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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan Journal of Information System JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTECOMS: Journal of Information Technology and Computer Science Systematics JATI (Jurnal Mahasiswa Teknik Informatika) JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Ilmu Teknik dan Komputer Jurnal Ilmiah Wahana Pendidikan TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat Society: Jurnal Pengabdian Masyarakat Innovative: Journal Of Social Science Research
garno, Garno
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Comparative Study of Web Server Performance Testing with and without Docker Based on Virtual Machines Ramadhan, Fajar Kurnia; Garno, Garno; Solehudin, Arip
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.v8i1.3884

Abstract

Web server development is often hindered by the cost and resources required, as developing a web server typically requires a bare-metal server. Container technology, which allows for the development of multiple web servers on a single bare-metal server, has become popular. One of the most widely used containers is Docker. Docker reduces the need for costs and resources. Beyond the issues of cost and resource requirements, the performance of web servers also needs to be considered. The performance of web servers with and without Docker needs to be verified. This research aims to test the performance of two web servers, one using Docker and one not using Docker, utilizing the native hypervisor VMware ESXi. The web server performance test items in this study include CPU and RAM resource usage. The method for developing infrastructure systems uses SIDLC (System Infrastructure Development Life Cycle). Performance testing (Load Test) was conducted using Apache JMeter as a tool, with the manipulation of the number of threads predetermined. Resource usage information was monitored using Prometheus and Grafana. The research results show that with the same resources for each virtual machine, the CPU resource usage of Virtual Machine 2 (Undockerized) is less than that of Virtual Machine 1 (Dockerized). Meanwhile, RAM resource usage is not affected by the number of users on both virtual machines. Virtual Machine 2 (Undockerized) is better at handling HTTP requests. Virtual Machine 1 (Dockerized) can handle only 2,790 users, while Virtual Machine 2 (Undockerized) can handle more than 2,790 users without errors.
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.
Co-Authors Aditya Daffa Syahputra Afridho, Afridho Ikhsan Alfajr, Nur Halizah Alfia, Alfia Meilani Putri Alwansyah, Izhari Ananda, Tri Darma Ananta, Ananta Ziaurohman Az Zaki Anggraeni, Dwi Suci Anisah Apriade Voutama Ari, Ari Rizwan Aries Suharso Arip Solehudin Athariq, Fajar Betha Nurina Sari Chandraditio, Ramadhan Deltania, Dellia Elvis Syahri, Mohamad Fauzan, Muh. Yoga Fitriyani, Dede Hayah, Najmil Heru Widiyanto, Muhammad Heryana, Nono Hidayat, Ibnu Nur Ihya Ullummuddien, Damareindra Iqbal Maulana Irawan, Agung Susilo Yuda Irna Purnahasanah Jamaludin, Asep Jaman, Jajam Haerul Johan Johan Juardi, Didi Kurnia Abdullah, Kunaifi Maheswara, Maheswara Abhista Hamdan Hafiz Maria, Giovanny Mayasari, Rini Mufid, Tsaqif Mu'tashim Muhammad Lias, Lintar Mukholad Fauzi, Wildan Novalia, Elfina Nur Padilah, Tesa Nurian, Andriani Nurina Sari, Betha Padilah, Tesa Nur Prihandani, Kamal Primaya, Aji Puad, Salim Purwantoro Raharjo, Yudi Rahma Putri, Annisa Rahmayani, Fenny Ramadhan Harahap, Yudha Ramadhan, Fajar Kurnia Risma, Risma Auliya Salsabilla Riza Ibnu Adam, Riza Ibnu Safitri, Nur Sandra Nathalie Silalahi, Jovanka Setiawan, Fikri Maulana Sofi Defiyanti Sufah, Feila Surya Prabu Al Amin, Sinar Susilo Yuda Irawan, Agung Susilo Yuda Irawan, Agung Umaidah, Yuyun Wildan Bahari, Bayu Yusuf, Dadang Yusup, Dadang Zidane, M Yazid