Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry最新文献_第5页

The Comparison of Some Version of Linear Vector Quantization (LVQ) for Vitamin and Mineral Deficiency Early Detection 几种线性矢量量化(LVQ)在维生素和矿物质缺乏早期检测中的比较

Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry Pub Date : 2020-09-28 DOI: 10.1145/3429789.3429869

N. Sevani, I. A. Soenandi, R. K. Sali

{"title":"The Comparison of Some Version of Linear Vector Quantization (LVQ) for Vitamin and Mineral Deficiency Early Detection","authors":"N. Sevani, I. A. Soenandi, R. K. Sali","doi":"10.1145/3429789.3429869","DOIUrl":"https://doi.org/10.1145/3429789.3429869","url":null,"abstract":"Vitamin and mineral deficiency are often ignored because they do not have a direct impact on body health. However, prolonged deficiency can cause various diseases from mild to serious illness. Some previous research in computer science already conducted to make early detection of vitamin and mineral deficiency, but no one has produced an adaptive model to find out the most dominant type of deficiency. Therefore, the goal of this research is to develop an adaptive model using an artificial neural network (ANN) with Linear Vector Quantization (LVQ) as the learning algorithm to make early detection of vitamin and mineral deficiency. LVQ consists of three layers: an input layer that represents the features, output layer that represent the class label, and the competitive layer. The competitive layer will save the distance between the input vector and the codebook vector from each class. The distance will calculate using Euclidean Distance. LVQ also involves some parameters in the training process, like epsilon value, learning rate, codebook vector, epoch, and window size which obtained by trial and error experiment. This research will also compare the performance of some version of LVQ. The experiment results show that the maximum accuracy level obtained by the system is 85.71% by using LVQ3. The dataset used split into data training and data testing with a ratio 84:16 respectively. From our scenario, the optimum model was achieved by using 20 codebook vectors with the number of epochs is 3400 and the value of the learning rate parameter (&agr;) of 0.4, window size (ō) of 0.3, and epsilon (ε) of 0.2.","PeriodicalId":416230,"journal":{"name":"Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132543368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Occupational Health and Safety Management System in Engineering Faculty of Diponegoro University Using Scrum Model 基于Scrum模型的迪波尼戈罗大学工程学院职业健康与安全管理体系

Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry Pub Date : 2020-09-28 DOI: 10.1145/3429789.3429793

D. Eridani, I. P. Windasari, Risma Septiana, Jojor Kakanda Purba, Fanny Hasbi, Dita Ananda Elisa Reviana

引用次数: 0

User Experience (UX) Evaluation of Online Marketplace for Beauty Care Services: Case Study Kerenaja 美容服务在线市场的用户体验评价:案例研究Kerenaja

Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry Pub Date : 2020-09-28 DOI: 10.1145/3429789.3429828

Arvan Halim, Kho I Eng, James Purnama

引用次数: 0

A Hybrid of Seasonal Autoregressive Integrated Moving Average (SARIMA) and Decision Tree for Drought Forecasting 基于季节自回归综合移动平均(SARIMA)和决策树的干旱预报

Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry Pub Date : 2020-09-28 DOI: 10.1145/3429789.3429870

Yasnita, E. Sutoyo, Ahmad Musnansyah

{"title":"A Hybrid of Seasonal Autoregressive Integrated Moving Average (SARIMA) and Decision Tree for Drought Forecasting","authors":"Yasnita, E. Sutoyo, Ahmad Musnansyah","doi":"10.1145/3429789.3429870","DOIUrl":"https://doi.org/10.1145/3429789.3429870","url":null,"abstract":"Drought is one of the triggers for forest fires due to depletion of surface water reserves. Along with the frequent drought, the incidence of forest fires has also increased. Therefore, it is important to know or forecast drought to take precautions. In this study, drought forecasting was carried out by applying the concept of data mining classification methods and forecasting methods. This classification uses the decision tree (CART) method, which is a method that aims to see the rules resulting from the classification of existing data. While forecasting uses the SARIMA method, this method is used to predict the factors that cause drought (temperature, humidity, and rainfall). Furthermore, the rule of the classification results is used to classify the results of forecasts. Based on the implementation of the CART algorithm which is evaluated using a confusion matrix is able to achieve an accuracy of 91.33%. Based on the implementation of the SARIMA method, a model is obtained for each variable to build forecasting. Each model was selected based on AIC criteria, and evaluated using MSE. The optimal model for temperature (Tx) is SARIMA (1, 1, 0) x (0, 1, 1, 12) with the MSE value of 0.15. While the selected model for humidity (RH_avg) is SARIMA (0, 1, 1) x (1, 1, 1, 12) with the MSE value of 3.85, and the optimal model for rainfall (RR) is SARIMA (0, 1, 1) x (0, 1, 1, 12) with the MSE value of 8.61.","PeriodicalId":416230,"journal":{"name":"Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128565032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

