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All Journal Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi
Bagus Pratama Putra, Dharma
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Computer Science & IT

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Klasifikasi Dokumen Skripsi Dengan Menggunakan Text Mining (Studi Kasus: Fakultas Teknologi Informasi) Irfanto, Feri; Dwi Indriyanti, Aries; Bagus Pratama Putra, Dharma
Inovate Vol 5 No 2 (2021): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v5i2.3118

Abstract

Thesis document classification is a data mining method with the aim of categorizing thesis abstracts whose categories are unknown. 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. The research discussed about the application of text mining in the classification of thesis documents with case studies at the Faculty of Information Technology. Text mining is functioned to extract data in the form of text to get information from a collection of documents. 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. This method has the aim of classifying the datatesting according to the datatraining attributes. Abstract files processed in this classification are abstract files from IT Faculty students who have graduated. There are 5 categories used, namely SPK, RPL, Data Mining, Image Processing, and System and Network Security. 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. 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. Of the total 15 testing data, 14 data were classified correctly and 1 sample was not classified correctly. The accuracy obtained from the thesis document classification system is 93%. Keywords: Thesis Document Classification, Text Mining, Naïve Bayes Classifier
Rancang Bangun Sistem Keputusan Penerimaan Siswa Baru MTsN 9 Jombang Dengan Metode Topsis Hartono, Sherly; Dwi Indriyanti, Aries; Bagus Pratama Putra, Dharma
Inovate Vol 6 No 2 (2022): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v6i2.3176

Abstract

This research was conducted to build a system.that facilitates the teacher and the student to registrants. The purpose of this research. is to design a new student decision support system which is based on the website to facilitate the acceptance of new students to be faster, efficient and effective and also obtain rapid results. In this research researchers use the method TOPSIS (Tecchnique For order preference by similarity to ideal solution). The TOPSIS is method provides ideal solution for the selection of alternatives with several criteria the TOPSIS method is also widely use to resolve problem on decision making practically completion of Multicriteria decision making, in the TOPSIS method there is a calculation of the distance between the ideal positive solution and the ideal negative solution so that it is increasingly supportive to get the calculation with good results.The results. of the study are. obtain a degree of accuracy calculations of the hundred percent. Keywords: Decision support system, TOPSIS, screening
Co-Authors ARIES DWI INDRIYANTI, ARIES Hartono, Sherly Irfanto, Feri