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

Enhancing learner performance prediction on online platforms using machine learning algorithms 利用机器学习算法加强在线平台上学习者的成绩预测

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp343-353

Mohammed Jebbari, B. Cherradi, S. Hamida, Mohamed-Amine Ouassil, Taoufiq El Harrouti, A. Raihani

{"title":"Enhancing learner performance prediction on online platforms using machine learning algorithms","authors":"Mohammed Jebbari, B. Cherradi, S. Hamida, Mohamed-Amine Ouassil, Taoufiq El Harrouti, A. Raihani","doi":"10.11591/ijeecs.v35.i1.pp343-353","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp343-353","url":null,"abstract":"E-learning has emerged as a prominent educational method, providing accessible and flexible learning opportunities to students worldwide. This study aims to comprehensively understand and categorize learner performance on e-learning platforms, facilitating timely support and interventions for improved academic outcomes. The proposed model utilizes various classifiers (random forest (RF), neural network (NN), decision tree (DT), support vector machine (SVM), and K-nearest neighbors (KNN)) to predict learner performance and classify students into three groups: fail, pass, and withdrawn. Commencing with an analysis of two distinct learning periods based on days elapsed (≤120 days and another exceeding 220 days), the study evaluates the classifiers’ efficacy in predicting learner performance. NN (82% to 96%) and DT (81%-99.5%) consistently demonstrate robust performance across all metrics. The classifiers exhibit significant performance improvement with increased data size, suggesting the benefits of sustained engagement in the learning platform. The results highlight the importance of selecting suitable algorithms, such as DT, to accurately assess learner performance. This enables educational platforms to proactively identify at-risk students and offer personalized support. Additionally, the study highlights the significance of prolonged platform usage in enhancing learner outcomes. These insights contribute to advancing our understanding of e-learning effectiveness and inform strategies for personalized educational interventions.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":"37 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141716493","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

Efficient and robust disaster recovery system using cloud-based algorithms with data integrity 利用基于云的算法实现高效、稳健的灾难恢复系统，并保证数据的完整性

Indonesian Journal of Electrical Engineering and Computer Science Pub Date : 2024-07-01 DOI: 10.11591/ijeecs.v35.i1.pp388-396

Gurumoorthi Gurulakshmanan, R. N. Amarnath

{"title":"Efficient and robust disaster recovery system using cloud-based algorithms with data integrity","authors":"Gurumoorthi Gurulakshmanan, R. N. Amarnath","doi":"10.11591/ijeecs.v35.i1.pp388-396","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp388-396","url":null,"abstract":"Incorporating cloud-based algorithms for disaster recovery (DR), it explores data replication, failover, virtual machine (VM) migration, and consistency algorithms. These algorithms play a pivotal role in safeguarding data and system continuity during unforeseen disruptions. Data replication ensures redundancy, failover algorithms swiftly transition to backup resources, VM migration facilitates resource optimization, and consistency algorithms maintain data integrity. Leveraging cloud technology enhances the effectiveness of these algorithms, providing robust DR solutions critical for business continuity in today's digital landscape. The recent growth in popularity of internet services on a massive scale has also raised the demand for stable underpinnings. Despite the fact that DR for big data is frequently overlooked in security research, the majority of existing approaches use a narrow, endpoint-centric approach. The significance of DR strategies has grown as cloud storage has become the norm for more data. But traditional cloud-centric DR techniques may be expensive, thus less expensive alternatives are being sought. There is persistent concern in the information technology (IT) community about whether or not cloud service providers (CPs) can guarantee data and service continuity in the event of a disaster.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":"12 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141700850","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

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