Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal SemanTIK : Teknik Informasi
Muh Saiful
Jurusan Teknik Informatika, Fakultas Teknik, Universitas Halu Oleo, Kendari
Computer Science & IT Control & Systems Engineering

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

Found 1 Documents
Search

 1 
KLASIFIKASI JENIS PELAPORAN DI LAMAN PROPAM POLDA SULAWESI TENGGARA MENGGUNAKAN METODE ROCCHIO Muh Saiful; Natalis Ransi; Jumadil Nangi
semanTIK Vol 5, No 1 (2019): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (16.004 KB) | DOI: 10.55679/semantik.v5i1.5993

Abstract

Service is the major thing in an organization. PROPAM Polda Sultra is one of the government institutions that have utilized telecommunications technology, namely complaints or online reporting pages. This technology is used to simplify and accelerate the delivery of information from the public to PROPAM Polda Sultra either in the form of complaints or reports for police who violate. The increasing number of reports of public complaints every day resulted in many reports that have not been responded to or unread. Therefore, the need to classify complaints reports automatically is needed to make time and human resources effectively in grouping complaints reports. The research was conducted with using Text Mining, the Rocchio method and using Tf-IDF weighting.Based on the results of testing, Rocchio algorithm has a good performance for the classification of types complaint report. This is evidenced in manual testing and system testing using complain report and then classified into eighth categories of Immoral, Traffic Accident, Narcotics, Poor Service, Persecution, Threatening, Insult and Deviations in Carrying Out Tasks (Case Handling). The results of classification using 79 reports obtained 89,87% accuracy.Keywords—Service, Public Complaints, Rocchio, Text Mining, TF-IDFDOI : 10.5281/zenodo.3255093 
Co-Authors Jumadil Nangi Natalis Ransi