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All Journal JT-IBSI (Jurnal Teknik Ibnu Sina) Scientific Journal of Informatics
Jorvick Steve
Institut Teknologi Batam
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Analisis K3 Dengan Menggunakan Metode JSA Pada Proses Bolting di PT. XYZ Jorvick Steve; Aulia Agung Dermawan
Jurnal Teknik Ibnu Sina (JT-IBSI) Vol. 10 No. 1 (2025): JT-IBSI (Jurnal Teknik Ibnu Sina)
Publisher : Fakultas Teknik Universitas Ibnu Sina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jt-ibsi.v10i1.1142

Abstract

Penelitian ini bertujuan untuk mengidentifikasi potensi bahaya dan merumuskan tindakan pengendalian risiko pada proses bolting di PT. XYZ dengan menggunakan metode Job Safety Analysis (JSA). Aktivitas bolting yang tampak rutin ternyata memiliki berbagai risiko tersembunyi yang dapat menyebabkan kecelakaan kerja jika tidak ditangani dengan tepat. Penelitian ini menggunakan pendekatan deskriptif kualitatif melalui observasi lapangan, wawancara, dan analisis dokumen. Tahapan JSA dilakukan mulai dari identifikasi pekerjaan berisiko, pemetaan langkah kerja, hingga penentuan potensi bahaya dan rekomendasi pengendalian. Hasil menunjukkan bahwa sebagian besar risiko berasal dari bahaya fisik, seperti tertabrak alat berat, kejatuhan beban, dan kerja di ketinggian tanpa perlindungan. Ditemukan pula kelemahan pada inspeksi alat dan kurangnya pemisahan area kerja yang aman. Penerapan JSA terbukti efektif dalam mengenali potensi bahaya dan merumuskan langkah mitigasi yang aplikatif. Penelitian ini menegaskan pentingnya inspeksi rutin, penggunaan APD, pelatihan K3, serta pengawasan aktif sebagai bagian dari strategi manajemen risiko yang berkelanjutan di industri fabrikasi.
Optimizing Inventory Management: Data-Driven Insights from K-Means Clustering Analysis of Prescription Patterns Aulia Agung Dermawan; Ansarullah Lawi; Dimas Akmarul Putera; Dwi Ely Kurniawan; Kuntum Khoiro Ummatin; Jorvick Steve
Scientific Journal of Informatics Vol. 11 No. 3: Special Issue
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.8690

Abstract

Purpose: The goal is to improve how inventory is managed in healthcare by using K-Means clustering to analyze prescription trends. This approach helps ensure better stock availability, streamlines operations, and ultimately increases sales opportunities. Methods: This research applied the K-Means clustering algorithm to analyze a comprehensive dataset of prescription behaviors from XYZ Clinic. By grouping similar prescriptions into clusters, this method highlighted patterns within the data. These insights led to the identification of unique prescription categories, enabling the creation of tailored recommendations for improving inventory management. Result: The analysis showed that Cluster 1 should be prioritized for inventory management due to its high sales potential and consistent prescription patterns. It is recommended to increase stock for the medications in Cluster 1 to improve inventory turnover and streamline clinical operations. These findings underscore the value of K-Means clustering in healthcare, especially for enhancing inventory management and operational efficiency. Novelty: This research presents a novel application of K-Means clustering in healthcare, focusing on prescription patterns and inventory management. While previous studies have primarily used K-Means clustering for areas such as risk assessment and logistics, this study provides valuable data-driven insights to improve inventory management strategies in healthcare. The results highlight how clustering methods can support better decision-making and resource allocation, ultimately leading to greater operational efficiency and improved patient care.
Co-Authors Aulia Agung Dermawan Dimas Akmarul Putera Dwi Ely Kurniawan Kuntum Khoiro Ummatin