Indonesian Journal of Electrical Engineering and Computer Science最新文献_第8页

Optimizing gula apong production with an IoT-based temperature monitoring system 利用基于物联网的温度监测系统优化古拉贡生产

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1509-1518

S. Sahrani, Dayang Azra Awang Mat, Dyg Norkhairunnisa Abang Zaidel, Kismet Anak Hong Ping

{"title":"Optimizing gula apong production with an IoT-based temperature monitoring system","authors":"S. Sahrani, Dayang Azra Awang Mat, Dyg Norkhairunnisa Abang Zaidel, Kismet Anak Hong Ping","doi":"10.11591/ijeecs.v34.i3.pp1509-1518","DOIUrl":"https://doi.org/10.11591/ijeecs.v34.i3.pp1509-1518","url":null,"abstract":"Determining the quality of gula apong is crucial to optimizing its production, with cooking temperature being a key factor affecting both taste and shelf life. The gula apong industry faced challenges due to the lack of reliable real-time temperature monitoring methods during the cooking process. Traditional approaches were inefficient and inaccurate, leading to difficulties in maintaining consistent product quality and meeting market demands. This highlights the necessity of monitoring the temperature throughout each cooking process. This research aims to develop an internet of things (IoT)- based cooking temperature monitoring system to enhance quality control measures in the production of gula apong. The IoT prototype collects temperature data from the thermocouple sensor, then transmits it to cloud storage through a Wi-Fi communication network, utilizing the Node-RED platform for data processing and analysis. Data obtained from the on-site measurement shows that the optimal temperature for producing standard-quality gula apong is approximately around 165 °C. The recommended boiling temperature for Nipah sap is 140 °C. This IoT system can reduce the cooking time of gula apong to 3 hours from the traditional 4 to 6 hours. Utilizing the data acquired from this study helps the producers not only maintaining the quality of gula apong but also reduce the cooking time and cost.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230883","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

Multi-microgrids system’s resilience enhancement 增强多微电网系统的复原力

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1399-1409

Samira Chalah, Hadjira Belaidi

{"title":"Multi-microgrids system’s resilience enhancement","authors":"Samira Chalah, Hadjira Belaidi","doi":"10.11591/ijeecs.v34.i3.pp1399-1409","DOIUrl":"https://doi.org/10.11591/ijeecs.v34.i3.pp1399-1409","url":null,"abstract":"Nowadays, electricity consumption is increasing rapidly which leads to conventional power systems exhaustion. Therefore, micro-grids (MGs) implantation can enhance the resilience of power systems by implication of new resources, such as renewable energy sources (solar panel and wind power systems), electric vehicles (EV), and energy storage systems (ESS). This paper proposes a new strategy for optimal power consumption inside one microgrid; then, the approach will be extended to optimize the power consumption to enhance the resilience in the case of multi-MGs systems. The system controller of each microgrid has been implemented using ESP32 microcontroller and Raspberry IP4. The proposed approach intends to enhance the resilience of the system to react to any contingency in the system such as loss of power linkage between MG and the network in case of any natural disaster, especially in the rural area. Two controllers are implemented; the first one ensures MG autonomy by the efficient use of its own sources. The second one handles the system resilience cases by demanding/delivering power from/into neighbor microgrids. Hence, this work enhances the system resilience with an optimal cost. Thus, the MG can offer ancillary services for the neighboring MGs.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141233811","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

Automatic translation from English to Amazigh using transformer learning 使用转换器学习从英语到阿马齐格语的自动翻译

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1924-1934

Otman Maarouf, Abdelfatah Maarouf, Rachid El Ayachi, Mohamed Biniz

引用次数: 0

Stacking classifier method for prediction of human body performance 用于预测人体性能的堆叠分类器方法

