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"Penyakit mata kering adalah suatu kondisi yang bersifat multifaktorial kronis yang sering terjadi pada permukaan mata. Mata kering diklasifikasikan menjadi dua, yaitu mata kering defisiensi aqueous dan mata kering evaporatif. Penyakit mata kering evaporatif yang disebabkan oleh disfungsi kelenjar meibom sudah terjadi pada lebih dari 85% kasus penyakit mata kering. Disfungsi kelenjar meibom adalah kelainan difus dan kronis dari kelenjar meibom yang umumnya ditandai dengan adanya perubahan kualitatif atau kuantitatif dalam sekresi kelenjar. Area kerusakan pada kelenjar meibom dibagi menjadi 4 tingkat atau skala, yaitu normal (meiboscore 0), meiboscore 1, meiboscore 2, dan meiboscore 3. Proses dalam mendiagnosis penyakit mata kering masih dilakukan secara subjektif oleh tenaga medis, hal tersebut dapat mengakibatkan perbedaan dalam menilai tingkat disfungsi kelenjar meibom. Penulis menggunakan data science untuk mendiagnosis penyakit mata kering dengan melakukan pendekatan Artificial Intelligence (AI) yang di dalamnya terdapat metode deep learning. Pada penelitian ini, penulis melakukan klasifikasi pada data citra yang merupakan hasil segmentasi model U-Net dengan 4 kelas skala meiboscore menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur ResNet50. Data citra yang digunakan pada penelitian ini adalah sebanyak 417 data citra yang dilatih menggunakan ResNet50 dengan epoch sebanyak 30 dan learning rate sebesar 0,001. Pembagian data dilakukan dengan masing-masing data training, data testing, dan data validation sebanyak 80%, 20%, dan 10% dari data training. Dari hasil simulasi, diperoleh masing-masing nilai rata-rata akurasi dan AUC adalah 92,62% dan 0,99 dengan running time yang didapat selama 1,8 detik.

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Dry eye disease is a chronic multifactorial condition that often occurs on the ocular surface. Dry eye is classified into two, namely aqueous deficiency dry eye and evaporative dry eye. Evaporative dry eye disease caused by meibomian gland dysfunction already occurs in more than 85% of dry eye disease cases. Meibomian gland dysfunction is a diffuse and chronic disorder of the meibomian glands that is generally characterized by qualitative or quantitative changes in glandular secretions. The area of damage to the meibomian glands is divided into 4 levels or scales, namely normal (meiboscore 0), meiboscore 1, meiboscore 2, and meiboscore 3. The process of diagnosing dry eye disease is still done subjectively by medical personnel, which can lead to differences in assessing the level of meibomian gland dysfunction. The author uses data science to diagnose dry eye disease by taking an Artificial Intelligence (AI) approach in which there is a deep learning method. In this research, the author classifies image data which is the result of segmentation of the U-Net model with 4 classes of meiboscore scale using the Convolutional Neural Network (CNN) method with ResNet50 architecture. The image data used in this research is 417 image data trained using ResNet50 with 30 epochs and a learning rate of 0.001. Data division is done with each training data, testing data, and validation data as much as 80%, 20%, and 10% of the training data. From the simulation results, the average accuracy and AUC values are 92.62% and 0.99 respectively with a running time of 1.8 seconds."

"The performance of solar cell with various surface texture patterns was reported. Wet, RIE one and two dimensions texturing with and without the nitridi antireflection coating were compared...."

"The main contemporary human-system interaction (H-SI) problems consist in design and/or improvement of the tools for effective exchange of information between individual humans or human groups and technical systems created for humans aiding in reaching their vital goals. This book is a second issue in a series devoted to the novel in H-SI results and contributions reached for the last years by many research groups in European and extra-European countries. The preliminary (usually shortened) versions of the chapters were presented as conference papers at the 3rd International Conference on H-SI held in Rzeszow, Poland, in 2010. A large number of valuable papers selected for publication caused a necessity to publish the book in two volumes. The given, 1st Volume consists of sections devoted to: I. Decision Supporting Systems, II. Distributed Knowledge Bases and WEB Systems and III. Impaired Persons Aiding Systems. The decision supporting systems concern various application areas, like enterprises management, healthcare, agricultural products storage, visual design, planning of sport trainings, etc. Other papers in this area are devoted to general decision supporting methods and tools. In the group of papers concerning knowledge bases and WEB-based systems are some focused on new computer networks technologies, models of malicious network traffic and selected problems of distributed networks resources organization and tagging. The concepts of a distributed virtual museum and of managing the process of intellectual capital creation in this part of the book are also presented. The last part of this volume contains a dozen of papers concerning various concepts and realizations of disabled persons aiding systems. Among them, the systems aimed at aiding visual or motion disability affected persons can be mentioned. The problems of residential infrastructure for ubiquitous health supervision and graphics- and gesture-based interactive children therapy supporting systems design in this volume are also presented."

