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All Journal Journal of ICT Research and Applications Indonesian Journal of Artificial Intelligence and Data Mining JURNAL NUSANTARA APLIKASI MANAJEMEN BISNIS Jurnal Telematika ACADEMICS IN ACTION Journal of Community Empowerment JISA (Jurnal Informatika dan Sains) Jurnal Sains Indonesia IT For Society : Journal of Information Technology
Tjong Wan Sen
Universitas Presiden
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

 1 
Real-Time Detection of Face Masked & Face Shield Using YOLO Algorithm with Pre-Trained Model and Darknet Muhamad Muhaimin; Wan Sen Tjong
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 2 (2021): September 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i2.14235

Abstract

There are new regulations requiring the use of masks or face shields to prevent the transmission of Covid-19. Using deep learning, a model can be made to detect faces that use masks and face shields by training the model using the previous pre-trained model and using a custom dataset. The purpose of this study is to create a deep learning model that can detect faces with and without masks and as well as face shields for the prevention of covid-19 transmission using YOLO (You Only Look Once) with pre-trained models and custom datasets in real-time. In this study, using pre-trained models from YOLOv3, YOLOv3-Tiny, YOLOv4, YOLOv4-Tiny, and YOLOv4-Tiny-3l with Darknet Framework and compare between average pooling and max pooling in the convolutional neural network YOLO to detect face masks and face shields as a real-time. From experiment the highest mAP was obtained from YOLOv4 using average pooling with a value is 97.64% although the difference is not too much with YOLOv4 using max pooling with value 97.57% and the lowest was YOLOv3-Tiny using max pooling, which was 94.09%, and for the highest FPS was obtained by YOLOv4-Tiny with Fps values is 171 and mAP 96.75%. And for real-time detection of face masks and face shields, the best model used in testing using webcam 1080p is from YOLOv4-Tiny, because the FPS is quite good and the mAP is quite high.
Optimization of the Naïve Bayes Classifier (NBC) Algorithm Using the Sparrow Search (SSA) Algorithm to Predict the Distribution of Goods Receipts Rachma Oktari; Tjong Wan Sen
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 2 (2021): September 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i2.15339

Abstract

Distribution must be able to meet all needs based on sales orders from consumers, be responsible for the delivery order process running optimally, and ensure the good receipt process is in accordance with consumer sales order requests. PT. Diamond Cold Storage currently uses Enterprise Resource Planning (ERP) to record all reports from production to sales. But in reality there are still some obstacles in the distribution section. In the good receipt process, several items were found that did not match the sales order, such as: the item did not match the order request or the item did not match the order request. The process of mismatching the good receipt with the sales order will be met with the completion of the good receipt process or the bad thing is that there is a cancellation, so this causes a loss for the company. This study uses data mining techniques with the Naïve Bayes Classifier algorithm to predict the distribution of goods receipts based on distribution data, and uses the Sparrow Search Algorithm (SSA) algorithm to optimize the Nave Bayes Classifier by selecting features to improve accuracy. In this study, the results obtained that the SSA algorithm can improve the performance of NBC from 95.05% to 97.95%.
Co-Authors Abdul Ghofir Arry Akhmad Arman Bambang Riyanto Trilaksono Darien Daud Earlliawand Diky Djafar Sidik Eko Wahyudi Muhamad Ghofur Muhamad Muhaimin Rachma Oktari Riady, Sasmitoh Rahmad Rila Mandala Rosalina Rosalina Sahuri, Genta Sahuri Sinung Suakanto Siregar, Amril Mutoi Siti Aisyah Naili Mutia Yahya Suryana