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1832-1839

Noer Rachmat Octavianto, Antoni Wibowo

{"title":"Stacking classifier method for prediction of human body performance","authors":"Noer Rachmat Octavianto, Antoni Wibowo","doi":"10.11591/ijeecs.v34.i3.pp1832-1839","DOIUrl":"https://doi.org/10.11591/ijeecs.v34.i3.pp1832-1839","url":null,"abstract":"A healthy body is the capital of success and supports human activities. To maintain health, humans need to avoid disease. A healthy life is everyone’s dream and should start early. Busy activities often hinder a healthy lifestyle. Nonetheless, it is important for every individual to lead a healthy lifestyle. Human activities determine health and the implementation of a healthy life. One method that can perform classification with machine learning is extreme gradient boosting (XGBoost). XGBoost is one of the techniques in machine learning for regression analysis and classification based on gradient boosting decision tree (GBDT). By using gradient descent to minimize the error when creating a new model, the algorithm is called gradient boosting. In determining a classification starting from determining the model to the results, usually only using one algorithm method, and combining other methods together with the method is an algorithm called random forest classifier. Among these merging methods are, stacking classifier, voting classifier, and bagging classifier. The conclusion obtained from the results of this research is that the test results show that the stacking classifier achieves the highest accuracy of 76.07%, making it the best method in this research. And the stacking classifier has a precision of 76.96%, recall of 75.83%, and F1-score of 75.81%. This shows that the model has a good balance between the ability to provide true positive results and the ability to recover positive data.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141233923","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

Energy efficient reliable data transmission for optimizing IoT data transmission in smart city 优化智慧城市物联网数据传输的高能效可靠数据传输

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1978-1988

Ruchita Ashwin Desai, Raj Bhimashankar Kulkarni

{"title":"Energy efficient reliable data transmission for optimizing IoT data transmission in smart city","authors":"Ruchita Ashwin Desai, Raj Bhimashankar Kulkarni","doi":"10.11591/ijeecs.v34.i3.pp1978-1988","DOIUrl":"https://doi.org/10.11591/ijeecs.v34.i3.pp1978-1988","url":null,"abstract":"The rapid proliferation of the internet of things (IoT) technology has significantly transformed urban landscapes, giving rise to smart city frameworks that leverage interconnected devices for enhanced efficiency and functionality. In these environments, vast amounts of data are generated by diverse sensors and devices, necessitating advanced strategies for effective data collection and transmission. This paper introduces a novel approach to address data collection and transmission challenges in IoT-enabled smart city frameworks. The proposed design integrates IoT-Cloud for efficient data collection and employs the energy efficient reliable data transmission (EERDT) model, optimizing IoT data transmission. The enhanced dragonfly routing algorithm, incorporating the firefly algorithm, enhances data routing efficiency. Experimental results demonstrate EERDT's superiority over energy-aware iot-routing (EAIR) and location-centric energy-harvesting aware-routing (LCEHAR), revealing significant improvements in communication overhead, data processing latency, and network lifetime. The EERDT exhibits substantial reductions in communication overhead, enhancing overall network performance. The EERDT model showcases lower data processing latency and energy consumption, highlighting its potential for resource-efficient IoT data transmission. This work contributes an innovative solution for smart city IoT networks, emphasizing performance enhancements and resource efficiency.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141234281","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

A device to device driven approach towards optimizing energy efficiency for 6G networks 设备到设备驱动法优化 6G 网络能效

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1682-1689

Sonia Aneesh, A. Shaikh

引用次数: 0

Privacy-preserving authentication approach for vehicular networks 车载网络的隐私保护认证方法

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1674-1681

Chindika Mulambia, Sudeep Varshney, Amrit Suman

{"title":"Privacy-preserving authentication approach for vehicular networks","authors":"Chindika Mulambia, Sudeep Varshney, Amrit Suman","doi":"10.11591/ijeecs.v34.i3.pp1674-1681","DOIUrl":"https://doi.org/10.11591/ijeecs.v34.i3.pp1674-1681","url":null,"abstract":"Vehicle AdHoc networks have an important role in intelligent transport systems that enhance safety in road usage by transmitting real traffic updates in terms of congestion and road accidents. The dynamic nature of the vehicular AdHoc networks make them susceptible to attacks because once malicious users gain access to the network they can transform traffic data. It is essential to protect the vehicular ad hoc network because any attack can cause unwanted harm, to solve this it is important to have an approach that detects malicious vehicles and not give them access to the network. The proposed approach is a privacy preserving authentication approach that authenticates vehicles before they have access to the vehicular network thereby identifying malicious vehicles. The model was executed in docker container that simulates the network in a Linux environment running Ubuntu 20.04. The model enhances privacy by assigning Pseudo IDs to authenticated vehicles and the results demonstrate effectiveness of the solution in that unlike other models it boasts faster authentication and lower computational overhead which is necessary in a vehicular network scenario.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229298","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

