Indonesian Journal of Electrical Engineering and Computer Science最新文献_第5页

Hardware implementation of type-2 fuzzy logic control for single axis solar tracker 单轴太阳能跟踪器的 2 型模糊逻辑控制的硬件实现

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp102-112

A. Krismanto, Radimas Putra Muhammad Davi Labib, H. Setiadi, Abraham Lomi, Muhammad Abdillah

引用次数: 0

Hyperspectral image construction in different spectral bands of tea leafs for identifying the tea type using O-ConvNet-RF model 利用 O-ConvNet-RF 模型构建不同光谱波段的茶叶高光谱图像以识别茶叶类型

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp301-309

Likitha Gongalla, Monali Bordoloi

{"title":"Hyperspectral image construction in different spectral bands of tea leafs for identifying the tea type using O-ConvNet-RF model","authors":"Likitha Gongalla, Monali Bordoloi","doi":"10.11591/ijeecs.v35.i1.pp301-309","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp301-309","url":null,"abstract":"Tea, a commonly consumed beverage, is susceptible to being sold in adulterated or expired forms by third-party vendors. Hyperspectral imaging across different wavelength bands has proven to precisely assess the diverse types of tea and their corresponding financial gains. This study aims to employ a deep learning methodology in conjunction with hyperspectral imaging for efficiently classifying tea leaves. A novel approach is proposed, wherein a waveband convolutional neural network is utilized to generate hyper spectral images of tea leaf samples with enhanced resolution. The model known as optimized-convolutional neural network-random forest O- (ConvNet-RF) demonstrated exceptional performance, achieving high accuracy, impressive recall, F1 score, and notable sensitivity rate, outperforming existing alternative methods. The tea leaf types, namely green, yellow, and black, were accurately identified using a combination of the random forest (RF) model and the O-ConvNet-RF model. The tree-based classification method for the identification of tea leaves demonstrated superior performance as compared to alternative machine learning models. In general, this study presents a successful methodology for the classification of tea leaves, with potential implications for consumer processing and distributor profit analysis.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":"17 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141698451","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

Real-time monitoring system for blood pressure monitoring based on internet of things 基于物联网的血压实时监测系统

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp62-69

Gusti Made, Ngurah Desnanjaya, I. Made, Aditya Nugraha

引用次数: 0

Deep learning for economic transformation: a parametric review 深度学习促进经济转型：参数审查

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp520-541

Usman Tariq, Irfan Ahmed, Muhammad Attique Khan, Ali Kashif Bashir

{"title":"Deep learning for economic transformation: a parametric review","authors":"Usman Tariq, Irfan Ahmed, Muhammad Attique Khan, Ali Kashif Bashir","doi":"10.11591/ijeecs.v35.i1.pp520-541","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp520-541","url":null,"abstract":"Deep learning (DL) is increasingly recognized for its effectiveness in analyzing and forecasting complex economic systems, particularly in the context of Pakistan's evolving economy. This paper investigates DL's transformative role in managing and interpreting increasing volumes of intricate economic data, leading to more nuanced insights. DL models show a marked improvement in predictive accuracy and depth over traditional methods across various economic domains and policymaking scenarios. Applications include demand forecasting, risk evaluation, market trend analysis, and resource allocation optimization. These processes utilize extensive datasets and advanced algorithms to identify patterns that traditional methods cannot detect. Nonetheless, DL's broader application in economic research faces challenges like limited data availability, complexity of economic interactions, interpretability of model outputs, and significant computational power requirements. The paper outlines strategies to overcome these barriers, such as enhancing model interpretability, employing federated learning for better data privacy, and integrating behavioral and social economic theories. It concludes by stressing the importance of targeted research and ethical considerations in maximizing DL's impact on economic insights and innovation, particularly in Pakistan and globally.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":"101 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141713583","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 slotted synchronization approach in LoRaWAN LoRaWAN 中的高能效插槽同步方法

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp203-212

Eva Shayo, Abdi T. Abdalla, A. Mwambela, Tole Sutikno

{"title":"Energy efficient slotted synchronization approach in LoRaWAN","authors":"Eva Shayo, Abdi T. Abdalla, A. Mwambela, Tole Sutikno","doi":"10.11591/ijeecs.v35.i1.pp203-212","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp203-212","url":null,"abstract":"In recent years, long-range wide-area networks (LoRaWAN) have gained much attention as low-power wide-area networks. LoRaWAN uses ALOHA as the medium access control protocol, where the end devices transmit data randomly and retransmit it up to eight times if collisions occur. ALOHA is not energy efficient and works perfectly for a smaller network. Several techniques, including the use of synchronization and scheduling schemes, to deal with the limitations imposed by ALOHA in LoRaWAN have been reported in the literature. However, the existing synchronization and scheduling algorithms transmit synchronization messages randomly using one super frame with fixed time slots that accommodate devices using different spreading factors, which limit the LoRaWAN network's scalability. This work proposes a slotted synchronization mechanism for transmitting synchronization requests to the gateway. The performance of the slotted synchronization was evaluated through simulation using packet delivery ratio (PDR) and energy efficiency as the performance parameters. The results indicate that when the number of devices in the network increases, a time-slotted synchronization consumes less energy, on average, by about 0.2 mAh. The use of a slotted synchronization can improve the energy efficiency of the end devices as collisions are completely avoided, achieving a PDR of 100%.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":"164 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141694921","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 of a payload for monitoring biological samples in microgravity and hypergravity conditions 开发用于在微重力和超重力条件下监测生物样本的有效载荷

