Recursive Journal of Informatics最新文献

Implementation of Convolutional Neural Network Algorithm Using Vgg-16 Architecture for Image Classification in Facial Images 基于Vgg-16架构的卷积神经网络算法在人脸图像分类中的实现

Recursive Journal of Informatics Pub Date : 2023-09-29 DOI: 10.15294/rji.v1i2.68059

Renita Arianti Hapsari, Aji Purwinarko

{"title":"Implementation of Convolutional Neural Network Algorithm Using Vgg-16 Architecture for Image Classification in Facial Images","authors":"Renita Arianti Hapsari, Aji Purwinarko","doi":"10.15294/rji.v1i2.68059","DOIUrl":"https://doi.org/10.15294/rji.v1i2.68059","url":null,"abstract":"Abstract: Face Recognition has now become a technology capable of recognizing facial patterns, facial image recognition is also used in various applications, for example in biological data recognition applications, digital image and video search, room security, and other applications. Purpose: This study aims to find out how the implementation of the CNN method with the VGG-16 architecture affects the classification of gender in facial images and how it affects the results. Methods/Study design/approach: In this study, we use the CNN method for data processing and build the program and use VGG-16 Architecture to build the model, then the tensorflow library for calling the required features such as when optimizing or for statistical plots and using the Confusion Matrix to determine the level of accuracy obtained. The desired results in this study are accuracy, precision, recall, and Fscore. Result/Findings: Classifying facial images using CNN with VGG-16 architecture provides an accuracy rate of 94%. From the results of this study it can be concluded that the model with the best accuracy is at epoch 20 compared to epoch 60, epoch 80, and epoch 100 which have previously been tested. Novelty/Originality/Value: The level of accuracy resulting from the implementation of the CNN method using the VGG-16 Architecture for image classification in facial images is quite good, resulting in an accuracy of 94%. Accuracy results were obtained from tests carried out by comparing several epoch values to produce the best accuracy of 94% using epoch 20.","PeriodicalId":485141,"journal":{"name":"Recursive Journal of Informatics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199341","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

Diagnosis of Heart Disease Using Optimized Naïve Bayes Algorithm with Particle Swarm Optimization and Gain Ratio 基于粒子群优化和增益比的优化Naïve贝叶斯算法在心脏病诊断中的应用

Recursive Journal of Informatics Pub Date : 2023-09-29 DOI: 10.15294/rji.v1i2.67278

Anisa Meidina, Zaenal Abidin

{"title":"Diagnosis of Heart Disease Using Optimized Naïve Bayes Algorithm with Particle Swarm Optimization and Gain Ratio","authors":"Anisa Meidina, Zaenal Abidin","doi":"10.15294/rji.v1i2.67278","DOIUrl":"https://doi.org/10.15294/rji.v1i2.67278","url":null,"abstract":"Abstract.  Purpose: This study aims to apply feature selection particle swarm optimization (PSO) and gain ratio to the naïve Bayes algorithm and gauging the level of accuracy before and after applying PSO feature selection and gain ratio to the naïve Bayes algorithm in the diagnosis of heart disease.Methods/Study design/approach: Data collection is done by using taking the Cleveland dataset obtained from the UCI machine learning repository. The data used in this study were 303 samples. The data is processed using the preprocessing stage. The naïve Bayes algorithm is used for a classifier, while PSO and gain ratio for feature selection.Result/Findings: The results of the study revealed that the classification accuracy of the naïve Bayes algorithm without the application of feature selection in the Cleveland dataset is 86.88%, while the results of the classification accuracy of the naïve Bayes algorithm after applying PSO and gain ratio in the Cleveland dataset is 93.44%. Application of PSO and gain ratio as feature selection algorithms can improve classification accuracy by 6.56%.Novelty/Originality/Value: This study combines the PSO feature selection and gain ratio on the naïve Bayes algorithm using the Cleveland dataset. The research model that was carried out was enriched by carrying out the preprocessing stages, namely data cleaning, changing the number of class labels, data normalization, and data discretization. This study shows that using a combination of the PSO feature selection algorithm and the gain ratio gives better accuracy to the naïve Bayes algorithm in diagnosing heart disease.","PeriodicalId":485141,"journal":{"name":"Recursive Journal of Informatics","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199820","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

Machine Learning Model Using Extreme Gradient Boosting (XGBoost) Feature Importance and Light Gradient Boosting Machine (LightGBM) to Improve Accurate Prediction of Bankruptcy 使用极端梯度增强(XGBoost)特征重要性和光梯度增强机(LightGBM)的机器学习模型来提高破产的准确预测

Recursive Journal of Informatics Pub Date : 2023-09-29 DOI: 10.15294/rji.v1i2.71229

