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All Journal Journal of Information Technology and Computer Engineering Jurnal Hilirisasi IPTEKS (JHI) CHIPSET (Journal on Computer Hardware, Signal Processing, Embedded System and Networking)
Hadelina, Rizka
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Engineering Health Professions Public Health Veterinary Other

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Journal : journal of information technology and computer engineering

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Continuous Integration and Continuous Deployment (CI/CD) for Web Applications on Cloud Infrastructures Alanda, Alde; Mooduto, Hanriyawan Adnan; Hadelina, Rizka
JITCE (Journal of Information Technology and Computer Engineering) Vol. 6 No. 02 (2022)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.6.02.50-55.2022

Abstract

At this time, the application development process has experienced much development in terms of tools and the programming language used. The application development process is required to be carried out in a fast process using various existing tools. The application development and delivery process can be done quickly using Continuous Integration (CI) and Continuous Delivery (CD). This study uses the CI/CD technique to develop real-time applications using various programming languages implemented on a cloud infrastructure using the AWS codepipeline, which focuses on automatic deployment. Application source code is stored on different media using GitHub and Amazon S3. The source code will be tested for automatic deployment using the AWS code pipeline. The results of this study show that all programming languages can be appropriately deployed with an average time of 60 seconds
Hand Gesture to Control Virtual Keyboard using Neural Network Anandika, Arrya; Rusydi, Muhammad Ilhamdi; Utami, Pepi Putri; Hadelina, Rizka; Sasaki, Minoru
JITCE (Journal of Information Technology and Computer Engineering) Vol. 7 No. 01 (2023)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.7.01.40-48.2023

Abstract

Disability is one of a person's physical and mental conditions that can inhibit normal daily activities. One of the disabilities that can be found in disability is speech without fingers. Persons with disabilities have obstacles in communicating with people around both verbally and in writing. Communication tools to help people with disabilities without finger fingers continue to be developed, one of them is by creating a virtual keyboard using a Leap Motion sensor. The hand gestures are captured using the Leap Motion sensor so that the direction of the hand gesture in the form of pitch, yaw, and roll is obtained. The direction values are grouped into normal, right, left, up, down, and rotating gestures to control the virtual keyboard. The amount of data used for gesture recognition in this study was 5400 data consisting of 3780 training data and 1620 test data. The results of data testing conducted using the Artificial Neural Network method obtained an accuracy value of 98.82%. This study also performed a virtual keyboard performance test directly by typing 20 types of characters conducted by 15 respondents three times. The average time needed by respondents in typing is 5.45 seconds per character.
ANN Models for Shoulder Pain Detection based on Human Facial Expression Covered by Mask Hadelina, Rizka; Rusydi, Muhammad Ilhamdi; Firza, Mutia; Samuel, Oluwarotimi Williams
JITCE (Journal of Information Technology and Computer Engineering) Vol. 7 No. 01 (2023)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.7.01.49-55.2023

Abstract

Facial expressions are a method to communicate if someone feels pain. Moreover, coding facial movements to assess pain requires extensive training and is time-consuming for clinical practice. In addition, in Covid 19 pandemic, it was difficult to determine this expression due to the mask on the face. There for, it needs to develop a system that can detect the pain from facial expressions when a person is wearing a mask. There are 41 points used to form 19 geometrical features. It used 20.000 frames of 24 respondents from the dataset as secondary data . From these data, training, and testing were carried out using the ANN (Artificial Neural Network) method with a variation of the number of neurons in the hidden layer, i.e., 5, 10, 15, and 20 neurons. The results obtained from testing these data are the highest accuracy of 86% with the number of 20 hidden layers.
Co-Authors Ainul Mardiah Alamsyah, Adithya Alde Alanda, Alde Alfiqri, Muhammad Rozaq Anandika, Arrya Aprilita, Vivi Arvan, Muhammad Haekal Fauzi Akbar, Fauzi Firza, Mutia Harzikra, Rafki Hersyah, Mohammad Hafiz Lathifah Arief, Lathifah Mooduto, Hanriyawan Adnan Muhammad Rusydi H. Multri Okta Ilham, Randa Puteri Novani, Nefy Rayna Kartika, Rayna Samuel, Oluwarotimi Williams Sanda Patrisia Komalasari Sasaki, Minoru Tati Erlina Tri, Diwah Ganda Triani, Laura Amelia Utami, Pepi Putri Yunita, Try Diana