Article Per Year (5 Year)
p-Index From 2021 - 2026
1.556
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering JPTK: Jurnal Pendidikan Teknologi dan Kejuruan JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmiah Informatika Komputer UG Journal Semantik Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ilmiah FIFO Jurnal Ilmiah Matrik CARADDE: Jurnal Pengabdian Kepada Masyarakat SmartComp Engagement: Jurnal Pengabdian Kepada Masyarakat
Rifiana Arief
Gunadarma University
Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Social Sciences Other

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : International Journal of Electrical and Computer Engineering

 1 
Automated hierarchical classification of scanned documents using convolutional neural network and regular expression Rifiana Arief; Achmad Benny Mutiara; Tubagus Maulana Kusuma; Hustinawaty Hustinawaty
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp1018-1029

Abstract

This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from pusat data teknologi dan informasi (technology and information data center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.
Co-Authors Achmad Benny Mutiara Arkan, Faisal Ary Bima Kurniawan Ary Bima Kurniawan Diharjo, Bayu Ditiya Himawati Erlina Erlina Erlina Erlina Solihin Faisal Arkan Gestyar Satrio Adji Hidayat, Muhammad Imam Hustinawaty Hustinawaty, Hustinawaty Karel Imanuel Naeli Umniati Novindasari Munawarah Umar Onny Marleen Suryarini Widodo Suryarini Widodo Swelandiah Endah Pratiwi Tubagus Maulana Kusuma Wibisono, Evan Albernan Widyo Nugroho