In the midst of digital business growth rapidly and Covid-19 pandemic that has lasted for several years, people tends to carry out all their activities from home, including fulfilling their needs through various e-commerce services. This research model intends to investigate the important role from customer perceived value in increasing online repurchase intention on various e-commerce sites in Indonesia, such as Shopee, Tokopedia, Bukalapak, Lazada and Blibli. This research method designed by quantitative causality with a total sample of 310 people through snowball sampling. The analytical method used AMOS-SEM to verified the role of customer value perceptions. This results shows that customer perceived value indirectly act as an crucial part in increasing the influence from e-service quality, ease of use, store image and online promotion on online repurchase intention.

Penelitian yang dilakukan ini merupakan bagian dari text mining untuk klasifikasi konten berita yang telah memiliki label berdasarkan katagori berita pada situs detik.com . Proses yang dilakukan adalah melakukan permodelan dan pengolahan data, mulai proses pre-processing, proses seleksi fitur information gain, dan penerapan model algoritma Naive Bayes Classifier dengan Bayesian Boosting. Hasil yang diperoleh atas model tersebut mendapatkan nilai evaluasi terhadap akurasi, recall, dan presisi sebesar 73.2%. Sedangkan dengan model yang lebih ringkas yaitu model algoritma Naive Bayes Classifier, dengan Bayesian Boosting mendapatkan nilai evaluasi yang sama besar yaitu 73.2%. Penilaian atas hasil evaluasi model yang telah terlaksankan berkesimpulan bahwa penerapan seleksi fitur Information Gain tidak berpengaruh besar atas kenaikan hasil performa terhadap kondisi label Polynomial.

This research is about the classification of news that optimizes with a combination of algorithms. About the dataset used is taken on the online news site. The algorithm used is the Naive Bayes Classifier and Random Forest algorithms by weighting the Information Gain feature selection. The dataset used is 615 datasets with 3 categories or news themes. Get useless models, Delete Useful Attributes, Naive Bayes Classifier-Multinomials, and Random Forest-Feature Selection Information gain. The results of the assessment obtained were an accuracy value of 85.67%, a recall value of 85.67%, and a precision value of 86.23%.

Sentiment analysis off odd even numbered systems in Bekasi toll using the Naive Bayes Algoritma, is a process of understanding, extracting, and processing textual data automatically to obtain a sentiment information contained in an opinion sentence. After doing the weighting process, the data goes through the classification stage using the Naive Bayes method. Naive Bayes is a technique for making predictions, both in the case of classification and regression. In addition there are stages of text mining that are broadly define as intensive knowledge processes that allow users to interact with acollection of documents from time to time using variouskinds of anlysis. The research method used in this study is to do text mining on comments related to posts about even odd effectiveness on Bekasi toll on Twitter, Instagram, Youtube and Facebook. In doing text mining the following steps will be carried out: selection, preprocessing, transformation, datamining and evaluation. The text mining tool that will be used is using Rapidminer version 9.0 for the modeling front that is used. Then the analytical sentiment is carried out from tweets, comments and post submitted by the public. After the research was done, the results showed that the resultd of the tests carried out through the Naive Bayes model resulted in Confusion Matrix, namely accuracy of 79.55%, Precision of 80,37.03%, and Sensitivity or Recall of 80.51%. KESIMPULANSetelah dilakukan penelitian maka dapat disimpulkan bahwa tingkat Hasil pengujian yang dilakukan melalui model Naïve Bayes menghasilkan Confusion Matrix, yaitu accuracy sebesar 79,55%, Precision sebesar 80,37%, dan Sensitivity atau Recall sebesar 80,91%. Penelitian selanjutnya yang akan dilakukan adalah membandingkan metode algoritma antara Naïve Bayes dengan menambahkan seleksi fitur Information Gain Untuk mencari hasil akurasi mana algoritma terbaik diantara keduanya.

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Support Vector Machine Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Support Vector Machine algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying SVM Algorithm model. The results obtained from the study using the SVM model are obtained Confusion Matrix result, namely accuracyof 78.18%, Precision of 74.03%, and Sensitivity or Recall of 86.82%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.