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All Journal MEDIAPSI Martabe : Jurnal Pengabdian Kepada Masyarakat MAJU : Jurnal Ilmiah Pendidikan Matematika Jurnal Ilmu Keperawatan Anak Jurnal Studi Guru dan Pembelajaran Journal of Issues in Midwifery Jurnal Teknik Informatika (JUTIF) Kutubkhanah Jurnal Penelitian Sosial Keagamaan Jurnal Ilmu Multidisplin Media Bina Ilmiah Innovative: Journal Of Social Science Research OMNICODE Journal (Omnicompetence Community Developement Journal) IJMA Journal of Indonesian Constitutional Law Education Curiosity Jurnal Indonesia Mengabdi
Milasari Milasari
Jurusan Sistem Informasi Fakultas Sains dan Teknologi UIN Syarif Hidayatullah Jakarta
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Journal : Jurnal Teknik Informatika (JUTIF)

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Classification of Helmet and Vest Usage for Occupational Safety Monitoring using Backpropagation Neural NetworkClassification of Helmet and Vest Usage for Occupational Safety Monitoring using Backpropagation Neural Network Arifin, Nurhikma; Insani, Chairi Nur; Milasari, Milasari; Rusman, Juprianus; Upa, Samrius; Utama, Muhammad Surya Alif
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4781

Abstract

Occupational Safety and Health (OSH) is a critical aspect in high-risk work environments, where the consistent use of Personal Protective Equipment (PPE) plays a vital role in preventing workplace accidents. However, non-compliance with PPE regulations remains a significant issue, contributing to a high number of work-related injuries in Indonesia. This study proposes an automated detection and classification system for PPE usage, specifically helmets and vests, using the Backpropagation algorithm in artificial neural networks. A total of 100 images were utilized, equally divided between complete and incomplete PPE usage. The dataset was split into 60% training and 40% testing. Image segmentation was performed using HSV color space conversion and thresholding, followed by RGB color feature extraction. The Backpropagation algorithm was then employed for classification. Experimental results show an average accuracy of 90%, with precision, recall, and F-measure all reaching 0.9. Despite some misclassifications due to color similarity between helmets and head coverings, the model demonstrated robust performance with relatively low computational requirements. This study contributes to the field of computer vision and intelligent safety systems by demonstrating the practical effectiveness of lightweight ANN architectures for PPE detection in real-time industrial scenarios, thereby highlighting the potential of backpropagation as an adaptive and practical alternative to more complex deep learning approaches for real-time PPE detection in occupational safety monitoring systems.
HORTICULTURE SMART FARMING FOR ENHANCED EFFICIENCY IN INDUSTRY 4.0 PERFORMANCE Arifin, Nurhikma; Insani, Chairi Nur; Milasari, Milasari; Rasyid, Muhammad Furqan
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2728

Abstract

Chili peppers and papayas are important horticultural commodities in Indonesia with high economic value. To enhance productivity and efficiency in cultivating these crops, the application of Smart Farming technology is crucial. This study evaluates the use of image processing and artificial intelligence in the pre-harvest and post-harvest processes for chili peppers and papayas. For the pre-harvest process, data from 50 images of ripe chili peppers on the plant were used. The counting of ripe chilies was performed using HSV color segmentation with two masking processes, resulting in an average accuracy of 82.58%. In the post-harvest phase, 30 images of papayas, consisting of 10 images for each ripeness category—unripe, half-ripe, and ripe—were used. Papaya ripeness classification was carried out using the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel and parameters C = 10 and γ = 10-3, achieving perfect classification accuracy of 100% for all categories. This study underscores the significant potential of Industry 4.0 technologies in enhancing agricultural practices and efficiency in the horticultural sector, providing important contributions to optimizing chili pepper and papaya production.
Co-Authors Agustini, Meti Agustini, Metti Ali Akbar Almazida, Aniskurlila Rizki Anggraini, Tri W Anita agustina Anita Agustina, Anita Arifiandi, Maya Devi Arifin, Nurhikma Ariyani, Herda Chintami, Yulia Dewi, Disa Kamila Diniya, Muhammad Isna Putra Dirgantara, Rona Az Zahra Ellyni Dwi Fortuna evy noorhasanah, evy Evy Noorhasanah, S.Kep.,Ns,M.Imun Fajriani, Ika Fhadali, Mohamad Halimah Halimah Hamidah . Hasanah, Nikmatun Hidayat, Arif Rohman Hidayati Hidayati Insani, Chairi Nur Irvinda, Irvinda Juprianus Rusman Kasful Anwar Us Lias Hasibuan lisnawati, ica Maharani, Windi Maulida Rahmah, Maulida Millati, Rida` Millati, Rida’ Muhammad Furqan Rasyid Mukhawanah, Ulfah Navisa, Zahwa Ni'am, Moh. Widadun Nur Fitriani Maskur Nurfiani, Siti Octarina Hidayatus Sholikhah Pramono, Yosra Sigit Pratama, Muhamad Rifki Puteri, Nadya Putri, Mufida Awalia Reni Prasetia Nurmawati Rini Sulastri Ririn Dewi Lestari, Ririn Dewi Rismaina Putri Rizki, Ahmad Fadhil Rosana Rosana Roslina Roslina, Roslina Rosnawati Rosnawati, Rosnawati Rudwi Hantoro, Ramandha Rusady, Fara Della Salsabila, Irma Shofiana Sandi Saputra Saputra, Dika Alfian Saripuddin, Saripuddin Sarwani Sarwani, Sarwani Silvia, Bhertatri Surayya , Fina Tauhidah, Nor Isna Upa, Samrius Uray, Ferry Haryanto Utama, Muhammad Surya Alif Wahdini, Wahdini Warda Warda Wijayanti, Farah Nur Syafi’ah