Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal JOINCS (Journal of Informatics, Network, and Computer Science)
Sumarno
Departement of Informatics Universitas Muhammadiyah Sidoarjo
Computer Science & IT

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

Found 1 Documents
Search
Journal : joincs journal of informatics network and computer science

 1 
Classification of Student Complaints with the Naive Bayes and Literature Methods Haris Ahmad Gozali; Mochamad Alfan Rosid; Sumarno
JOINCS (Journal of Informatics, Network, and Computer Science) Vol 3 No 1 (2020): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1327.767 KB) | DOI: 10.21070/joincs.v3i0.711

Abstract

E-Complaint Data is a collection of data which contains comments or complaints from students towards the University. Lots of comments - comments that occur in the university environment about the performance facilities of lecturers etc ... Text mining is also known as text data mining or knowledge search in a textual database is a semi-automatic process of extracting data patterns. The purpose of text mining is to get useful information from a collection of documents. In this study using the naïve Bayes method with TFIDF weighting features. The stages will be taken to determine its classification. First is taking data from the E-Complaint System then the data will go through the preprocessing stage using literary libraries, after going through the preprocessing stage the data will be divided into 2 namely training data and testing data. Then the training data will be carried out the TF-IDF weighting process up to the probability, if it has, the next is to process the testing data by determining priors. Next is the data testing stage between the testing data and the training data, the results of the testing data will come in the form of a predetermined category. The trial results show that the classification of complaints with the naïve Bayes algorithm and with the TF-IDF feature and literary libraries in the preprocessing process has an average accuracy that is quite high at 82%.
Co-Authors Haris Ahmad Gozali Mochamad Alfan Rosid