Analisis Ergonomi Stasion Kerja Menggunakan Metode Quick Exposure Checklist Pada PT. Sama-Altanmiah Engineering

The application of occupational safety and health is currently widely spread in almost every industrial sector. However, business owners often ignore the informal sector. This of course will have an impact on employee performance, problems related to the risk of work accidents can occur in the company PT. Sama-Altanmiah Engineering, namely an injury to the body posture of employees who carry out the wood production process. Therefore, it is necessary to assess and analyze work posture improvement. The purpose of this study was to determine the exposure level of the worker's body parts and to determine the work station with the highest exposure level. The method used in this research is the Quick Exposure Checklist (QEC). The QEC method can assess risk disorders that occur in the back, shoulders / arms, wrists, and neck and their combination with risk factors for duration, repetitions, static and dynamic work, and the energy required. The results showed that the exposure level of PT. Sama-Altanmiah Engineering ranges from 68 - 91% and the work station with the highest exposure level of 91% is the station where the wood is unloaded. Keselamatan dan Kesehatan Kerja dewasa ini implementasinya telah menyebar secara luas di hampir setiap sektor industri. Namun, penerapan keselamatan dan kesehatan kerja di sektor informal sering kali tidak diperhatikan oleh pemilik usaha. Tentunya akan berakibat pada kinerja karyawan Permasalahan terkait dengan risiko ergonomi yang akan terjadi, harus diadakan penilaian dan analisis perbaikan postur kerja. Hal ini diharapkan dapat diterapkan untuk mengurangi atau menghilangkan risiko cedera yang dialami. Ada beberapa metode untuk menilai risiko kerja, di sini peneliti akan menggunakan metode Quick Exposure Cheklist (QEC). Metode QEC menilai gangguan risiko yang terjadi pada bagian belakang punggung, bahu/lengan, pergelangan tangan, dan leher serta kombinasinya dengan faktor risiko durasi, repetisi, pekerjaan statis dan dinamis, dan tenaga yang dibutuhkan. Lalu didapat setelah hasil penelitian Tingkat exposure level dari PT.SAMA AL-TANMIAH ENGINEERING dari stasiun kerja yang diamati dan diteliti yaitu stasius tempat menurunkan kayu memiliki tingkat exposure level 91%, stasiun pembersih kulit tingkat exposure levelnya yaitu 85%, stasiun mesin penyerutan tingkat exposure levelnya adalah 82%, dan stasiun mesin potong tingkat exposure levenya adalah 68%.

Heterogeneous CPU-GPU Epsilon Grid Joins: Static and Dynamic Work Partitioning Strategies

Given two datasets (or tables) A and B and a search distance $$\epsilon$$ ϵ , the distance similarity join, denoted as $$A \ltimes _\epsilon B$$ A ⋉ ϵ B , finds the pairs of points ($$p_a$$ p a , $$p_b$$ p b ), where $$p_a \in A$$ p a ∈ A and $$p_b \in B$$ p b ∈ B , and such that the distance between $$p_a$$ p a and $$p_b$$ p b is $$\le \epsilon$$ ≤ ϵ . If $$A = B$$ A = B , then the similarity join is equivalent to a similarity self-join, denoted as $$A \bowtie _\epsilon A$$ A ⋈ ϵ A . We propose in this paper Heterogeneous Epsilon Grid Joins (HEGJoin), a heterogeneous CPU-GPU distance similarity join algorithm. Efficiently partitioning the work between the CPU and the GPU is a challenge. Indeed, the work partitioning strategy needs to consider the different characteristics and computational throughput of the processors (CPU and GPU), as well as the data-dependent nature of the similarity join that accounts in the overall execution time (e.g., the number of queries, their distribution, the dimensionality, etc.). In addition to HEGJoin, we design in this paper a dynamic and two static work partitioning strategies. We also propose a performance model for each static partitioning strategy to perform the distribution of the work between the processors. We evaluate the performance of all three partitioning methods by considering the execution time and the load imbalance between the CPU and GPU as performance metrics. HEGJoin achieves a speedup of up to $$5.46\times$$ 5.46 × ($$3.97\times$$ 3.97 × ) over the GPU-only (CPU-only) algorithms on our first test platform and up to $$1.97\times$$ 1.97 × ($$12.07\times$$ 12.07 × ) on our second test platform over the GPU-only (CPU-only) algorithms.