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"Sektor pariwisata menjadi salah satu sektor yang memiliki banyak potensipemasukan anggaran negara. Salah satu cara untuk meningkatkan pemasukanmelalui sektor pariwisata adalah dengan memanfaatkan teknologi informasi agardapat menarik lebih banyak wisatawan yang datang. Pemanfaatan teknologitersebut adalah dengan menggunakan smart tourism. Implementasi smart tourismyang digunakan pada pariwisata di Indonesia, khususnya untuk objek wisataMonumen Nasional (Monas) adalah dengan memanfaatkan aplikasi telepon pintarberbasis Visual Question Answering (VQA) untuk memberikan informasi detailmengenai objek pariwisata yang sedang diamati dari kamera ponsel. Fokus dariskripsi ini adalah untuk menghasilkan model latihan dengan akurasi deteksi objekyang baik. Hasil dari proses latihan model akan dijadikan sebagai model untukdeteksi objek yang ada di sekitar Monas yang akan digunakan untuk melakukan VQA. Dataset yang digunakan dalam penelitian ini adalah gambar Monas besertaobjek-objek sekitarnya sebanyak 600 gambar dengan label kelas sebanyak 25 kelasobjek. Jaringan yang digunakan untuk melakukan deteksi objek adalah denganmenggunakan YOLO dan RetinaNet, dimana nantinya kedua jaringan ini akandilakukan komparasi dengan mencari skor akhir dari hasil evaluasi kedua modelyang telah dihasilkan. Dengan menggunakan dataset orisinil, pada jaringan YOLO mean average precision (mAP) yang didapatkan dengan rentang nilai confidencelevel threshold 0,1 sampai 0,9 berkisar antara 60,77% sampai 71,99%, sedangkanuntuk jaringan RetinaNet mAP yang didapatkan berkisar antara 72,18% sampai92,98%. Dengan menggunakan dataset augmentasi, pada jaringan YOLO mAPyang didapatkan berkisar antara 52,51% sampai 93,72%, sedangkan untuk jaringanRetinaNet mAP yang didapatkan berkisar antara 23,8% sampai 56,19%. Untuk skorArea Under Curve (AUC) pada dataset orisinil sebesar 0,99 dan 0,96 pada datasetaugmentasi. Berdasarkan hasil eksperimen ini dapat disimpulkan model YOLOdapat mendeteksi lebih baik dibandingkan dengan RetinaNet dan datasetaugmentasi dapat menghasilkan deteksi gambar lebih baik dibandingkan dengandataset orisinil.

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Tourism sector has become one of the most potential income for some countires.One of the way to increase income from tourism sector is to implement informationtechnology so it can attract more tourists to come. The technology that can beimplemented is smart tourism. One of the smart tourism implementations forIndonesia tourism, especially for Monumen Nasional (Monas) tourism destinationis mobile based Visual Question Answering (VQA) application that can providedetailed information about tourism object from mobile phone camera. Focus of thisthesis is to produce training model with good detection accuracy. The result of themodel training process will be used as model for object detection model that willbe used for doing VQA. Dataset that will be used for this research are 600 picturescontaining Monas and 25 surrounding objects called class. The networks that willbe used for object detection is using YOLO and RetinaNet, where both of thesenetworks will be compared each other by searching the accuracy from evaluationmetric from both networks. By using original dataset, in YOLO network the meanaverage precision (mAP) score is between 60.77% to 71.99% with 0.1 to 0.9confidence level threshold range and in RetinaNet network the mAP score isbetween 72.18% to 92.98%. By using augmented dataset, in YOLO network themAP score is between 52.51% to 93.72% and in RetinaNet network the mAP scoreis between 23,8% to 56,19%. The Area Under Curve (AUC) score for originaldataset is 0.99 and 0.96 for augmented dataset using YOLO network. Based on theevaluation result, YOLO can detect objects better than RetinaNet and augmenteddataset can produce better detection than original dataset."

"Computer Vision Syndrome (CVS) merupakan sekumpulan gejala yang sering dialami oleh pengguna komputer dan dipengaruhi oleh berbagai faktor, baik faktor personal, komputer, durasi, lingkungan, dan kombinasi dari keempatnya. Penelitian ini merupakan penelitian deskriptif untuk melihat distribusi dan frekuensi dari faktor risiko CVS pada pegawai Pengembagan & Pelayanan Sistem Informasi (PPSI) di Gedung Fakultas Ilmu Komputer Universitas Indonesia (UI). Desain studi penelitian ini adalah cross sectional dan melibatkan 26 pegawai sebagai responden penelitian. Pengambilan data pada penelitian ini menggunakan kuesioner, wawancara, observasi, dan pengukuran langsung. Hasil penelitian menunjukan bahwa 84,6% responden mengalami keluhan gejala CVS dengan keluhan yang paling banyak dirasakan adalah eyestrain (73,1%), fatigue (65,4%), nyeri pundak dan nyeri punggung (57,7%) pada responden yang memiliki area kerja dengan tingkat pencahayaan < 300 lux. Memperbaiki tingkat pencahayaan pada area kerja, melakukan koreksi yang tepat pada kelainan refraksi mata, dan memperbaiki postur duduk saat bekerja dengan menggunakan komputer dapat membantu mengurangi gejala CVS.

