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All Journal Jurnal Neutrino : jurnal fisika dan aplikasinya Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan Jurnal Spektra JOIN (Jurnal Online Informatika) Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ Jurnal Informasi dan Komputer Communautaire: Journal of Community Service
Riza Ibnu Adam, Riza Ibnu
Departemen Fisika, FMIPA, Universitas Indonesia, Depok, 16424
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Implementation of Identity Loss Function on Face Recognition of Low-Resolution Faces With Light CNN Architecture Mufid, Tsaqif Mu'tashim; Adam, Riza Ibnu; Jaman, Jajam Khaeru; Garno, Garno; Maulana, Iqbal
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.6274

Abstract

Face recognition in low-resolution images has seen significant advancements over the past few decades. Although extensive research has been conducted to improve accuracy in these conditions, one of the main challenges remains the difficulty in identifying unique facial features in low-resolution images, leading to high error rates in identification. The use of Deep Convolutional Neural Networks (DCNN) for low-resolution face recognition is still limited. However, employing super-resolution models like REAL-ESRGAN can enhance recognition accuracy in low-resolution images. This study utilizes the Light CNN architecture and applies the margin-based identity loss function AdaFace on low-resolution datasets. The model is trained using the Casia-WebFace dataset and evaluated using the LFW and TinyFace test datasets. Based on the evaluation results on the LFW test data, the best model is Light CNN9-AdaFace, achieving the highest accuracy of 97.78% at 128x128 resolution. For images with the lowest resolution of 16x16, an accuracy of 83.37% was achieved using super-resolution techniques. On the TinyFace test data, the use of super-resolution resulted in performance metrics with a Rank-1 accuracy of 47.26%, Rank-5 accuracy of 55.25%, Rank-10 accuracy of 58.61%, and Rank-20 accuracy of 61.90% using the Light CNN9-AdaFace architecture.
Analisis Sentimen Ulasan pada Aplikasi E-Commerce dengan Menggunakan Algoritma Naive Bayes Ramadhan, Bintang Zulfikar; Adam, Riza Ibnu; Maulana, Iqbal
Journal of Applied Informatics and Computing Vol. 6 No. 2 (2022): December 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v6i2.4725

Abstract

The rapid development of E-commerce has given rise to many marketplaces in Indonesia such as Tokopedia, Shopee, Lazada. Tokopedia, Shopee and Lazada applications are applications that help sellers and buyers to make sales and purchase transactions for goods and services. Until now, of the three major E-Commerce applications, around 100 million users have downloaded the three E-Commerce applications. With the launch of some of these applications, it has caused a lot of opinions and criticisms from the public. Based on this, a sentiment analysis of the Naive Bayes algorithm was carried out to find out how the sentiment of users compares to the E-Commerce application on the Google Play Store. This research uses the Knowledge Discovery in Database (KDD) method which consists of 5 stages, namely data selection, preprocessing, transformation, data mining, and evaluation. The data used is a review of 500 E-Commerce applications per each application. At the data mining stage, it is carried out with 3 scenarios data sharing is 80:20, 70:30 and 60:40. The best results were obtained in scenario 1 (80:20) on the Shopee application using the Naive Bayes algorithm which resulted in an accuracy of 92%, precision of 92.13%, recall of 98.8% and f1-score of 95.35%.
Peningkatan Deteksi Kecelakaan di Jalan Raya Menggunakan Real-ESRGAN pada Citra CCTV Persimpangan Jalan Ikhsal, Muhammad Fachry; Dermawan, Budi Arif; Adam, Riza Ibnu
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5562

Abstract

The failure of the accident detection system on CCTV cameras can affect the increase in the death rate on the highway. The use of the CNN method in the construction of CCTV accident detection systems has been widely used before. However, common problems that are often encountered are dirty lenses and varifocal zooms that don't automatically focus, causing the quality of the resulting CCTV images to decrease, thus affecting system performance. In this research, a model was developed to detect accidents on CCTV images using the MobileNetV2 pre-trained model which was optimized by upscaling the dataset using the Real-ESRGAN model to produce more optimal performance. This study uses a CCTV image dataset totaling 989 and consisting of 2 types of prediction classes including accident and non-accident. The results showed that the MobileNetV2 model succeeded in producing 94% testing accuracy and an average inference time of 3.33 seconds in the GT test scenario. During the testing process, it was found that the model was not optimal if it identified new data with clustered objects. In addition, based on the test scenarios X2, X4, X8 it was found that the image quality calculated based on PSNR and SSIM values greatly influences classification performance such as accuracy, precision, recall, and AUC score.
Pengenalan Wajah Resolusi Rendah Menggunakan Arsitektur Lightweight VarGFaceNet dengan Adaptive Margin Loss Ramadani, Daffa Tama; Adam, Riza Ibnu; Jaman, Jajam Haerul; Rozikin, Chaerur; Garno, G.
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5831

Abstract

Face recognition is a modern security solution that is quickly and easily integrated into most existing devices, so this system is widely applied to several domains as one of the security authorizations. Developing face recognition models using mainstream architectures (AlexNet, VGGNet, GoogleNet, ResNet, and SENet) will make it difficult to implement the models on mobile devices and embedded systems. In addition, low resolution images, such as those from CCTV surveillance cameras or drones, pose challenges for the models to recognize faces, as the images lack sufficient details for identification. Therefore, this research aims to analyze the performance of a face recognition model developed using the lightweight VarGFaceNet architecture with the adaptive margin loss AdaFace on a low-resolution image dataset. Based on the evaluation results on the LFW dataset, an accuracy of 99.08% was achieved on high-resolution data (112x112 pixels), while on the lowest synthetic low-resolution data (14x14 pixels), an accuracy of 79.87% was obtained with the assistance of the Real-ESRGAN and GFP-GAN super-resolution models. On the TinyFace dataset, without fine-tuning, a Rank-1 accuracy of 46.08% was achieved without using super-resolution models and 45.03% when utilizing super-resolution models.
Co-Authors Anto Sulaksono Apriade Voutama Aprilia, Dita Arnisa Stefanie Budi Arif Dermawan Dwijaksara, Ni Luh Bella Efelina, Vita Fikri, Raihan Garno, G. garno, Garno Ibrahim Ibrahim Ikhsal, Muhammad Fachry Iqbal Maulana Irawan, Agung Susilo Yuda Jaman, Jajam Haerul Lela Nurpulaela Masyita, Nurani Mufid, Tsaqif Mu'tashim Putri, Divasetya Pratama Ramadani, Daffa Tama Ramadhan Sumantri, Muhammad Jodi Ramadhan, Bintang Zulfikar Reni Rahmadewi, Reni Rizal, Adhi Rizal, Adhi Rizal, Adhi Rozikin, Chaerur Santoso, Dian Budhi savina, savina Sitanggang, Altolyto Suci, Farradina Choria Susilawati, Susilawati Umaidah, Yuyun