The accuracy obtained from the thesis document classification system is 93%.Ribuan contoh skripsi lengkap semua jurusan dari mulai contoh skripsi manajemen manajemen ekonomi manajemen pemasaran manajemen sdm skripsi pendidikancontoh skripsi keperawatancontoh skripsi teknik informatika contoh skripsi akuntansicontoh skripsi hukum contoh skripsi bahasa inggriscontoh skripsi matematikacontoh skripsi manajemen. Of the total 15 testing data, 14 data were classified correctly and 1 sample was not classified correctly. The data used in this study were 49 data, 34 of which were used for training data and the remaining 15 were used for testing data. The process of calculating the classification of the thesis document using the Naïve Bayes Classifier method begins with inputting training data, preprocessing, calculating the term frequency (word occurrences), calculating the word probability value from the training data, and the final process is calculating the maximum probability value for each category. There are 5 categories used, namely SPK, RPL, Data Mining, Image Processing, and System and Network Security. Abstract files processed in this classification are abstract files from IT Faculty students who have graduated. This method has the aim of classifying the datatesting according to the datatraining attributes. In this study using the Naïve Bayes Classifier method, a classification method by calculating probability by adding frequencies with a combination of values in the data set. Text mining is functioned to extract data in the form of text to get information from a collection of documents. The research discussed about the application of text mining in the classification of thesis documents with case studies at the Faculty of Information Technology. The purpose of thesis document classification aims to assist students in finding a thesis document that is in accordance with their research by reading the abstract to find out specific category. Thesis document classification is a data mining method with the aim of categorizing thesis abstracts whose categories are unknown.
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