MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer最新文献_第2页

Feature Selection on Grouping Students Into Lab Specializations for the Final Project Using Fuzzy C-Means 使用模糊 C-Means 对学生进行毕业设计实验室专业分组的特征选择

MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Pub Date : 2023-11-20 DOI: 10.30812/matrik.v23i1.3341

Indradi Rahmatullah, Gibran Satya Nugraha, Arik Aranta

{"title":"Feature Selection on Grouping Students Into Lab Specializations for the Final Project Using Fuzzy C-Means","authors":"Indradi Rahmatullah, Gibran Satya Nugraha, Arik Aranta","doi":"10.30812/matrik.v23i1.3341","DOIUrl":"https://doi.org/10.30812/matrik.v23i1.3341","url":null,"abstract":"The student’s Final Project is critical as a requirement to graduate from the University. In the PSTI at Mataram University, each student is required to choose a specialization lab to focus on the final project topic that they will work on. From the questionnaire, 57.7% of students answered that it is difficult to select a lab, and others answered that they prefer to determine the labs based on the grades of the courses that represent each lab. This research aimed to group and analyze students in the final project specialization lab by using the main method, namely Fuzzy C-Means (FCM). The methods used were FCM for clustering, Silhouette Coefficient for analysis of cluster quality results, Pearson Correlation, and Principal Component Analysis for the feature selection processing. The results of this study showed that the FCM method followed by a method for feature selection has better results than previous studies that used the K-Means method without feature selection; with this research result using 131 data, the cluster validation result is 0.501, after feature selection using Pearson correlation is 0.534. Thus, Fuzzy C-Means followed by the right feature selection method can group students into specialization laboratories with good results and can be further developed.","PeriodicalId":364657,"journal":{"name":"MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer","volume":"89 3-4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139255096","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

Gender Classification of Twitter Users Using Convolutional Neural Network 使用卷积神经网络对 Twitter 用户进行性别分类

MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Pub Date : 2023-11-09 DOI: 10.30812/matrik.v23i1.3318

F. A. Mubarok, M. Reza Faisal, D. Kartini, D. T. Nugrahadi, T. H. Saragih

{"title":"Gender Classification of Twitter Users Using Convolutional Neural Network","authors":"F. A. Mubarok, M. Reza Faisal, D. Kartini, D. T. Nugrahadi, T. H. Saragih","doi":"10.30812/matrik.v23i1.3318","DOIUrl":"https://doi.org/10.30812/matrik.v23i1.3318","url":null,"abstract":"Social media has become a place for social media analysts to obtain data to gain deeper insights and understanding of user behavior, trends, public opinion, and patterns associated with social media usage. Twitter is one of the most popular social media platforms where users can share messages or ”tweets” in a short text format. However, on Twitter, user information such as gender is not shown, but without realizing it or not, there is information about it in an unstructured manner. In social media analytics, gender is one of the important data that someone likes, so this research was conducted to determine the best accuracy for gender classification. The purpose of this study was to determine whether using combined data can improve the accuracy of gender classification using data from Twitter, tweets, and descriptions. The method used was word vector representation using word2vec and the application of a 2D Convolutional Neural Network (CNN) model. Word2vec was used to generate word vector representations that take into account the context and meaning of words in the text. The 2D CNN model extracted features from the word vector representation and performed gender classification. The research aimed to compare tweet data, descriptions, and a combination of tweets and descriptions to find the most accurate. The result of this study was that combined data between tweets and","PeriodicalId":364657,"journal":{"name":"MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139282130","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

Lungs X-Ray Image Segmentation and Classification of Lung Disease using Convolutional Neural Network Architectures 利用卷积神经网络架构进行肺部 X 光图像分割和肺病分类

MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Pub Date : 2023-11-09 DOI: 10.30812/matrik.v23i1.3133

