Figure 1
Source publication
![Confusion matrix for multi-class classification [21]](publication/363099952/figure/tbl1/AS:11431281081753700@1661871639889/Confusion-matrix-for-multi-class-classification-21_Q320.jpg)
Purpose: However, from the variety of uses of these algorithms, in general, accuracy problems are still a concern today, even accuracy problems related to multi-class classification still require further research.Methods: This study proposes a stacking ensemble classifier method to produce better accuracy by combining Logistic Regression, Random Fo...
Context in source publication
Similar publications
Social media and web have become popular platforms of information sharing, knowledge gathering, expressing sentiments, perceiving choices regarding products and services through major news sources, and an active channel for marketing. Hence, with these promising features comes the threat of misinformation propagation, leading to undesirable effects...
Mental health problems in the world, especially in Indonesia are still significant. According to the Ministry of Health of the Republic of Indonesia stated that depression is experienced by adolescents from the age of 15 to 24 years. The depression experienced by a person is sometimes not realized by the sufferer, so social media becomes an interme...
Busy lifestyles of people which resulted in an increase in non-communicable diseases have demanded a revolution in the healthcare system. This has prompted active research in developing smart sensing devices to automatically monitor the health status of a user with less human intervention. This could be more challenging when the disease is asymptom...
Industry 5.0 benefits from advancements being made in the field of machine learning and the Internet of Things. Different sensors have been installed in a variety of IoT devices present in different industries such as transportation, healthcare, manufacturing, agriculture, etc. The sensors present in these devices should automatically predict error...
This paper proposed an integrated Chinese malicious webpages detection method. Firstly, we collected and released a Chinese malicious webpages detection dataset called “ChiMalPages” containing URLs and HTML/JavaScript files, and specified the detailed types of malicious pages according to relevant laws. Secondly, we designed a feature template for...
Citations
... Another method are Support Vector Machine (SVM) [14][15] [16], and Backpropagation Neural Network [17]. Naive Bayes has the advantage of being simple but has high accuracy even though it uses not a lot of data, while SVM has the advantage of solving linear and non-linear classification and regression problems which can become a learning algorithm capability for regression and classification, but the Support Vector Machine (SVM) are not efficient in training large-capacity data [18]. Backpropagation has advantages over other Neural Networks, namely Backpropagation using supervised training [19]. ...
Cancer is currently one of the leading causes of death worldwide. One of the most common cancers, especially among women, is breast cancer. There is a major problem for cancer experts in accurately predicting the survival of cancer patients. The presence of machine learning to further study it has attracted a lot of attention in the hope of obtaining accurate results, but its modeling methods and predictive performance remain controversial. Some Methods of machine learning that are widely used to overcome this case of breast cancer prediction are Backpropagation. Backpropagation has an advantage over other Neural Networks, namely Backpropagation using supervised training. The weakness of Backpropagation is that it handles classification with high-dimensional datasets so that the accuracy is low. This study aims to build a classification system for detecting breasts using the Backpropagation method, by adding a method of forward selection for feature selection from the many features that exist in the breast cancer dataset, because not all features can be used in the classification process. The results of combining the Backpropagation method and the method of forward selection can increase the detection accuracy of breast cancer patients by 98.3%.
