutes the thoroughly reviewed post-proceedings of the 8th International Workshop on Argumentation in Multi-Agent Systems, ArgMas 2011, held in Taipei, Taiwan in May 2011 in association with the 10th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2011). The 8 revised full papers taken from ArgMAS 2011. Also included are 5 invited papers based on presentations on argumentation at the AAMAS 2011 main conference. All together the 13 papers included in the book give a representative overview on current research on argumentation in multi-agent systems. The papers are listed alphabetically by first author within three thematic topics, foundations and theory, argumentation and dialogue, and applications."

"This work investigates two-dimensional change detection methods. The existing methods in the literature are grouped into four categories: pixel-based, transformation-based, texture analysis-based, and structure-based. In addition to testing existing methods, four new change detection methods are introduced: fuzzy logic-based, shadow detection-based, local feature-based, and bipartite graph matching-based. "