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Classification Heart Diseases Base on Heart Sound Using Backpropagation Algorithm 基于心音反向传播算法的心脏病分类
Journal FORTEI-JEERI Pub Date : 2020-05-26 DOI: 10.46962/FORTEIJEERI.V1I1.4
A. Setyawan, F. Arifin
{"title":"Classification Heart Diseases Base on Heart Sound Using Backpropagation Algorithm","authors":"A. Setyawan, F. Arifin","doi":"10.46962/FORTEIJEERI.V1I1.4","DOIUrl":"https://doi.org/10.46962/FORTEIJEERI.V1I1.4","url":null,"abstract":"Heart sounds have a special pattern that can indicate a person's heart condition. An abnormal heart will produce a characteristic sound that is often called a murmur ( Sitinjak , 2008). Murmurs are caused by various things that can indicate a person's heart condition. From these murmurs can be known the types of abnormalities experienced by patients. In this study, cardiac abnormalities that can be identified are aortic stenosis (as), mitral regurgitation (mr), mitral valve prolapse (mvp), mitral stenosis (ms), and normal. The data used for training as many as 1000 heart sound files consisting of 200 files each for each heart abnormality.Data in the form of heart rate sound samples with the format. Wav. The program was created using the Artificial Neural Network method to identify the five types of cardiac abnormalities. The training method is created using the traingdx function provided in the Neural Network Toolbox on MATLAB. Based on the results of the training can be obtained a validity value of 97,7%.","PeriodicalId":175469,"journal":{"name":"Journal FORTEI-JEERI","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132609099","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
Automatic Watering System for Tomato Plants Based on Soil Moisture Detection 基于土壤水分检测的番茄植株自动浇水系统
Journal FORTEI-JEERI Pub Date : 2020-05-26 DOI: 10.46962/FORTEIJEERI.V1I1.3
S. Praptodiyono, Wawan Setiawan, Sumardi Sadi
{"title":"Automatic Watering System for Tomato Plants Based on Soil Moisture Detection","authors":"S. Praptodiyono, Wawan Setiawan, Sumardi Sadi","doi":"10.46962/FORTEIJEERI.V1I1.3","DOIUrl":"https://doi.org/10.46962/FORTEIJEERI.V1I1.3","url":null,"abstract":"Water is the main element of growth for tomato plants; a further suitable watering system of the plants should be taken into consideration. Tomato plants require water that is under ideal soil moisture conditions so the plants can continue to flourish. Problems occur during the dry season; farmers must pay extra for watering. In order to address the problem, an automatic tomato plant watering system is needed. The system should streamline the use of water as well as the energy and costs of farmers in tomato cultivation. This research aims to find out the watering system prototype to tomato plants that use the solar cell as the energy supplier. The system used a control system with Arduino UNO as soil moisture levels detection. A DC pump is connected to the control system to supply water when law level soil moisture detected. Experiments were done to test the prototype using 25 tomato plants. The results showed the system could control soil moisture 65% - 90% and streamline water usage by 24.71 liters per month. The 50 Wp Solar Cell can charge 20 Ah battery with an average charging current of 0.78 A, and the charging time is 7 hours per day.","PeriodicalId":175469,"journal":{"name":"Journal FORTEI-JEERI","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122928995","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
Performance of Genetic Algorithm-Support Vector Machine (GA-SVM) and Autoregressive Integrated Moving Average (ARIMA) in Electric Load Forecasting 遗传算法-支持向量机(GA-SVM)和自回归综合移动平均(ARIMA)在电力负荷预测中的应用
Journal FORTEI-JEERI Pub Date : 2020-05-22 DOI: 10.46962/FORTEIJEERI.V1I1.8
R. Hasanah, Dicky Indratama, H. Suyono, Mahfudz Shidiq, M. Abdel-Akher
{"title":"Performance of Genetic Algorithm-Support Vector Machine (GA-SVM) and Autoregressive Integrated Moving Average (ARIMA) in Electric Load Forecasting","authors":"R. Hasanah, Dicky Indratama, H. Suyono, Mahfudz Shidiq, M. Abdel-Akher","doi":"10.46962/FORTEIJEERI.V1I1.8","DOIUrl":"https://doi.org/10.46962/FORTEIJEERI.V1I1.8","url":null,"abstract":"The main business focus of an electric power service provider is to meet the consumers’ demand in time and quality as required. The increase of electric load demand is influenced by various factors, for example the development of technology, business, region, standard of life, climatic and weather changes, or even consumers behavior.  They must be considered by the power service provider in order to anticipate the load increase beyond the company’s capability and the existing power generator capacity. This study focuses on comparing the performances of two methods in electric load demand forecasting. The Genetic Algorithm-Support Vector Machine (GA-SVM) and the Autoregressive Integrated Moving Average (ARIMA) methods are applied for the prediction of daily load in Malang city, Indonesia, which is under the service coverage of the Indonesian national electricity provider, PT PLN Sub Unit P3B Jawa Timur-Bali. Two specific influencing factors, temperature and precipitation, are considered. The performance comparison is based on the error parameters of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results of study indicate that the use of GA-SVM method provides better performance than that of the ARIMA method.","PeriodicalId":175469,"journal":{"name":"Journal FORTEI-JEERI","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114549041","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
A Improving Feature Selection on Heart Disease Dataset With Boruta Approach 基于Boruta方法的心脏病数据集特征选择改进
Journal FORTEI-JEERI Pub Date : 2020-05-21 DOI: 10.46962/FORTEIJEERI.V1I1.6
Muhammad Arzanul Manhar, I. Soesanti, N. A. Setiawan
{"title":"A Improving Feature Selection on Heart Disease Dataset With Boruta Approach","authors":"Muhammad Arzanul Manhar, I. Soesanti, N. A. Setiawan","doi":"10.46962/FORTEIJEERI.V1I1.6","DOIUrl":"https://doi.org/10.46962/FORTEIJEERI.V1I1.6","url":null,"abstract":"Coronary artery disease (CAD) is one of the deadliest diseases in the entire world, including in Indonesia. CAD occurs due to narrowing or blockage of coronary arteries which is usually caused by atherosclerosis. Various studies have been conducted with the aim to predict the nature and characteristics of this disease. Some researches uses the Z-Alizadeh Sani dataset which consists of 54 attributes with two results of classification, CAD and Normal to classify its data. Feature selection is one way to reduce the number of attributes that exist by leaving the attributes that have a high effect on the dataset. In this study, the Boruta method is used as a feature selection to minimize the attributes and leave the attributes with high relative with the dataset. By reducing the attributes in the dataset through the feature selection process, sets of 17 and 18 attributes are selected as attributes with high relative with the dataset. These attributes then used to calculate the accuracy value of the dataset using the several classification methods and 90,3% accuracy is obtained from this study.","PeriodicalId":175469,"journal":{"name":"Journal FORTEI-JEERI","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132504119","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: 4
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