Telematika Pub Date : 2024-02-16 DOI: 10.35671/telematika.v17i1.2774

Raisa Amalia, M. Faisal, Fatma Indriani, Irwan Budiman, Muhammad Itqan Mazdadi, Friska Abadi, Muhammad Meftah Mafazy

{"title":"Identification of Social Media Posts Containing Self-reported COVID-19 Symptoms using Triple Word Embeddings and Long Short-Term Memory","authors":"Raisa Amalia, M. Faisal, Fatma Indriani, Irwan Budiman, Muhammad Itqan Mazdadi, Friska Abadi, Muhammad Meftah Mafazy","doi":"10.35671/telematika.v17i1.2774","DOIUrl":"https://doi.org/10.35671/telematika.v17i1.2774","url":null,"abstract":"The COVID-19 pandemic has permeated the global sphere and influenced nearly all nations and regions. Common symptoms of this pandemic include fever, cough, fatigue, and loss of sense of smell. The impact of COVID-19 on public health and the economy has made it a significant global concern. It has caused economic contraction in Indonesia, particularly in face-to-face interaction and mobility sectors, such as transportation, warehousing, construction, and food and beverages. Since the pandemic began, Twitter users have shared symptoms in their tweets. However, they couldn't confirm their concerns due to testing limitations, reporting delays, and pre-registration requirements in healthcare. The classification of text from Twitter data about COVID-19 topics has predominantly focused on sentiment analysis regarding the pandemic or vaccination. Research on identifying COVID-19 symptoms through social media messages is limited in the literature. The main objective of this study is to identify symptoms using word embedding techniques and the LSTM algorithm. Various techniques such as Word2Vec, GloVe, FastText, and a composite approach are used. LSTM is used for classification, improving upon the RNN technique. Evaluation criteria include accuracy, precision, and recall. The model with an input dimension of 147x100 achieves the highest accuracy at 89%. This study aims to find the best LSTM model for detecting COVID-19 symptoms in social media tweets. It evaluates LSTM models with different word embedding techniques and input dimensions, providing insights into the optimal text-based method for COVID-19 detection through social media texts.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"437 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140454099","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

Deep Learning for Histopathological Image Analysis: A Convolutional Neural Network Approach to Colon Cancer Classification 组织病理学图像分析的深度学习：结肠癌分类的卷积神经网络方法

Telematika Pub Date : 2024-02-16 DOI: 10.35671/telematika.v17i1.2831

Sarifah Agustiani, Yan Rianto

{"title":"Deep Learning for Histopathological Image Analysis: A Convolutional Neural Network Approach to Colon Cancer Classification","authors":"Sarifah Agustiani, Yan Rianto","doi":"10.35671/telematika.v17i1.2831","DOIUrl":"https://doi.org/10.35671/telematika.v17i1.2831","url":null,"abstract":"Colon cancer is a type of cancer that attacks the last part of the human digestive tract. Factors such as an unhealthy diet, low fiber consumption, and high animal protein and fat intake can increase the risk of developing this disease. Diagnosis of colon cancer requires sophisticated diagnostic procedures such as CT scan, MRI, PET scan, ultrasound, or biopsy, which are often time-consuming and require particular expertise. This study aims to classify colon cancer based on histopathological images using a dataset of 10,000 images. This data is divided into 7,950 images for training, 2,000 for testing, and 50 for validation, aiming to achieve effective generalization. The Convolutional Neural Network (CNN) method was applied in this research with a relatively shallow architecture consisting of 4 convolution layers, 2 fully connected layers, and 1 output layer. Research results were evaluated by looking at the accuracy value of 99.55%, precision value of 99.49%, recall of 99.59%, prediction experiments on several images, and loss and accuracy graphs to detect signs of overfitting. However, this research has limitations in determining hyperparameters and layer depth, which was only tested from 1 to 5 convolution layers. Therefore, there are still opportunities for further development, such as applying unique feature extraction before the classification process","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"170 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140454441","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

Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization 情感分析中分类方法的比较分析：特征选择和集合技术优化的影响

Telematika Pub Date : 2024-02-16 DOI: 10.35671/telematika.v17i1.2824

Sarjon Defit, A. Windarto, Putrama Alkhairi

引用次数: 0

Optimizing Clustering of Indonesian Text Data Using Particle Swarm Optimization Algorithm: A Case Study of the Quran Translation 使用粒子群优化算法优化印度尼西亚文本数据的聚类：古兰经》翻译案例研究

Telematika Pub Date : 2024-02-16 DOI: 10.35671/telematika.v17i1.2724

M. D. R. Wahyudi, Agung Fatwanto

{"title":"Optimizing Clustering of Indonesian Text Data Using Particle Swarm Optimization Algorithm: A Case Study of the Quran Translation","authors":"M. D. R. Wahyudi, Agung Fatwanto","doi":"10.35671/telematika.v17i1.2724","DOIUrl":"https://doi.org/10.35671/telematika.v17i1.2724","url":null,"abstract":"The Quran considered the holy book for Muslims, contains scientific and historical facts affirming Islam's truth, beauty, and influence on human life. Consequently, the Quran text and its translations are valuable sources for text mining research, particularly for studying the interrelationship of its verses. One approach to grouping objects using certain algorithms is clustering, with K-Means Clustering being a prominent example. However, clustering results are often suboptimal due to the random selection of centroids. To address this, the study proposes using the Particle Swarm Optimization (PSO) algorithm, which selects centroids based on PSO results. The hybrid PSO algorithm initiates a single iteration of the K-means algorithm. It concludes either upon reaching the maximum iteration limit or when the average shift in the center of the mass vector falls below 0.0001. Evaluation of the clustering results from the three models indicates that the K-Means algorithm produced the lowest Sum of Squared Error (SSE) value of 1032.19. Additionally, the hybrid PSO algorithm generated the highest Silhouette value of 0.258 and the lowest quantization value of 0.00947. Further evaluation using a confusion matrix showed that K-Means clustering had an accuracy rate of 81.7%, K-Means with PSO had 82.5%, and the combination of K-Means with hybrid PSO yielded the highest accuracy rate of 91.1% among the three grouping model.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"76 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140454722","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

