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All Journal Techno.Com: Jurnal Teknologi Informasi Industrial Management and Engineering Journal
Rahadian, Yanuar Rafi
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Analisa Potensi Bahaya Dengan Metode HIRA (Hazard Identification And Risk Asessment) Di Departemen Produksi PT. ABC Firia Ferlania, Dianda Aryntya; Rahadian, Yanuar Rafi
IMEJ (Jurnal Teknik dan Managemen Industri) Vol 2 No 1 (2023)
Publisher : Universitas PGRI Ronggolawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55719/imej.v2i2.1032

Abstract

PT. X is one of the fertilizer companies in Indonesia that was built in 1972. There are various departments in this company, but this research only focuses on the production department. There are several potential dangers that need to be identified using the HIRA (Hazard Identification and Risk) method. After processing the data, it was found that the highest score was 12 and there were 3 potential dangers in this department, they are respiratory problems, hearing problems, and falling from the stairs. So the suggestions that can be given are to improve the work environment that has the highest work potential, provide socialization about company regulations, and implement 5S to maintain the cleanliness and comfort the workers.   PT. X merupakan salah satu perusahaan pupuk di Indonesia yang dibangun pada tahun 1972. Ada berbagai macam departemen pada perusahaan ini, namun penelitian ini hanya berfokus pada departemen produksi. Setelah dilakukan studi lapangan, ada beberapa potensi bahaya yang ada sehingga perlu dilakukan identifikasi menggunakan metode HIRA (Hazard Identification and Risk). Setelah dilakukan pengolahan data didapatkan skor tertingginya adalah 12 dan ada 3 potensi bahaya pada departemen ini yaitu pekerja mengalami gangguan pernapasan, pekerja mengalami gangguan pendengaran, dan pekerja yang jatuh dari tangga. Maka dari itu perlu untuk dilakukan perbaikan, sehingga saran yang dapat diberikan adalah memperbaiki lingkungan kerja yang memiliki potensi kerja tertinggi, memberikan sosialisasi tentang peraturan perusahaan, dan menerapkan 5S untuk menjaga kebersihan dan kenyamanan pekerjanya.
Utilization of Big Data For PPE Detection Using Convolutional Neural Network And Yolov8 Bisri, Hasan; Maghfur, Maula Aringga; Rahadian, Yanuar Rafi
Techno.Com Vol. 24 No. 3 (2025): Agustus 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i3.13774

Abstract

Indonesia holds a strategic position in the global manufacturing sector, with a manufacturing output of USD 228.32 billion in 2021, ranking 10th worldwide. In 2023, it ranked 12th globally by manufacturing value added, according to the World Bank’s report. However, this growth is accompanied by 297,725 workplace accidents reported in Indonesia in 2022, marking a 27.03% increase from the previous year. This study aims to develop a Personal Protective Equipment (PPE) monitoring system using Big Data, employing Convolutional Neural Network (CNN) and You Only Look Once (YOLO) algorithms. The dataset consists of at least 1,000 images for each of four classes: Helmet, Vest, NoHelmet, and NoVest. Evaluation results show a mAP@50 of 83.1%, with the highest detection performance in Vest (0.90), followed by NoHelmet (0.88), Helmet (0.85), and NoVest (0.81). These findings demonstrate strong potential in supporting safety protocol compliance and reducing workplace accidents in high-risk industrial environments.   Keywords - Big Data, Convolutonal Neural Network, You Only Look Once
Segmentasi Pelanggan Bisnis Kursus Berbasis LRFM Menggunakan t-SNE, UMAP, dan PaCMAP untuk Optimalisasi Profitabilitas Bisnis Rahadian, Yanuar Rafi; Bisri, Hasan; Amina, Latifa Indirani
Techno.Com Vol. 24 No. 3 (2025): Agustus 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i3.13782

Abstract

Segmentasi pelanggan memainkan peran penting dalam meningkatkan profitabilitas bisnis dengan mengidentifikasi pola perilaku pelanggan. Penelitian ini mengusulkan pendekatan segmentasi berbasis model LRFM (Length, Recency, Frequency, Monetary) untuk meningkatkan ketepatan klasifikasi pelanggan. Dengan menggunakan data transaksi, skor LRFM dihitung dan diklasifikasikan ke dalam empat kategori: Best Customers (121 pelanggan), Loyal Customers, Potential, dan At Risk. Untuk meningkatkan interpretabilitas hasil, tiga teknik reduksi dimensi : t-SNE, UMAP, dan PaCMAP digunakan dalam analisis visual. Di antara ketiganya, UMAP menunjukkan visualisasi paling seimbang berdasarkan indikator separation clarity, cluster compactness, dan outlier identification. Hasil penelitian menunjukkan bahwa segmentasi visual membantu pengambil keputusan dalam mengidentifikasi kelompok pelanggan strategis untuk program retensi dan optimalisasi. Studi ini memberikan referensi berharga bagi industri yang ingin meningkatkan manajemen hubungan pelanggan melalui pendekatan berbasis data.   Kata kunci - Model LRFM, profitabilitas bisnis, reduksi dimensi, segmentasi pelanggan, UMAP
Co-Authors Amina, Latifa Indirani Firia Ferlania, Dianda Aryntya Hasan Bisri Maghfur, Maula Aringga