B. Suprihatin, Yuli Andriani, F. N. Kurdi, Anita Desiani, Ibra Giovani Dwi Putra, Muhammad Akmal Shidqi

{"title":"Lungs X-Ray Image Segmentation and Classification of Lung Disease using Convolutional Neural Network Architectures","authors":"B. Suprihatin, Yuli Andriani, F. N. Kurdi, Anita Desiani, Ibra Giovani Dwi Putra, Muhammad Akmal Shidqi","doi":"10.30812/matrik.v23i1.3133","DOIUrl":"https://doi.org/10.30812/matrik.v23i1.3133","url":null,"abstract":"Lung disease is one of the biggest causes of death in the world. The SARS-CoV-2 virus causes diseases like COVID-19, and the bacteria Streptococcus sp., which causes pneumonia, are two sample causes of lung disease. X-ray images are used to detect the lung disease. This study aimed to combine the stages of segmentation and classification of lung disease. This study in segmentation aims to separate the features contained in the lung images. The classification aimed to provide holistic information on lung disease. This research method used the Deep Residual U-Net (DrU-Net) segmentation architecture and the Deep Residual Neural Network (DResNet) classification architecture. DrU-Net is a modified U-Net architecture with dropout added in its convolutional layers. DResNet is a modified Residual Network (ResNet) architecture with dropout added in its convolutional block layers. The result of this study was segmentation using the DrU-Net architecture obtained 99% for accuracy, 98% for precision, 98% for recalls, 98% for F1-Score, and 96.1% for IoU. The classification results of the segmented images using the DResNet architecture obtained 91% for accuracy, 86% for precision, 85% for recalls, and 84% for F1-Score. The performance results of DrU-Net architecture were excellent and robust in image segmentation. Unfortunately, the average performance of DResNet in classification was still below 90%. These results indicate that Dres-Net performs well in classifying lung disorders in 3 labels, namely Covid, Normal, and Pneumonia, but still needs improvement.","PeriodicalId":364657,"journal":{"name":"MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer","volume":"172 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139282076","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

Hyperparamaters Fine Tuning for Bidirectional Long Short Term Memory on Food Delivery 超配位体对食物传递的双向长短期记忆进行微调

MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Pub Date : 2023-11-08 DOI: 10.30812/matrik.v23i1.3084

Rahman Rahman, Teguh Iman Hermanto, Meriska Defriani

引用次数: 0

Comparison of the Karney Polygon Method and the Shoelace Method for Calculating Area 卡尼多边形法与鞋带法计算面积的比较

MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Pub Date : 2023-11-07 DOI: 10.30812/matrik.v23i1.2929

Vikky Aprelia Windarni, Adi Setiawan, Atina Rahmatalia

{"title":"Comparison of the Karney Polygon Method and the Shoelace Method for Calculating Area","authors":"Vikky Aprelia Windarni, Adi Setiawan, Atina Rahmatalia","doi":"10.30812/matrik.v23i1.2929","DOIUrl":"https://doi.org/10.30812/matrik.v23i1.2929","url":null,"abstract":"In calculating the area of an area, latitude and longitude coordinates are based on data from Global Administrative Region Database and Google Earth can be used. The aim of this research is to calculate the area. This research uses the Karney and Shoelace method to determine its accuracy based on Median Absolute Percentage Error in calculating the area of an area. Median Absolute Percentage Error results use data based on Global Administration The Regional Database by applying the polygon method proposed by Karney is 18.73%, and the percentage is 18.19% by applying the Shoelace method. Based on Google Earth data, implementation the method proposed by Karney obtained a percentage of 19.14%, and the application of shoelaces method obtained a percentage of 19.72%. In this case, Karney polygons and the Shoelace method has good accuracy because the value is below 20%. The proposed Shoelace method is easier to perform understand compared to the Karney method for calculating land area because it uses the Universal Transverse Mercator coordinate system, which projects points on the Earth's surface onto a flat plane.","PeriodicalId":364657,"journal":{"name":"MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139287593","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

Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning 利用深度学习识别胸部 X 光片中的肺炎感染

MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Pub Date : 2023-10-10 DOI: 10.30812/matrik.v23i1.3197