New Product Development with Kano Model to Supports Supply Chain Performance of Jewelry Industry in Indonesia 用Kano模型开发新产品以支持印尼珠宝行业的供应链绩效

Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry Pub Date : 2020-09-28 DOI: 10.1145/3429789.3429873

Jennifer, M. Hartono

引用次数: 2

Analysis of Gravitational Water Vortex Turbine Performance 重力水涡轮机性能分析

Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry Pub Date : 2020-09-28 DOI: 10.1145/3429789.3429862

D. L. Zariatin, Tri Murniati, D. Antoro

{"title":"Analysis of Gravitational Water Vortex Turbine Performance","authors":"D. L. Zariatin, Tri Murniati, D. Antoro","doi":"10.1145/3429789.3429862","DOIUrl":"https://doi.org/10.1145/3429789.3429862","url":null,"abstract":"Hydropower is one of the renewable energy widely available in countries. The resource of hydropower in a remote area is promising. Many rivers with low heads have not been utilized. Simple construction and low manufacturing cost are necessary to build a hydropower plant in a remote area. Vortex turbine is one hydropower turbine type that can generate electric power with low heads and suitable for remote areas. Vortex turbine is utilizing the water flow kinetic force of a whirlpool and transforms into shaft rotation. In this research, a laboratory-scale vortex turbine power plant was developed. Three different runners made of rust-resistant materials, SS-304, AA-5057, and Polyvinyl Chloride (PVC), were tested and generated electric power of 3.98 W, 3.47 W, and 3.3 W, respectively. It can turn on a 3 W LED lamp. Compared to the calculated potential hydraulic power of 12.5 Watt, the maximum efficiency is up to 31.8%. The runner weight significantly affects the electric power generated. Havier runner has a more significant moment of inertia, which results in higher torque, ultimately more power, as long as the flowing water can rotate the runner.","PeriodicalId":416230,"journal":{"name":"Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132274121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Ergonomics and Workstyle Risk Factors Analysis of Musculoskeletal Disorders in Students Conducting Distance Learning 远程教育学生肌肉骨骼疾病的工效学及工作方式风险因素分析

Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry Pub Date : 2020-09-28 DOI: 10.1145/3429789.3429796

Mimin Edwar, B. N. Moch, E. Muslim

{"title":"Ergonomics and Workstyle Risk Factors Analysis of Musculoskeletal Disorders in Students Conducting Distance Learning","authors":"Mimin Edwar, B. N. Moch, E. Muslim","doi":"10.1145/3429789.3429796","DOIUrl":"https://doi.org/10.1145/3429789.3429796","url":null,"abstract":"Working ergonomically can reduce the risk of pain, increase the sense of comfort at work, reduce stress and cause increased productivity. However, working ergonomically has not been implemented maximally when working, especially people who work in the office, where the fact that skeletal muscle disorders are the biggest enemy of office workers, around 40--50% of workers report complaints. And worse conditions are predicted while working from home. Musculoskeletal Disorders (MSDs) related to work are disorders of the musculoskeletal system caused or exacerbated by the interaction of the work environment. Musculoskeletal Disorders can be caused by the contribution of various risk factors including individual factors, occupational or biomechanical factors and psychosocial factors. This study wanted to find out ergonomic risk factors and work styles for musculoskeletal disorders in students especially those carrying out distance learning. Structural Equation Modeling (SEM) method is used to process the Workstyle model with the addition of work posture factors, and musculoskeletal pain. Structural Equation Modeling (SEM) is a statistical method used to test the relationship between variables, such as between manifest and latent variables, the relationship between latent variables, and measuring measurement error variables on several independent and dependent variables in a model (Hair, 2010). The analysis was carried out by distributing questionnaires to 200 respondents. Respondents are shown to UI students who are carrying out distance learning and using laptops or computers while doing work / assignments. The results of the study indicate that there are several workstyle factors that contribute to student musculoskeletal disorders.","PeriodicalId":416230,"journal":{"name":"Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130410866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Usability Evaluation of "Inventory Information System" Design of Disaster Management in Yogyakarta Province - Indonesia 印尼日惹省灾害管理“库存信息系统”设计的可用性评估

Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry Pub Date : 2020-09-28 DOI: 10.1145/3429789.3429843

N. Handayani, W. Kusuma, Z. F. Rosyada, Y. Widharto, Ajeng Hanifah

引用次数: 0

Designing Strategies for Inbound Logistics Performance Indicator Improvement in the Indonesian Mining Industry 印尼矿业进境物流绩效指标改进策略设计

Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry Pub Date : 2020-09-28 DOI: 10.1145/3429789.3429849

Fadhilah M Karimah, I. M. Hakim

{"title":"Designing Strategies for Inbound Logistics Performance Indicator Improvement in the Indonesian Mining Industry","authors":"Fadhilah M Karimah, I. M. Hakim","doi":"10.1145/3429789.3429849","DOIUrl":"https://doi.org/10.1145/3429789.3429849","url":null,"abstract":"Inbound logistic activity in the mining company has an essential role in receiving, store, and disseminate input from suppliers to the point of use in production operations. In the case of one of the mining companies in Indonesia, inbound logistic performance has not yet reached the optimal target. This research builds an appropriate method to measure inbound logistic processes and then design strategies based on the results of the most critical performance indicators to improve performance indicators in the inbound logistic processes. The study begins with the collection of performance indicators and criteria for measuring the performance of the mining company's inbound logistic processes. The first questionnaire is filled and processed to determine the chosen performance indicators for the study. To assess the relationships among the performance indicators, the second questionnaire is filled. The results are then processed using Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Network Process (ANP) to confirm the causal relationship, identify the major performance indicators, and develop strategies accordingly. Based on the result, vessel capacity utilization (31.24%) is the first performance indicator that should be the priority, followed by material availability (12.18%), delivery cycle time (12.01%), and performance in making purchase orders (10.92%). The proposed strategies recommendations are container space optimization, a specialized standard operating procedure for fast-moving goods, implementation of Radio Frequency Identification (RFID) technology for each handling unit, and differentiation in purchasing strategy based on purchasing portfolio.","PeriodicalId":416230,"journal":{"name":"Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132020690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Demand Forecasting for Drinking Water Products to Reduce Gap Between Estimation and Realization of Demand Using Artificial Neural Network (ANN) Methods in PT. XYZ 用人工神经网络(ANN)方法进行饮用水产品需求预测以减少需求估算与实现之间的差距

Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry Pub Date : 2020-09-28 DOI: 10.1145/3429789.3429844

R. Syafitri, A. Ridwan, Nia Novitasari

{"title":"Demand Forecasting for Drinking Water Products to Reduce Gap Between Estimation and Realization of Demand Using Artificial Neural Network (ANN) Methods in PT. XYZ","authors":"R. Syafitri, A. Ridwan, Nia Novitasari","doi":"10.1145/3429789.3429844","DOIUrl":"https://doi.org/10.1145/3429789.3429844","url":null,"abstract":"Supply Chain has components such as vendors, manufacturers, factories, warehouses retailers, customers, etc. Every relationship between components must have good information in order to create informed business decisions. Sales forecast are part of a decline in supply chain function and are a way to predict future product sales. The large gap between demand forecasting and actual demand proves that the forecasting method used in forecasting is not quite right so it can cause high error rates. In this study, the calculation of demand forecasting using the Artificial Neural Network (ANN) method was chosen as a good method because ANN learning method that works through an iterative process using training data comparing the predicted value of the network each sample of data and the weight of the network relation in each process is modified to minimize the value of Mean Squared Error (MSE). With the right parameters and good training in the data, the error number at the ANN calculation output using MATLAB will produce demand forecasting numbers that are getting closer to the actual demand numbers. The application of the ANN method to demand forecasting can make improvements to the error value performance using the MSE, MAD equation. and MAPE. The decline in MSE in 2018 from 1,894,299,389 to 26,612,567, in 2019 from 1,035,177,794 to 16,889,433, and in 2020 from 426,876,921 to 2,647,350. The decline in MAD in 2018 from 42,089 to 3,324, in 2019 from 26,924 to 2,888, and in 2020 from 20,661 to 1,627. MAPE reduction in 2018 from 23% to 2%, 2019 from 15% to 2%, and in 2020 from 11% to 1%.","PeriodicalId":416230,"journal":{"name":"Proceedings of the 2021 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125096098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1