Article Per Year (5 Year)
p-Index From 2020 - 2025
1.745
P-Index
This Author published in this journals
All Journal Seminar Nasional Informatika (SEMNASIF) Format : Jurnal Imiah Teknik Informatika IJTI (International Journal Of Transportation And Infrastructure) IJEEIT : International Journal of Electrical Engineering and Information Technology Journal of Applied Sciences and Advanced Technology Jurnal Pengabdian kepada Masyarakat Journal of Engineering and Information Technology for Community Service Jurnal Nusantara Berbakti
Hermawan Hermawan
Department Of Computer Science Universtitas Pakuan Bogor
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Journal of Applied Sciences and Advanced Technology

 1 
Environmental Burden Computation in White Crystal Sugar Industry using the Life Cycle Assessment Methods Hermawan Hermawan; Yulian Syahputri; Kotim Subandi; Adriana Sari Aryani
Journal of Applied Sciences and Advanced Technology Vol 3, No 2 (2020): Journal of Applied Sciences and Advanced Technology
Publisher : Faculty of Engineering Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/jasat.3.2.35-44

Abstract

Life Cycle Assessment (LCA) is used to assess environmental impacts that can potentially result from an industrial activity, from cradle to grave. LCA assessment in accordance with the principles of ISO 14040 is carried out starting from the stage of determining the scope, collecting data, preparing the Life Cycle Inventory (LCI), formulating the Life Cycle Impact Assessment (LCIA), interpretation and presentation. At the LCI stage, data from a sugar factory studied is collected from the results of material balance analysis, exhaust gas analysis, liquid waste analysis, and solid waste calculations for the 2019 period. Life Cycle Impact Assessment characterizes each LCI data towards potential environmental impacts that are make it possible. Characterization was carried out by grouping the impacts on Energy Depletion Potential (EDP), global warming (GWP), Eco toxicity aquatic (ETA), terrestrial Eco toxicity (ETT), Abiotic Depletion Potential (ADP), Photochemical Oxydant Formation (POF), Acidification Potential (ACP) , Human Toxicity Potential (HTP), Nutrification Potential (NTP), Ozone Depletion Potential (ODP). The fourth largest contribution to the environmental burden of sugar factories based on 2019 data is GWP 375,966.95 tons of CO2 equivalent, followed by ACP 89,183.03 tons equivalent to NOx, EDP worth 33,086.91 tons of fuel oil equivalent, and NTP of 14,598.66 tons equivalent to COD . In addition, it also needs attention, namely HTP 11,621.83 tonnes equivalent to phenol, ETA 11,163.18 tonnes equivalent to BOD5, and ETT 9,748.49 tonnes equivalent to ash.
Value Chain Analysis Indonesian Animal Husbandry Industry Kotim Subandi; Hermawan Hermawan; Adriana Sari Aryani
Journal of Applied Sciences and Advanced Technology Vol 2, No 1 (2019): Journal of Applied Sciences and Advanced Technology
Publisher : Faculty of Engineering Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3778.56 KB) | DOI: 10.24853/jasat.2.1.21-28

Abstract

At least until 2017, a very low production capacity coupled with livestock methods that are still traditional causes almost all major livestock commodities in Indonesia to close their deficits through imports. Meat, milk, eggs, and even the skin, all of them still cannot be fulfilled entirely from the country.As much as 83% of raw material for the dairy industry is imported. Leather processing industry, importing raw materials for cow leather by 3 million pieces and 13.5 million pieces (sheep and goat skin). Specifically for beef, it was noted that 2016 was the highest volume of beef imports reaching 132.74 thousand tons. As for eggs in general, the pattern of development of export volume is lower than the rate of imports per year. Data on chicken meat imports and their values during the 2012-2016 period showed quite high values compared to export volumes. Knowledge of industry value chains is needed to explore the gap in the dependence of imported raw materials.The value chain analysis carried out in the livestock base industry chain shows a map of the relationship between a number of livestock industry bases in Indonesia so as to facilitate the breakdown of dependence on raw materials. Analysis carried out on the main chain (livestock base) and joint chain (supporting base). Five farm-based industry value chains have been assembled, namely: 1) beef-cattle base industry; 2) industrial livestock-poultry meat base; 3) dairy-based livestock industry; 4) leather-based industry base; and 5) egg-based livestock industry. Core industries or prime movers namely: a) RPH on beef chains, b) industrial pasteurization in the milk chain; c) tanning industry on leather chains; d) food freezing industry on chicken meat chains; and e) egg packing house on the egg chain. The existence of the core industry greatly determines the position of the raw materials of the downstream industry.
Alert Control Model for Exposure of COVID-19 in Industrial Closed Work Space Hermawan Hermawan; Kotim Subandi; Adriana Sari Aryani; Syarif Hidayatullah; Dinar Munggaran Akhmad; Victor Ilyas Sugara
Journal of Applied Sciences and Advanced Technology Vol 5, No 3 (2023): Journal of Applied Sciences and Advanced Technology
Publisher : Faculty of Engineering Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/jasat.5.3.105-112

Abstract

The Covid-19 pandemic has changed the industrial management system, including the regulation of labor. Most of the work in the industry cannot be done through work from home, but must still be in production units. In a number of cases, clusters of COVD 19 were found in the industry, forcing the industry to have to lock down. The problem of the lack of a model for controlling the spread of COVID 19 in the manufacturing industry is the basis for this research. The model designed in this study is also expected to be relevant in the new normal era for the manufacturing industry. The modeling and application of COVID-19 control research in the manufacturing industry is carried out through four stages, namely identification and characterization of work patterns, model design and validation, model implementation and verification, and model comparison testing. At the stage of identification and characterization of work patterns using the methods as guided by the International Labor Organization. The design phase and model validation used the Epidemic Mathematics approach and the Shewhart Control Chart. The application of the model in the industry is in accordance with the guidelines for working in a factory during the COVID-19 Pandemic according to the World Health Organization. The comparative test of the model will be processed using the diversity test. The data used is collected from the company in the form of simple tracing monitoring data for workers before entering the work area and shortly before leaving the work area, COVID 19 test data if any, employee health data, and other data if relevant to support this research. The data obtained is used for model design, both the employee health control model and the COVID-19 distribution model in the work area. The model is made with a scope that is limited only to the industrial work environment, not including outside facilities. Contamination to employees may occur when employees return home or are outside the factory. The model also does not adopt the presence of employees who are being treated for COVID-19 healing in a healing facility. In the Shewhart Control Chart model, it is hoped that a control limit can be obtained that can be used to monitor fluctuations in employee health, the diagram will be designed for daily monitoring of workers. Out-of-control data becomes a warning to carry out a reliability test (Capability) and to trace sources of contamination obtained by employees.
Co-Authors Adriana Sari Aryani Agung Prajuhana Putra Arie Qur’ania Asep Saepulrohman Dinar Munggaran Achmad Dinar Munggaran Akhmad Erniyati Erniyati Fajar Delli Wihartiko Fitria Dewi Sulistiyono Gustian Rama Putra I Wayan Sriyasa Khotim Subandi Kotim Subandi Legis Tsaniyah Legis Tsaniyah Mutiara Prihatini Puspa Citra Puspa Citra Reno Cahyo Hutomo Sawarni Hasibuan Sufiatul Maryana Syarif Hidayatullah Victor Ilyas Sugara Yugo Ahmad Abdillah Yulian Syahputri Yusma Yanti