Development and implementation of a Python functions for automated chemical reaction balancing 开发和实施用于自动化学反应平衡的 Python 函数

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1557-1565

Pankaj Dumka, Rishika Chauhan, Dhananjay R. Mishra, Feroz Shaik, Pavithra Govindaraj, Abhinav Kumar, Chandrakant Sonawane, V. Velkin

引用次数: 0

Malaria cell identification using improved machine learning and modified deep learning architecture 利用改进的机器学习和修正的深度学习架构识别疟疾细胞

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp2078-2086

Shashikiran S., S. H. D.

{"title":"Malaria cell identification using improved machine learning and modified deep learning architecture","authors":"Shashikiran S., S. H. D.","doi":"10.11591/ijeecs.v34.i3.pp2078-2086","DOIUrl":"https://doi.org/10.11591/ijeecs.v34.i3.pp2078-2086","url":null,"abstract":"Malaria continues to be a serious problem for public health because of its occurrence in tropical and subtropical areas with inadequate healthcare systems and few resources. For prompt intervention and treatment of malaria, effective and precise diagnosis is essential. Professional pathologists examine blood smear films by hand to get a microscopic diagnosis and another way they will do a rapid antigen malaria test which produces the result of 50% accuracy. Convolutional neural network (CNN) is a type of deep learning (DL) model that has been effectively used for a variety of image recognition applications. Our suggested approach uses, improved machine learning (IML) methods like support vector machine (SVM)+principal component analysis (PCA) fit, SVM+t-distributed stochastic neighbor embedding (t-SNE) fit, and CNN architecture with an accuracy of 86.23%, 88.27%, and 97.16% accuracy respectively, to combine feature extraction, data augmentation, and modify the layers by including the SVM algorithm in the final layer of the CNN architecture. The proposed method will significantly reduce pathologists' burden by automating the identification of malaria and improving diagnosis accuracy in resourceconstrained contexts","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230595","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

A novel ensemble approach for Twitter sentiment classification with ML and LSTM algorithms for real-time tweets analysis 一种利用 ML 和 LSTM 算法进行推特情感分类的新型集合方法，用于实时推文分析

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-06-01 DOI: 10.11591/ijeecs.v34.i3.pp1904-1914

Thotakura Venkata Sai Krishna, T. S. Rama Krishna, Srinivas Kalime, Chinta Venkata Murali krishna, S. Neelima, Raja Rao Pbv

{"title":"A novel ensemble approach for Twitter sentiment classification with ML and LSTM algorithms for real-time tweets analysis","authors":"Thotakura Venkata Sai Krishna, T. S. Rama Krishna, Srinivas Kalime, Chinta Venkata Murali krishna, S. Neelima, Raja Rao Pbv","doi":"10.11591/ijeecs.v34.i3.pp1904-1914","DOIUrl":"https://doi.org/10.11591/ijeecs.v34.i3.pp1904-1914","url":null,"abstract":"Social media sentiment classification was an essential consideration in natural language processing (NLP) for evaluating normal people’s perspectives on a given topic. With Twitter’s massive rise in popularity in recent years, the capacity to extract information about public sentiment from tweets became a major focus. This paper not only analyzed public sentiment through data from Twitter but introduced a novel ensemble approach in the methods employed for Twitter sentiment classification. Real-time tweets on various topics, including “covid,” “crime,” “spam,” “flipkart,” “migraine,” and “airlines,” were extracted and thoroughly examined to gain insight into public opinions. Leveraging the Twitter API for real-time tweet extraction, natural language processing techniques were applied to clean the tweet data. Subsequently, we applied several machine learning (ML) algorithms Naïve Bayes, decision tree (DT), random forest (RF), logistic regression (LGR), and deep learning (DL) algorithms recurrent neural network (RNN), LSTM, and GRU individually. Later, we proposed a novel ensemble of ML and DL algorithms for sentiment classification, with a novel emphasis on ensemble techniques and enhanced the accuracy with a significance compared to individual ML or DL model applied. The experimental results demonstrated that our novel ensemble approach achieved high accuracy when compared to existing work.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229874","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