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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Mantik
Fachrul Kurniawan
Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Facemask Detection using the YOLO-v5 Algorithm: Assessing Dataset Variation and R esolutions Fachrul Kurniawan; I Nyoman Gede Arya Astawa; I Made Ari Dwi Suta Atmaja; Aji Prasetya Wibawa
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 2 (2023): July (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i2.3249

Abstract

The Covid-19 pandemic has made it imperative to prioritize health standards in companies and public areas with a large number of people. Typically, officers oversee the usage of masks in public spaces; however, computer vision can be employed to facilitate this process. This study focuses on the detection of facemask usage utilizing the YOLO-v5 algorithm across various datasets and resolutions. Three datasets were employed: the face with mask dataset (M dataset), the synthetic dataset (S dataset), and the combined dataset (G dataset), with image resolutions of 320 pixels and 640 pixels, respectively. The objective of this study is to assess the accuracy of the YOLO-v5 algorithm in detecting whether an individual is wearing a mask or not. In addition, the algorithm was tested on a dataset comprising individuals wearing masks and a synthetic dataset. The training results indicate that higher resolutions lead to longer training times, but yield excellent prediction outcomes. The system test results demonstrate that face image detection using the YOLO-v5 method performs exceptionally well at a resolution of 640 pixels, achieving a detection rate of 99.2 percent for the G dataset, 98.5 percent for the S dataset, and 98.9 percent for the M dataset. These test results provide evidence that the YOLO-v5 algorithm is highly recommended for accurate detection of facemask usage.
Follow-up on fertilizer distribution to rice farming groups using the k-means algorithm classification approach : ENGLISH Mochamad Habil Noor; Fachrul Kurniawan; Muhammad Ainul Yakin
Jurnal Mantik Vol. 7 No. 4 (2024): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i4.4728

Abstract

The distribution of fertilizers regulated in the Definitive Plan for Farmer Group Needs (RDKK) can be obtained by farmers through retail, which is specifically aimed at direct distributors, and the policies are regulated by the local regional government. The aim of this research is to develop a recommendation system that can assist the Pasuruan City Agriculture Service in making decisions regarding fertilizer distribution. In this research, an analysis and application of the K-Means classification method were carried out as a recommendation system for follow-up distribution of subsidized fertilizer. The K-Means method is used to group farmer groups based on several criteria, such as land area, amount of fertilizer received, and harvest yields. In this way, it is hoped that this method can provide more accurate recommendations for distributing subsidized fertilizer to rice farmer groups that need it. The research results show that the recommendation system developed can provide precise and accurate recommendations regarding the distribution of fertilizer to farmer groups. It is hoped that this system can help farmers increase their productivity and welfare
Co-Authors Aji Prasetya Wibawa Atmaja, I Made Ari Dwi Suta I Nyoman Gede Arya Astawa Mochamad Habil Noor Muhammad Ainul Yakin