"Latar belakang: Penggunaan komputer dapat menimbulkan suatu keluhan kesehatan yang disebut dengan Computer Vision Syndrome (CVS), Sindrom ini dapat dipengaruhi oleh berbagai faktor risiko individual, lingkungan dan komputer. Tujuan: Mengidentifikasi dan menganalisis faktor-faktor resiko ergonomik individual dan komputer yang berhubungan dengan kejadian Computer Vision Syndrome (CVS) pada pekerja pengguna komputer yang berkacamata dan pekerja yang tidak berkacamata. Metode: Penelitian ini merupakan penelitian metode kualitatatif. Penelitian dilakukan pada bulan April - Mei 2013 di Unit Pelakasana dan Pelatihan. Sampel sebanyak 18 orang dengan kriteria tertentu, dibagi menjadi 2 kelompok pekerja berkacamata dan pekerja yang tidak berkacamata. Peneliltian dilakukan dengan wawancara langsung menggunakan kuesioner dan pengukuran. Hasil: Faktor-faktor yang berhubungan dengan kejadian CVS adalah Kelembaban 71%, Pencahayaan kurang dari 300-500 lux (KEPMENKES nomor 1405/Menkes/SK/XI/2002), Usia lebih dari 40 tahun (das et al.), lama bekerja dengan komputer, dan jarak komputer dengan mata. Kesimpulan: Gejala ekstraokuler pada pekerja pengguna kacamata bifocal melakukan retrofleksi leher sehingga leher tertekuk kebelakang yang menyebabkan keluhan nyeri pada leher. Penderita terbanyak bukan dari pengguna kacamata tetapi pada pekerja yang tidak berkacamata. Serta penderita CVS (berdasarkan kriteria anamnesa) di usia 25 tahun, kedua hal ini berkaitan dengan potur ergonomi pada saat kerja baik secara design tempat kerja, kondisi ruangan ataupun durasi kerja yang semuanya saling berkaitan sehingga menimbulkan gejala Computer Vision Syndrome (CVS).

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Background: Computer usage could cause health complaints called Computer Vision Syndrome (CVS). This syndrome was influenced by individual and computer risk factors.

Aim: The objective of the study is to identify and to analyze individual and computer factors of computer Vision Syndrome (CVS).

Methods: This study was an observational study with methods qualitatively. The research was conducted in April-May 2013 in the Pelakasana and Training Unit. Sample of 18 people with certain criteria, divided into 2 groups of workers and workers who are not wearing glasses glasses. Peneliltian done by direct interviews using questionnaires and measurements.

Results: Factors associated with the incidence of CVS is Humidity 71%, less than the 300-500 lux lighting (KEPMENKES 1405/Menkes/SK/XI/2002), age over 40 years (das et al.), Long working computers, and computer distance by eye.

Conclusion: Extraocular symptoms in workers bifocal glasses users do retrofleksi neck so the neck is bent backwards which causes pain in the neck. Most patients but not from users goggles to workers who do not wear glasses. And people with CVS (based on criteria anamnesis) at the age of 25 years, these two things related to ergonomic posture at work both in design work, ambient conditions or duration of action that are all intertwined, giving rise to symptoms of Computer Vision Syndrome (CVS)."

"Seiring dengan kemajuan teknologi, kemampuan kendaraan atau robot untuk dapat secara otonom menjelajahi lingkungannya menjadi semakin diminati. Terdapat banyak tantangan yang dihadapi oleh kendaraan atau robot otonom agar dapat melakukannya. Salah satu tantangan tersebut adalah melacak gerakan inkremental dan menganalisis lingkungannya dengan akurat untuk melakukan lokalisasi. Salah satu metode yang dapat digunakan untuk membantu menangani masalah tersebut adalah dengan menggunakan visual odometry. Visual odometry adalah proses mengestimasi gerakan translasi dan rotasi kendaraan atau robot menggunakan kamera yang dipasangkan dengan menganalisa gambar-gambar yang diambil. Dalam penelitian ini, penulis mencoba membangun sebuah sistem visual odometry stereo sederhana. Sistem ini terdiri dari enam bagian utama yaitu mendeteksi fitur dan mengkomputasi deskriptornya menggunakan Oriented FAST and Rotated BRIEF (ORB), mencocokkan fitur secara brute force berdasarkan jarak Hamming dari deskriptor-deskriptor fitur, melacak fitur menggunakan optical flow Lucas-Kanade, melakukan triangulasi terhadap titik-titik fitur menggunakan linear triangulation, mengestimasi translasi dan rotasi dengan menyelesaikan permasalahan Perspective-n-Point (PnP) menggunakan gabungan metode Efficient PnP (EPnP) dan Random Sample Consensus (RANSAC), dan memperbaharui estimasi posisi dan orientasi. Sistem yang dibangun ini memperoleh average translation root mean squared error sebesar 5.1284% dan average rotation error sebesar 0.027 deg/m pada dataset odometry publik KITTI dengan performa kecepatan 18.88 frames per second pada environment komputer 1 core dengan clock speed 2.7 Ghz.

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As technology advances, the ability of vehicles or robots to be able to autonomously explore their environment is becoming increasingly desirable. There are many challenges that autonomous vehicles or robots face in order to do so. One of the challenges is to track incremental motions and accurately analyze their environment for localization. One of the methods that can be used to help to deal with this problem is by using visual odometry. Visual odometry is the process of estimating the translational and rotational movements of a vehicle or robot using a camera attached by analyzing the images taken. In this research, the author tried to build a simple stereo visual odometry system. This system consists of six main parts, namely detecting features and computing their descriptors using Oriented FAST and Rotated BRIEF (ORB), matching features by brute forcing based on Hamming distance from the feature descriptors, tracking features using Lucas-Kanade optical flow, triangulating the feature points using linear triangulation, estimating translation and rotation by solving Perspective-n-Point (PnP) problems using a combination of Efficient PnP (EPnP) and Random Sample Consensus (RANSAC) methods, and updating the position and orientation estimation. This system has an average translation root mean squared error of 5.1284% and an average rotation error of 0.027 deg/m on the KITTI public odometry dataset with a speed performance of 18.88 frames per second in a 1 core computer with a clock speed of 2.7 Ghz."