The human ability to recognize a variety of objects, however complex the object, is the special ability that humans possess. Any normal human will have no difficulty in recognizing handwriting objects between an author and another author. With the rapid development of digital technology, the human ability to recognize handwriting objects has been applied in a program known as Computer Vision. This study aims to create identification system different types of handwriting capital letters that have different sizes, thickness, shape, and tilt (distinctive features in handwriting) using Linear Discriminant Analysis (LDA) and Euclidean Distance methods. LDA is used to obtain characteristic characteristics of the image and provide the distance between the classes becomes larger, while the distance between training data in one class becomes smaller, so that the introduction time of digital image of handwritten capital letter using Euclidean Distance becomes faster computation time (by searching closest distance between training data and data testing). The results of testing the sample data showed that the image resolution of 50x50 pixels is the exact image resolution used for data as much as 1560 handwritten capital letter data compared to image resolution 25x25 pixels and 40x40 pixels. While the test data and training data testing using the method of 10-fold cross validation where 1404 for training data and 156 for data testing showed identification of digital image handwriting capital letter has an average effectiveness of the accuracy rate of 75.39% with the average time computing of 0.4199 seconds.Keywords: Computer vision; Euclidean distance; Linear discriminant analysis; 10-Fold Cross Validation. AbstrakKemampuan manusia dalam mengenali berbagai macam objek, seberapa pun rumitnya objek tersebut, merupakan kemampuan istimewa yang dimiliki manusia. Manusia normal manapun tidak akan mengalami kesulitan dalam mengenali objek tulisan tangan antara seorang penulis dengan penulis lainnya. Permasalahannya apabila komputer melakukan pengenalan tulisan tangan yang memiliki ukuran, ketebalan, bentuk, dan kemiringan yang berbeda (ciri khas tersendiri dalam menulis dengan tulisan tangan). Dengan pesatnya perkembangan teknologi digital maka kemampuan manusia untuk mengenali objek tulisan tangan telah diterapkan dalam suatu program yang dikenal dengan nama Computer Vision. Penelitian ini bertujuan membuat sistem identifikasi berbagai jenis huruf kapital tulisan tangan menggunakan metode Linear Discriminant Analysis (LDA) dan Euclidean Distance. LDA digunakan untuk mendapatkan karakteristik ciri dari citra dan memberikan jarak antara kelas menjadi lebih besar, sedangkan jarak antara data training dalam satu kelas menjadi lebih kecil, sehingga waktu pengenalan citra digital huruf kapital tulisan tangan dengan menggunakan Euclidean Distance menjadi lebih cepat waktu komputasi (dengan mencari jarak terdekat antara data training dengan data testing). Hasil pengujian data sampel menunjukan bahwa resolusi citra sebesar 50x50 pi...

The coronavirus disease (COVID-19) outbreak has turned the world upside down bringing about a massive impact on society due to enforced measures such as the curtailment of personal travel and limitations on economic activities. The global pandemic resulted in numerous people spending their time at home, working, and learning from home hence exposing them to air contaminants of outdoor and indoor origins. COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which spreads by airborne transmission. The viruses found indoors are linked to the building's ventilation system quality. The ventilation flow in an indoor environment controls the movement and advection of any aerosols, pollutants, and Carbon Dioxide (CO2) created by indoor sources/occupants; the quantity of CO2 can be measured by sensors. Indoor CO2 monitoring is a technique used to track a person's COVID-19 risk, but high or low CO2 levels do not necessarily mean that the COVID-19 virus is present in the air. CO2 monitors, in short, can help inform an individual whether they are breathing in clean air. In terms of COVID-19 risk mitigation strategies, intelligent indoor monitoring systems use various sensors that are available in the marketplace. This work presents a review of scientific articles that influence intelligent monitoring development and indoor environmental quality management system. The paper underlines that the non-dispersive infrared (NDIR) sensor and ESP8266 microcontroller support the development of low-cost indoor air monitoring at learning facilities.

English has become the international language now used in almost all areas of global life and connect to transfer knowledge to the whole world, for the mastery of the English language allows a person to expand interaction in the international world. To help in learning English to primary school students, especially in class 3-5 primary school will be built learning module applications. In order to teach English well, better know the methods used in the process of learning English in primary school is the grammar translation method (GTM), which is a method of teaching grammar to the main characteristics focusing on translation (translation) and to memorize verb forms. This application is intended as an alternative to learning to enhance students’ understanding of the material about the translation and pronunciation of English interactive fun, the application is built using the Android operating system. Index Terms— Android, English, Elementary students, Grammar Traslations Method, Learning Module.

Early diagnosis of dyslexia disorders symptoms could minimize the disruption of children learning development and also help parents to give proper treatment for the children. The diagnosis process that has been done so far is mostly done manually that depends on the pediatric psychologist which is has limited number and relatively expensive. Therefore, the early diagnosis of dyslexia disorders symptoms is useful to make the diagnosis process easier and shorter. On this research, the researcher using the Dempster-Shafer method to detect three types of dyslexia disorders symptoms they are dyslexia, dysgraphia, and dyscalculia. The method was used to manage the dyslexia disorders symptoms in percentages and in this research the researcher had done a system test by comparing the diagnosis results from the system and the expert using ten sample cases, with 50% accuracy rate diagnose result.