Data used in big data applications are typically kept decentralized computing resources the real world, which has an impact on design of artificial intelligence algorithms. When there significantly more observations from one class than another, dataset is said to be imbalanced. Therefore, this work, study elaborates model as SMOTE-SVM resolves imbalance issues sampling and improves overall accu...

Classification algorithms are unable to make reliable models on the datasets with huge sizes. These datasets contain many irrelevant and redundant features that mislead the classifiers. Furthermore, many huge datasets have imbalanced class distribution which leads to bias over majority class in the classification process. In this paper combination of unsupervised dimensionality reduction method...

Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive instances (e.g. in gene profiling and detecting credit card fraud). This paper discusses the factors behind ...

We developed a multiscale object-based classification method for detecting diseased trees (Japanese Oak Wilt and Japanese Pine Wilt) in high-resolution multispectral satellite imagery. The proposed method involved (1) a hybrid Intensity-Hue-Saturation (IHS)/Smoothing Filter-based Intensity Modulation (SFIM) pansharpening approach 10 (IHS-SFIM) to obtain more spatially and spectrally accurate im...

The punctuality of students in completing their studies is an important aspect the study program. Because there are still who have not been able to complete on time. purpose this determine factors that influence by extracting student academic data obtain a classification model can be used predict accuracy period. method for predicting student's period uses Naive Bayes algorithm using Feature Fo...

Many real-world classification problems such as fraud detection, intrusion churn prediction, and anomaly detection suffer from the problem of imbalanced datasets. Therefore, in all tasks, we need to balance datasets before building classifiers for prediction purposes. Several data-balancing techniques (DBT) have been discussed literature address this issue. However, not much work is conducted a...

Indihome merupakan salah satu dari sekian internet service provider terbesar di Indonesia dengan peningkatan pelanggan setiap tahunnya. Pelanggan hingga akhir Maret 2022 tercatat mencapai 8,7 juta, tumbuh 7,2 persen dibanding periode yang sama tahun lalu. Namun, seiring berjalannya waktu, banyak melayangkan keluhan kepada IndiHome mengenai lambatnya akses internet, kenaikan billing secara tiba-...

There are too many cattle in the world and breeds of cattle. For someone who is new to breeding, it may be difficult tell which species their are. In some cases, an experienced person not understand two that similar appearance. this study, aim classify with image processing methods mobile applications written Flutter TensorFlow Lite. classifying breeds, The VGG-16 algorithm was used for feature...

Class imbalanced datasets are prevalent in real-world applications, including engineering, medical domain, financial sector, and others. Machine learning (ML)-based prediction models have successfully demonstrated the applicability of various algorithms for solution different problems. However, their application soil liquefaction issue considering class imbalance situation is limited. This pape...