Design Automatic Parking Application of Amikom Purwokerto University Amikom purokerto大学自动停车应用程序设计

Telematika Pub Date : 2023-03-01 DOI: 10.31315/telematika.v20i1.8933

Atmaja Jalu Narendra Kisma, Hendra Marcos

{"title":"Design Automatic Parking Application of Amikom Purwokerto University","authors":"Atmaja Jalu Narendra Kisma, Hendra Marcos","doi":"10.31315/telematika.v20i1.8933","DOIUrl":"https://doi.org/10.31315/telematika.v20i1.8933","url":null,"abstract":" Purpose: This study aims to deal with parking problems in the area of Amikom University, Purwokerto. In addition, this research is designed to implement theoretical and practical knowledge that has been obtained in lectures.Design/methodology/approach: In research on parking design applications in the Amikom University area, Purwokerto, library study methods and literature study methods are used. The amount of data can add insight and can make it easier to process data in research.Findings/result: This application will be able to help more Amikom Purwokerto University residents, especially in the Faculty of Computer Science. The use of this application will help find parking areas in FIK areas such as Basement Parking, Front Parking and Field Parking. In addition, security will be helped by this application because if it is implemented, vehicles parked in the reserved area will be tidier and safer. In addition, security does not need to find an empty parking area for users.Originality/value/state of the art: This research focuses on parking system design like previous studies. However, this research focuses more on designing parking applications at Amikom Purwokerto University. ","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75356160","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

Input Variable Selection for Oil Palm Plantation Productivity Prediction Model 油棕种植园生产力预测模型的输入变量选择

Telematika Pub Date : 2023-03-01 DOI: 10.31315/telematika.v20i1.9674

A. P. Suryotomo, A. Harjoko

引用次数: 0

Digital Image Processing to Detect Cracks in Buildings Using Naïve Bayes Algorithm (Case Study: Faculty of Engineering, Halu Oleo University) 利用Naïve Bayes算法的数字图像处理检测建筑物裂缝(案例研究:Halu Oleo大学工程学院)

Telematika Pub Date : 2023-03-01 DOI: 10.31315/telematika.v20i1.8925

Waode Siti Nurul Hassanah, Yunda Lestari, Rizal Adi Saputra

{"title":"Digital Image Processing to Detect Cracks in Buildings Using Naïve Bayes Algorithm (Case Study: Faculty of Engineering, Halu Oleo University)","authors":"Waode Siti Nurul Hassanah, Yunda Lestari, Rizal Adi Saputra","doi":"10.31315/telematika.v20i1.8925","DOIUrl":"https://doi.org/10.31315/telematika.v20i1.8925","url":null,"abstract":"Purpose: To detect cracks in the walls of buildings using digital image processing and the Naïve Bayes Algorithm.Design/methodology/approach: Using the YCbCr color model for the segmentation process and the HSV color model for the feature extraction process. This study also uses the Naïve Bayes Algorithm to calculate the probability of feature similarity between testing data and training data.Findings/result: Detecting cracks is an important task to check the condition of the structure. Manual testing is a recognized method of crack detection. In manual testing, crack sketches are prepared by hand and deviation states are recorded. Because the manual approach relies heavily on the knowledge and experience of experts, it lacks objectivity in quantitative analysis. In addition, the manual method takes quite a lot of time. Instead of the manual method, this research proposes digital-based crack detection by utilizing image processing. This study uses an intelligent model based on image processing techniques that have been processed in the HSV color space. In addition, this study also uses the YcbCr color space for feature extraction and classification using the Naïve Bayes Algorithm for crack detection analysis on building walls. The accuracy of the research test data reached 88.888888888888890%, while the training data achieved an accuracy of 93.333333333333330%.Originality/value/state of the art: This study has the same focus as previous research, namely detecting cracks in building walls, but has different methods and is implemented in case studies.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"815 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79114576","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

Monitoring Development Board based on InfluxDB and Grafana 基于InfluxDB和Grafana的监测开发板

Telematika Pub Date : 2023-03-01 DOI: 10.31315/telematika.v20i1.7643

N. Noprianto, V. N. Wijayaningrum, R. Wakhidah

引用次数: 0

Application of Expert System Identification of Horticultural Plant Diseases with Certainty Factor and Forward Chaining for Smart Village Concept Development 确定性因子与正向链园艺植物病害专家系统识别在智慧村概念发展中的应用

Telematika Pub Date : 2023-03-01 DOI: 10.31315/telematika.v20i1.9358

Damar Wicaksono, Imam Adi Nata

引用次数: 0

Application Random Forest Method for Sentiment Analysis in Jamsostek Mobile Review 随机森林方法在Jamsostek移动评论情感分析中的应用

Telematika Pub Date : 2023-03-01 DOI: 10.31315/telematika.v20i1.8868

Tasya Auliya Ulul Azmi, Luthfi Hakim, D. C. R. Novitasari, W. D. Utami

引用次数: 0

Telematika最新文献