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All Journal Jurnal Ergonomi Indonesia (The Indonesian Journal of Ergonomic) Jusikom: Jurnal Sistem Informasi Ilmu Komputer
-, Amalia
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Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Public Health

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Ergonomic Assessment of Manual Material Handling Workers In The Semarang Tofu Industry Utilizing SNI 9011:2021 -, Amalia; Kurniatie, Menik Dwi; Nugroho, Doni Satriyo; Wijaya, Dewa Kusuma
Jurnal Ergonomi Indonesia (The Indonesian Journal of Ergonomic) Vol 10 No 01 (2024): Volume 10 No 01 Tahun 2024
Publisher : Program Studi Magister Ergonomi Fisiologi Kerja Pascasarjana Universitas Udayana Denpasar Bekerjasama dengan Perhimpunan Ergonomi Indonesia (PEI)

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Abstract

In order to produce delectable, nutritious and affordable pricing, tofu products go through a labor-intensive manufacturing process that demands physical exertion and muscular vigor. In Indonesia, particularly in Semarang, Central Java, the tofu manufacturing process is still carried out manually, including the handling of raw soybeans and finished tofu, generally called Manual Material Handling (MMH). Among the tasks performed by MMH workers include lifting soybean, holding soybean extraction, lifting and carrying tofu barrels. Lifting loads range from 10 to 70 kg. The objective of this paper is to assess the degree of ergonomic risks and complains among MMH workers. The method used is quantitative analysis with a cross-sectional research design that refers to SNI 9011: 2021. Research methods to collect data include observations, interviews, and surveys. 80% of respondents complained of experiencing pain. Painful body parts with a high level of frequency and severity are located in the neck, right shoulder, upper back, lower back, arm, and right hand. A score of 9 indicates a high-risk level for MSD concerns. The total score of potential ergonomic risks is in the range of 11–19, with body scores vary from 2 and 8, material handling scores is 8. In order to reduce the risk of ergonomic hazards in MMH activities, risk control efforts are carried out by dividing the load into acceptable limits, lifting loads balance on both sides of the body. Providing assisting devices, such as ergonomic hand trolleys, can handle huge amounts with fewer frequent material handlings. Counseling is carried out for workers to help them because more aware and knowledgeable about how to conduct MMH activities safely.
APPLICATION OF DATA MINING USING THE RANDOM FOREST METHOD TO PREDICT HEART DISEASE Felix, Felix; Sitanggang, Delima; Laia, Yonata; -, Amalia; Radhi, Muhammad; Barus, Ertina Sabarita
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 2 (2024): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM PRIMA)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i2.4801

Abstract

A heart attack is when fatty deposits block the arteries. This causes symptoms such as shortness of breath and chest pain. In addition, obstructed blood flow to the heart can cause damage to the heart muscle. Heart attacks are still the highest cause of death in Indonesia to date. The problem today is that it is tough to predict and identify heart disease. The appropriate method needed to predict heart disease is the Random Forest method. This research aims to calculate the level of accuracy in predicting heart attacks. Based on research and data processing carried out by previous study by comparing two K-Neighbor algorithms, which produced an accuracy value of 83% and the Logistic Regression algorithm produced an accuracy value of 88% and it was found that the Random Forest algorithm had an accuracy of 86.88%. Thus, other algorithms are better at predicting heart attacks than the Random Forest algorithm. Keywords: Heart Attack, Random Forest, Prediction.
Co-Authors Barus, Ertina Sabarita Delima Sitanggang, Delima Dewa Kusuma Wijaya Felix Felix Kurniatie, Menik Dwi Nugroho, Doni Satriyo Radhi, Muhammad Yonata Laia, Yonata