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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal INKOM Proceedings of KNASTIK Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications Seminar Nasional Informatika (SEMNASIF) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING KOMPUTIKA - Jurnal Sistem Komputer Jurnal Abdimasa TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol JMECS (Journal of Measurements, Electronics, Communications, and Systems) Jurnal Teknik Informatika (JUTIF) Arus Jurnal Sosial dan Humaniora Almufi Jurnal Pengabdian kepada Masyarakat Prosiding Konferensi Nasional PKM-CSR Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) eProceedings of Engineering
Koredianto Usman
Universitas Telkom
Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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PEOPLE COUNTING FOR PUBLIC TRANSPORTATIONS USING YOU ONLY LOOK ONCE METHOD Tsabita Al Asshifa Hadi Kusuma; Koredianto Usman; Sofia Saidah
Jurnal Teknik Informatika (Jutif) Vol. 2 No. 1 (2021): JUTIF Volume 2, Number 1, June 2021
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2021.2.2.77

Abstract

People counting have been widely used in life, including public transportations such as train, airplane, and others. Service operators usually count the amount of passengers manually using a hand counter. Nowadays, in an era that most of human-things are digital, this method is certainly consuming enough time and energy. Therefore, this research is proposed so the service operator doesn't have to count manually with a hand counter, but using an image processing with You Only Look Once (YOLO) method. This project is expected that people counting is no longer done manually, but already based on computer vision. This Final Project uses YOLOv4 that is the latest method in detecting untill 80 classes of object. Then it will use transfer learning as well to change the number of classes to 1 class. This research was done by using Python programming language with various platforms. This research also used three training data scenarios and two testing data scenarios. Parameters measured are accuration, precision, recall, F1 score, Intersection of Union (IoU), and mean Average Precision (mAP). The best configurations used are learning rate 0.001, random value 0, and sub divisions 32. And the best accuration for this system is 69% with the datasets that has been trained before. The pre-trained weights have 72.68% of accuracy, 77% precision, and 62.88% average IoU. This research has resulted a proper performance for detecting and counting people on public transportations.
WEBINAR STUDENT PRESENCE SYSTEM BASED ON REGIONAL CONVOLUTIONAL NEURAL NETWORK USING FACE RECOGNITION Akbar Trisnamulya Putra; Koredianto Usman; Sofia Saidah
Jurnal Teknik Informatika (Jutif) Vol. 2 No. 2 (2021): JUTIF Volume 2, Number 2, December 2021
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2021.2.2.82

Abstract

World health organization announce Covid-19 as a pandemic so On March 15th 2020, the social distancing has been established with working, learning, and praying from home. Webinar is one of the solutions so those activities still can be done face to face and conference-based. With webinar, users can interact each other in an online meeting from home. Student presence is part of a webinar. The purpose of this research is to design an accurate student presence with a face recognition system using R-CNN method. The object of this research is a human face with sufficient light, medium, and the face must be facing the camera. This research proposed for a webinar student presence system is using face recognition with Regional Convolutional Neural Network (R-CNN). With object detection and several scenarios used in this method, the webinar student presence system using R-CNN will be more accurate than the methods that have ever been used before. This research has done four scenarios to obtain the best parameters like 45 of total layers, test data of the whole dataset percentage as 10%, RMSProp as model op- timizer, and 0.0001 learning rate. With those parameters, it have resulted the best system performance including 99.6% accuration, 1 × 10-4 loss, 100% precision, 99% recall, and 99.5% F1 Score.
THE CALCULATION SYSTEM OF NUMBER OF PEOPLE IN A ROOM BASED ON HUMAN DETECTION USING HAAR-CASCADE CLASSIFIER Gusti Ngurah Rama Putra Atmaja; Koredianto Usman; Muhammad Ary Murti
Jurnal Teknik Informatika (Jutif) Vol. 2 No. 2 (2021): JUTIF Volume 2, Number 2, December 2021
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2021.2.2.83

Abstract

Data of number of people in the room, calculations are usually carried out by assigning someone to oversee a room. In this final project, a system for calculating the number of people in the room is designed with image processing based on human detection that can be used in rooms, both for commercial applications and for security. This system uses Raspberry Pi device that already has an image processing method Haar-Cascade Classifier. Input data is in the form of video taken directly via webcam to be captured into a frame so that it can be used as a input the Haar-Cascade Classifier method and perform the counting process will be sent to the Antares platform. The system design has been tested with five scenarios. Scenario 1 the effect of the distance of the object, scenario 2 the effect of the pose of the object, scenario 3 the effect of the amount the object in the frame, scenario 4 affects the scale factor and scenario 5 measurement computation time. Scenarios 1 to 3 will do the best configuration for minimum neighbour. The system gets the best accuracy of 98,5% when the object distance 4 meters, the best accuracy of 96,6% when the object is facing forward and accuracy the best is 97,7% when the object in the frame is more than two objects with the best configuration use the minimum neighbour 5. Scenario 4 gets accuracy the best is 76,2% when using the scale factor 1.1. Scenario 5 gets the average computation time of the system is under one second, meaning the detection process done pretty fast.
Co-Authors Achmad Rizal Achmad Rizal Achmad, Fariz Adam Agus Kurniawan Aditya S.B, I Dewa Agung Akbar Trisnamulya Putra Alfarizi, Muhammad Alfian Andriyan B. Suksmono Andriyan Bayu Suksmono, Andriyan Bayu ARIS HARTAMAN Bambang Hidayat Bedy Purnama Budiyanto, Anggara Denny Darlis Denta Rahmadani Eriel Mar Fiky Y. Suratman Fityanul Akhyar Gryaningrum Widi Pangestuti Gusti Ngurah Rama Putra Atmaja Hanung Tyas Saksono Hendra Gunawan Hendra Gunawan Heri Syahrian, Heri Heroe Wijanto Huljannah, Miftah Humayra, Tia Hasna Imanadi, Muhammad Tsabit Imanadi Ivy Anindhita Hadyningtyas Jangkung Raharjo Julyano , Muhammad Billy Kalfika Yani Ledya Novamizanti Maharani , Kartika Dwi Maharani, Kartika Dwi Marnelius, Chelsya Dwi Muhammad Ary Murti Nasirudin, Akhmad Yusuf Noor Akhmad Gilar Romadhon Nor Kumalasari Caecar Pratiwi Nur Ibrahim Nurinda Fadhilah Amani Nurinda Fadhilah Amani Pellokila, Victor Aji Admaja Priyambodo, Afif Putranto, Devdan Wisesa Putri Marito R. Irawan Satria Putra R. Yunendah Nur Fu’adah Radhibilla, Maulaya Radian Sigit Raditiana Patmasari Rahmayati Alindra Razief Moch Diar Rita Magdalena Runik Machfiroh Rustam Rustam Rustam Sigit, Radian Singgih Yuliyanto SOFIA SAIDAH Sugondo Hadiyoso Suryo Adhi Wibowo Susmini Indriani Lestariningati Syahanifa , Nancy Olivia Syahanifa, Nancy Olivia Tita Haryanti Tsabita Al Asshifa Hadi Kusuma Vitria Puspitasari Rahadi Wardani , Shania Yuliyanto, Singgih Zhafar, Mochamad Noverian