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp78-89

Elber E. Canto-Vivanco, Sebastian Ramos-Cosi, Victor N. Romero-Alva, N. Vargas-Cuentas, A. Roman-Gonzalez

引用次数: 0

Development of virtual tour reality using 360-degree panoramic images and Leaflet JavaScript 利用 360 度全景图像和传单 JavaScript 开发虚拟旅游实景

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp655-664

L. N. Amali, Muhammad Rifai Katili, Alif Perdana Sugeha

{"title":"Development of virtual tour reality using 360-degree panoramic images and Leaflet JavaScript","authors":"L. N. Amali, Muhammad Rifai Katili, Alif Perdana Sugeha","doi":"10.11591/ijeecs.v35.i1.pp655-664","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp655-664","url":null,"abstract":"This paper describes virtual reality (VR) development using a 360-degree panoramic and Leaflet JavaScript (Leaflet JS) to introduce campus buildings in real-time. The campus building of Universitas Negeri Gorontalo (UNG) in Bone Bolango Regency was chosen as a case study. It allows users to navigate and listen to background sound and narration, open the site map interactively, and read brief information about each location. Each panorama contains hotspots that allow users to explore further. All images are combined using a photo-stitching technique to produce a panoramic image. The research method used is the multimedia development life cycle (MDLC), which consists of six stages: concept, design, material collection, assembly, testing, and distribution. Based on the system usability scale (SUS) test, the virtual tour reality website application received feedback from users regarding its usability, satisfaction, and effectiveness, and it is interesting to use this application. The results show that the website application can visualize the campus building environment with various layers of information and can create a very realistic and detailed representation of the campus environment.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141700614","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-layer perceptron hyperparameter optimization using Jaya algorithm for disease classification 利用 Jaya 算法优化疾病分类的多层感知器超参数

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp620-630

Andien Dwi Novika, A. S. Girsang

引用次数: 0

Extracting contextual insights from user reviews for recommender systems: a novel method 为推荐系统从用户评论中提取语境洞察：一种新方法

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp542-550

Rabie Madani, Abderrahmane Ez-Zahout, F. Omary

{"title":"Extracting contextual insights from user reviews for recommender systems: a novel method","authors":"Rabie Madani, Abderrahmane Ez-Zahout, F. Omary","doi":"10.11591/ijeecs.v35.i1.pp542-550","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp542-550","url":null,"abstract":"Recommender systems (RS) primarily rely on user feedback as a core foundation for making recommendations. Traditional recommenders predominantly rely on historical data, which often presents challenges due to data scarcity issues. Despite containing a substantial wealth of valuable and comprehensive knowledge, user reviews remain largely overlooked by many existing recommender systems. Within these reviews, there lies an opportunity to extract valuable insights, including user preferences and contextual information, which could be seamlessly integrated into recommender systems to significantly enhance the accuracy of the recommendations they provide. This paper introduces an innovative approach to building context-aware RS, spanning from data extraction to ratings prediction. Our approach revolves around three essential components. The first component involves corpus creation, leveraging Dbpedia as a data source. The second component encompasses a tailored named entity recognition (NER) mechanism for the extraction of contextual data. This NER system harnesses the power of advanced models such as bidirectional encoder representations from transformers (BERT), bidirectional long short term memory (Bi-LSTM), and bidirectional conditional random field (Bi-CRF). The final component introduces a novel variation of factorization machines for the prediction of ratings called contextual factorization machines. Our experimental results showcase robust performance in both the contextual data extraction phase and the ratings prediction phase, surpassing the capabilities of existing state-of-the-art methods. These findings underscore the significant potential of our approach to elevate the quality of recommendations within the realm of context-aware recommender systems.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":"40 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141709788","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

Review on integration of ontology and deep learning in cultural heritage image retrieval 本体论与深度学习在文化遗产图像检索中的融合综述

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp583-592

Fikri Budiman, E. Sugiarto, Novi Hendriyanto

{"title":"Review on integration of ontology and deep learning in cultural heritage image retrieval","authors":"Fikri Budiman, E. Sugiarto, Novi Hendriyanto","doi":"10.11591/ijeecs.v35.i1.pp583-592","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp583-592","url":null,"abstract":"Image retrieval methods are currently developing towards big data processing. The literature review is focused on image big data extraction with cultural heritage domain as training and testing datasets. The development of image retrieval process starts from content-based using machine algorithms, deep learning to ontology-based. Image recognition research with cultural heritage domain is conducted because of the importance of preserving and appreciating cultural heritage, in this case, cultural heritage images such as Indonesian Batik are discussed. Batik motif images are Indonesian cultural heritage that has thousands of motifs that are grouped into many classes with a non-linear hyperplane. The problem is focused on processing big data that has many classes. Currently research is evolving into knowledge-based image retrieval using ontologies due to semantic gap constraints. The results of this literature study can be the basis for developing research on the application of appropriate deep learning algorithms so as to utilize the hierarchy of classes and subclasses of image ontologies with cultural heritage domains.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":"28 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141713890","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