N. Saraswati, I. W. D. Suryawan, Ni Komang Tri Juniartini, I. Muku, Poria Pirozmand, Weizhi Song

{"title":"Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning","authors":"N. Saraswati, I. W. D. Suryawan, Ni Komang Tri Juniartini, I. Muku, Poria Pirozmand, Weizhi Song","doi":"10.30812/matrik.v23i1.3197","DOIUrl":"https://doi.org/10.30812/matrik.v23i1.3197","url":null,"abstract":"One of the diseases that attacks the lungs is pneumonia. Pneumonia is inflammation and fluid in the lungs making it difficult to breathe. This disease is diagnosed using X-Ray. Against the darker background of the lungs, infected tissue shows denser areas, which causes them to appear as white spots called infiltrates. In the image processing approach, pneumonia-infected X-rays can be detected using machine learning as well as deep learning. The convolutional neural network model is able to recognize images well and focus on points that are invisible to the human eye. Previous research using a convolutional neural network model with 10 convolution layers and 6 convolution layers has not achieved optimal accuracy. The aim of this research is to develop a convolutional neural network with a simpler architecture, namely two convolution layers and three convolution layers to solve the same problem, as well as examining the combination of various hyperparameter sizes and regularization techniques. We need to know which convolutional neural network architecture is better. As a result, the convolutional neural network classification model can recognize chest x-rays infected with pneumonia very well. The best classification model obtained an average accuracy of 89.743% with a three-layer convolution architecture, batch size 32, L2 regularization 0.0001, and dropout 0.2. The precision reached 94.091%, recall 86.456%, f1-score 89.601%, specificity 85.491, and error rate 10.257%. Based on the results obtained, convolutional neural network models have the potential to diagnose pneumonia and other diseases.","PeriodicalId":364657,"journal":{"name":"MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer","volume":"97 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139320875","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

Implementing K-Nearest Neighbor to Classify Wild Plant Leaf as a Medicinal Plants 利用 K 近邻法将野生植物叶片归类为药用植物

MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Pub Date : 2023-10-10 DOI: 10.30812/matrik.v23i1.2220

Z. E. Fitri, Lalitya Nindita Sahenda, Sulton Mubarok, Abdul Madjid, A. M. N. Imron

{"title":"Implementing K-Nearest Neighbor to Classify Wild Plant Leaf as a Medicinal Plants","authors":"Z. E. Fitri, Lalitya Nindita Sahenda, Sulton Mubarok, Abdul Madjid, A. M. N. Imron","doi":"10.30812/matrik.v23i1.2220","DOIUrl":"https://doi.org/10.30812/matrik.v23i1.2220","url":null,"abstract":"in leaf shape. Therefore, this study aimed to create a system to help increase public knowledge about wild plant leaves that also function as medicinal plants by the KNN method. Leaves of wild plants, namely Rumput Minjangan, Sambung Rambat, Rambusa, Brotowali, and Zehneria japonica, are also medicinal plants in comparison. Image processing techniques used were preprocessing, image segmentation, and morphological feature extraction. Preprocessing consists of scaling and splitting the RGB components and using an RGB component decomposition process to find the color component that best describes the leaf shape and generate the blue component image. The segmentation process used a thresholding technique with a gray threshold value (T) of less than 150, which best separates objects and backgrounds. Some morphological feature extraction used are area, perimeter, metric, eccentricity, and aspect ratio. Based on the results of this research, the KNN method with variations in K values, namely 13, 15, and 17, obtained a system accuracy of 94.44% with a total of 90% training data and 10% test data. This comparison also affected the increase in system accuracy.","PeriodicalId":364657,"journal":{"name":"MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139321267","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

Investigating The Effectiveness of Various Convolutional Neural Network Model Architectures for Skin Cancer Melanoma Classification 研究各种卷积神经网络模型架构在皮肤癌黑色素瘤分类中的有效性

MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Pub Date : 2023-10-07 DOI: 10.30812/matrik.v23i1.3185

Rizky Hafizh Jatmiko, Yoga Pristyanto

{"title":"Investigating The Effectiveness of Various Convolutional Neural Network Model Architectures for Skin Cancer Melanoma Classification","authors":"Rizky Hafizh Jatmiko, Yoga Pristyanto","doi":"10.30812/matrik.v23i1.3185","DOIUrl":"https://doi.org/10.30812/matrik.v23i1.3185","url":null,"abstract":"Melanoma is one of the most dangerous types of skin cancer. Since 2018, the number of skin cancer cases in the US has increased and exceeded 100,000. Melanoma is the third most common cancer in Indonesia, following womb cancer and breast cancer. Standard detection of melanoma skin cancer biopsy is costly and time-consuming. The purpose of this research is to build and compare melanoma skin cancer detection using various Convolutional Neural Network method. This research used four CNN model architectures methods, VGG-16, LeNet, Xception, and MobileNet. The dataset for this research is image data that consists of 9605 data divided into benign and malignant classes. The data will be augmented to increase its quantity. After that, the data will be trained using four CNN architecture models and evaluated using the confusion matrix. The result of this study is that Xception model has the best accuracy and the lowest loss, with 93% accuracy and 19% loss, with precision 93%, recall 93,5%, and f1-score 93%. Whereas the other model, VGG-16 gives 90 % accuracy, 27% loss, LeNet 89,7% accuracy, 28% loss, and mobileNet 90,8% accuracy and 22,5% loss.","PeriodicalId":364657,"journal":{"name":"MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139321996","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

Mobile Forensic for Body Shaming Investigation Using Association of Chief Police Officers Framework 利用警察局长协会框架进行身体羞辱调查的流动法医

MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Pub Date : 2023-07-31 DOI: 10.30812/matrik.v22i3.2987

Yana Safitri, I. Riadi, Sunardi Sunardi

引用次数: 0

IoT-based Integrated System Portable Prayer Mat and DailyWorship Monitoring System 基于物联网的集成系统便携式祈祷垫及日常礼拜监控系统

MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Pub Date : 2023-07-29 DOI: 10.30812/matrik.v22i3.3058

Luh Kesuma Wardhani, Nenny Anggraini, Nashrul Hakiem, M. T. Rosyadi, Amin Rois

{"title":"IoT-based Integrated System Portable Prayer Mat and DailyWorship Monitoring System","authors":"Luh Kesuma Wardhani, Nenny Anggraini, Nashrul Hakiem, M. T. Rosyadi, Amin Rois","doi":"10.30812/matrik.v22i3.3058","DOIUrl":"https://doi.org/10.30812/matrik.v22i3.3058","url":null,"abstract":"Muslims have various difficulties in praying, such as difficulty memorizing the number of rak’ah they have been doing and determining the direction of the Qibla. In this research, we proposed a technological device for monitoring daily worship in Islam. We presented the IoT-based integrated system as a portable prayer mat serving as a rak’ah counter, Qibla direction finder, and a mobile worship monitoring system. A prototyping approach was used to produce a portable smart prayer mat, and Rapid Application Development was used to develop a mobile daily worship system. The device comprises an Arduino AT Mega 2560 powered portable prayer mat through a force-sensitive resistor sensor and an HMC 5883L compass module. The device sends the prayer activity to the worship applications in detail. The daily worship monitoring application itself has numerous features that enable users to track their daily worship activities, including the Hijri calendar, the time of compulsory prayers, the fulfillment of sunnah prayers, and fasting. Evaluation results showed that the system detected the rak’ah correctly in each cycle with average pressure to the FSR sensor of 81.36. The average time required to connect with a smartphone was 0.862 seconds. It also functions well as a Qibla finder. The black box testing results showed that the device and application performed effectively. It can send the worship data recapitulation to the application using Bluetooth.","PeriodicalId":364657,"journal":{"name":"MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114166286","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