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All Journal Jurnal Ilmiah Teknik (JUIT) International Journal Science and Technology (IJST)
Ivan Maurits
Universitas Gunadarma
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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SISTEM DETEKSI PEMAKAIAN MASKER PADA WAJAH SECARA REAL TIME MENGGUNAKAN FRAMEWORK TENSORFLOW DAN LIBRARY OPENCV Althaf Adhari Rachman; Ivan Maurits
Jurnal Ilmiah Teknik Vol. 2 No. 1 (2023): Januari : Jurnal Ilmiah Teknik
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/juit.v2i1.496

Abstract

At the end of December 2019, there was a mysterious virus that attacked the city of Wuhan in China. Which at the beginning of 2020 the new virus was a new type, namely (SARS-CoV-2) and the disease was called Coronavirus disease 19 (COVID-19). The World Health Organization (WHO) provides instructions to maintain a minimum distance of 1 meter, always wash hands with soap, and always use a mask when leaving the house. The purpose of this writing is to create a program to detect wearing masks on the face, which makes it easier for users to detect who is wearing a mask or not. The method used in this writing uses the SDLC model or System Data Life Cycle by carrying out five stages namely, planning, analysis, design, implementation, and testing. The design uses flowcharts and navigation structures as well as two UML diagrams, namely use case diagrams and activity diagrams. Then for making the program using Python as a programming language, Tensorflow as a framework, Keras and Opencv as libraries, and Visual Studio Code as a text editor. From the results of trials that have been carried out using three different devices and with different specifications, this system can detect mask objects very well and this detection system can also detect more than one person wearing a mask, or not wearing a mask.
APLIKASI MEMO ONLINE (E-MEMO) LABORATORIUM TEKNIK INFORMARTIKA BERBASIS ANDROID Robby Nugraha; Ivan Maurits; M Achsan Isa Al Anshori
Jurnal Ilmiah Teknik Vol. 2 No. 1 (2023): Januari : Jurnal Ilmiah Teknik
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/juit.v2i1.503

Abstract

Practicum is one of the academic activities that must be carried out by students at Gunadarma University. It's not just one department that does practicums, but all majors do practicums. One of the practicums that students must do is the Informatics Engineering Laboratory which is intended for students of the Faculty of Industrial Technology. In practicums in several majors, including practicum in the Informatics Engineering Laboratory, if the practitioner does not take part in the practicum for any reason, except for getting a special case, they must make a memo as the practitioner's identity to take part in the practicum again. To make a memo, the practitioner must come to the Informatics Engineering Laboratory Staff room to make a memo. The practitioner can make memos if the practitioner follows the specified time to make memos. Not only does the memo maker have to comply with the time the memo is written, but taking memos also must comply with the time the memo is taken. It can take quite a long time to get the memo because you have to come on time to the Informatics Engineering Laboratory Staff room and wait to pick up the memo. Therefore, the author took the initiative to create a mobile application called "Online Memo Application (E-Memo) Android Based Informatics Engineering Laboratory". With this application, the practitioner can make memos anywhere and anytime and also the practitioner can come to the Informatics Engineering Laboratory Staff room to validate the memo which is useful for checking the authenticity of the memo. This application is made with the Dart programming language with the Flutter framework. This application is also made using Android Studio and Firebase as a place to accommodate data on practitioners who have made memos. This application runs on Android Version 5.0 with the code name Nougat and above.
HUMAN GENDER DETECTION SYSTEM BASED ON FACIAL IMAGE USING CONVOLUTIONAL NEURAL NETWORK ALGORITHM Abdul Roid; Ivan Maurits
International Journal Science and Technology Vol. 2 No. 1 (2023): March: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v2i1.847

Abstract

The demand for system automation has been continuously increasing with the current technological developments. One of these advancements is in the implementation of face recognition. Camera capabilities have evolved from merely capturing images or videos to being able to process the resulting images. Facial images contain a wealth of information, one of which is the gender information of the individuals. To obtain this information, facial image classification using deep learning is required. In this scientific paper, the author utilizes the Convolutional Neural Network algorithm implemented with the Python programming language and employs TensorFlow as its framework. The research aims to predict human gender based on facial images. The dataset used in this study is obtained from the kaggle.com dataset provider, consisting of 9,600 male facial data and 9,600 female facial data. The data is divided into a training and testing set, with an 80% ratio for training data and a 20% ratio for testing data from the total available data. The model training process is performed for 15 epochs with 768 steps in each epoch. The testing results show that the Convolutional Neural Network method achieves a validation accuracy of approximately 91%. The developed program runs well through a webcam.
Co-Authors Abdul Roid Althaf Adhari Rachman M Achsan Isa Al Anshori M. Achsan Isa Al Anshori Robby Nugraha