Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Muhammad Sholeh Hudin
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Implementasi Metode Text Mining dan K-Means Clustering untuk Pengelompokan Dokumen Skripsi (Studi Kasus: Universitas Brawijaya) Muhammad Sholeh Hudin; Mochammad Ali Fauzi; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1213.336 KB)

Abstract

Research or final assignment is a requirement of graduation students. Every year the research becomes increasing and allows the students to take the same or similar topics. Through this research developed an application to classify student thesis reports. The results of this grouping also indicate that the themes are varied and when the themes becomes non-varied. Student research reports or commonly called a thesis report can be grouped by theme, object or method of the research. The process of extracting this thesis is done by using text mining technology. Then the process of grouping thesis document can be done by using k-means clustering method on a set of thesis documents by taking abstract, keywords and table of contents as an important information that represents the content of the document. Then the document will be done preprocessing first by using text mining method. To process the preprocessing is divided into several parts, namely tokenisasi, filtering, stemming and term weighting. After the document passes through the preprocessing process, then the document can be grouped by using the method of k-means clustering. In this experiment, trials are conducted by entering the number of clusters that vary. From the results of the analysis by entering the different cluster values have obtained the optimal value by entering the number of with the resulting silhouette value 0,483695522.
Co-Authors Adinugroho, Sigit M. Ali Fauzi