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Computer Vision Syndrome (CVS) is a group of symptoms that are often experienced by computer users and it is influenced by various factors: personal, computer, duration, and environmental factors or combination of these factors.

This descriptive study aims to determine the distribution and frequency of CVS risk factors in PPSI employee at Faculty of Computer Science, University of Indonesia (UI).

The design of this study is cross-sectional and involved 26 employees as respondent. The data were collected by questionnaires, interviews, observation, and direct measurement.

The results showed that 84.6 % respondents get CVS complaint with the most complaints are eyestrain (73.1 %), fatigue (65.4 %), shoulder and back pain (57.7 %) in work area with light levels <300 lux. Improve the level of lighting in the work area, correct the vision error with a proper lens, and improve sitting posture while working with computer may help to reduce the CVS symptoms."

"This chapter describes some advances in modern pattern classification techniques, and new classes of information systems dedicated for image analysis, interpretation and semantic classification. In this book we present some new solutions for the development of modern pattern recognition techniques for processing and analysis of several classes of visual patterns, as well as some theoretical foundations for modern pattern interpretation approaches. In particular this monograph presents selected areas of application of pattern recognition and classification approaches including handwriting recognition, medical image analysis and interpretation, development of cognitive systems for image computer understanding, moving object detection, advanced image filtration and intelligent multi-object labeling and classification."

"This book collects interdisciplinary evidences and presents an answer from the perspective of computing, namely, the theory of cognitive prism. To recognize an environment, an intelligent system only needs to classify objects, structures them based on the connection relation (not through measuring), subjectively orders the objects, and compares with the target environment, whose knowledge is similarly structured. The intelligent system works, therefore, like a prism: when a beam of light (a scene) reaches (is perceived) to an optical prism (by an intelligent system), some light (objects) is reflected (are neglected), those passed through (the recognized objects) are distorted (are ordered differently). So comes the term 'cognitive prism'. "

"The international conference on Advances in Computing and Information technology (ACITY 2012) provides an excellent international forum for both academics and professionals for sharing knowledge and results in theory, methodology and applications of Computer Science and Information Technology. The Second International Conference on Advances in Computing and Information technology (ACITY 2012), held in Chennai, India, during July 13-15, 2012, covered a number of topics in all major fields of Computer Science and Information Technology including: networking and communications, network security and applications, web and internet computing, ubiquitous computing, algorithms, bioinformatics, digital image processing and pattern recognition, artificial intelligence, soft computing and applications. "

"This book constitutes the refereed proceedings of CVM 2012, the First International Conference on Computational Visual Media, held in Beijing, China, in November 2012. The 33 revised full papers were carefully reviewed and selected from 81 submissions. The papers are organized in topical sections on image processing I and II, geometric processing, saliency, recognition, perception and learning, shape analysis, media retrieval, and capture, rendering and visualization."

"This book constitutes the thoroughly refereed post-conference proceedings of the Third International Conference on Scale Space Methods and Variational Methods in Computer Vision, SSVM 2011, held in Ein-Gedi, Israel in May/June 2011. The 24 revised full papers presented together with 44 poster papers were carefully reviewed and selected from 78 submissions. The papers are organized in topical sections on denoising and enhancement, segmentation, image representation and invariants, shape analysis, and optical flow."

"This book constitutes the refereed proceedings of the International Conference on Computer Vision and Graphics, ICCVG 2012, held in Warsaw, Poland, in September 2012. The 89 revised full papers presented were carefully reviewed and selected from various submissions. The papers are organized in topical sections on computer graphics, computer vision and visual surveillance."

"This book constitutes the refereed proceedings of the Chinese Conference on Pattern Recognition, CCPR 2012, held in Beijing, China, in September 2012. The 82 revised full papers presented were carefully reviewed and selected from 137 submissions. The papers are organized in topical sections on pattern recognition theory, computer vision, biometric recognition, medical imaging, image and video analysis, document analysis, speech processing, and natural language processing and information retrieval."