Risma Moulidya Syafei, Devi Ajeng Efrilianda

{"title":"Machine Learning Model Using Extreme Gradient Boosting (XGBoost) Feature Importance and Light Gradient Boosting Machine (LightGBM) to Improve Accurate Prediction of Bankruptcy","authors":"Risma Moulidya Syafei, Devi Ajeng Efrilianda","doi":"10.15294/rji.v1i2.71229","DOIUrl":"https://doi.org/10.15294/rji.v1i2.71229","url":null,"abstract":"Abstract. Humans have limitations in processing and analyzing large amounts of data in a short time, including in terms of analyzing bankruptcy data. Bankruptcy data is one of the data that has complex information, so it requires technology that can assist in the process of analyzing and processing data more quickly and efficiently. Data science technology enables data processing and analysis on a large scale, using parallel processing techniques. Parallel processing can be implemented in machine learning models. Purpose: Using parallel processing techniques, data science technologies enable data processing and analysis at scale. Parallel processing can be implemented in machine learning models. Therefore, this study aims to implement a machine learning model using the Light Gradient Boosting Machine (LightGBM) classification algorithm which is optimized using Extreme Gradient Boosting (XGBoost) Feature Importance to increase the accuracy of bankruptcy prediction. Methods/Study design/approach: Bankruptcy prediction is carried out by applying LightGBM as a classification model and optimized using the XGBoost algorithm as a Feature Importance technique to improve model accuracy. the dataset used is the Taiwanese Bankruptcy dataset collected from the Taiwan Economic Journal for 1999 to 2009 and has 6,819 data. Taiwanese Bankruptcy is unbalanced data, so this study applies random oversampling. Result/Findings: The results obtained after going through the model testing process using the confusion matrix obtained an accuracy of the performance of LightGBM+XGBoost Feature Importance of 99.227%. Novelty/Originality/Value: So it can be concluded that the implementation of XGBoost Feature Importance can be used to improve LightGBM's performance in bankruptcy prediction.","PeriodicalId":485141,"journal":{"name":"Recursive Journal of Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199827","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

Stock Return Prediction Using Voting Regressor Ensemble Learning 基于投票回归集成学习的股票收益预测

Recursive Journal of Informatics Pub Date : 2023-09-29 DOI: 10.15294/rji.v1i2.68048

Ramadhan Ridho Arrohman, Riza Arifudin

引用次数: 0

Sentiment Analysist of the TPKS Law on Twitter Using InSet Lexicon with Multinomial Naïve Bayes and Support Vector Machine Based on Soft Voting 利用InSet词典与多项Naïve贝叶斯和基于软投票的支持向量机对Twitter上TPKS法律的情感分析

Recursive Journal of Informatics Pub Date : 2023-09-29 DOI: 10.15294/rji.v1i2.68324

Salsabila Rahadatul Aisy, Budi Prasetiyo

{"title":"Sentiment Analysist of the TPKS Law on Twitter Using InSet Lexicon with Multinomial Naïve Bayes and Support Vector Machine Based on Soft Voting","authors":"Salsabila Rahadatul Aisy, Budi Prasetiyo","doi":"10.15294/rji.v1i2.68324","DOIUrl":"https://doi.org/10.15294/rji.v1i2.68324","url":null,"abstract":"Abstract. The Indonesian Sexual Violence Law (TPKS Law) is a law that regulates forms of sexual violence. The TPKS Law reaped pros and cons in the drafting process and was officially ratified on April 12th, 2022. However, after being ratified, pros and cons can still be found and supervision is needed over the implementation of the law. Purpose: This study was conducted to identify the application and accuracy of soft voting on multinomial naïve Bayes and support vector machine algorithm, also to find out public opinion on the TPKS Law as a support tool in evaluating the law. Methods/Study design/approach: The method used is InSet lexicon for labeling with the soft voting classification method on the multinomial naive Bayes and support vector machine algorithm. Result/Findings: The accuracy obtained by applying 10 k-fold cross validation in soft voting is 84.31%, which uses a weight of 1:3 for multinomial naive Bayes and support vector machines. Soft voting obtains better accuracy than its standalone predictor, and also works well for sentiment analysis of the TPKS Law. Novelty/Originality/Value: This study using two combined lexicons (Colloquial Indonesian lexicon and the InaNLP formalization dictionary) in normalization process and using InSet lexicon as automatic labeling for sentiment analysis on TPKS Law.","PeriodicalId":485141,"journal":{"name":"Recursive Journal of Informatics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135246023","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

Comparison of Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) Algorithms for Diabetes Classification 概率神经网络(PNN)与k-近邻(k-NN)算法在糖尿病分类中的比较

Recursive Journal of Informatics Pub Date : 2023-09-29 DOI: 10.15294/rji.v1i2.66078

Diah Siti Fatimah Azzahrah, Alamsyah Alamsyah

引用次数: 0

Image classification of Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning 基于迁移学习的卷积神经网络例外架构的人脸图像分类

Recursive Journal of Informatics Pub Date : 2023-09-29 DOI: 10.15294/rji.v1i2.70774

Resta Adityatama, Anggyi Trisnawan Putra

{"title":"Image classification of Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning","authors":"Resta Adityatama, Anggyi Trisnawan Putra","doi":"10.15294/rji.v1i2.70774","DOIUrl":"https://doi.org/10.15294/rji.v1i2.70774","url":null,"abstract":"Abstract. The development of information technology in facial recognition is influenced by a faster and more accurate authentication system. This allows the computer system to identify a person's face. Purpose: Similar to fingerprints and the retina of the human eye, each person's face has a different shape and contour. Since it is known that the human face provides a lot of information, as well as topics that attract attention make it studied intensively. Methods/Study design/approach: Several studies examining information from human faces are facial recognition. One of the approaches used to recognize facial imagery is through the use of a Convolutional Neural Network (CNN). CNN is a method in the field of Deep Learning that can be used to recognize and classify objects in digital images. In this study, the method used to implement facial image classification is the Xception architecture CNN algorithm with a transfer learning approach. Result/Findings: The dataset used in this study was obtained from Kaggle, namely the Face Shape Dataset which contains 5000 data. After testing, an accuracy rate of 96.2% was obtained in the training process and 81.125% in the validation process. This study also uses new data to test the model that has been made, and the results show an accuracy rate of 85.1% in classifying facial imagery. Novelty/Originality/Value: Therefore, it can be said that the model created in this study has the ability to classify images of facial shapes Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning.","PeriodicalId":485141,"journal":{"name":"Recursive Journal of Informatics","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135246866","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