Article Per Year (5 Year)
p-Index From 2019 - 2024
0.751
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JOIN (Jurnal Online Informatika) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Julian Saputra
Institut Teknologi Telkom Purwokerto
Computer Science & IT Control & Systems Engineering Education Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Penerapan Algoritma Support Vector Machine dan Multi-Layer Perceptron pada Klasifikasi Topik Berita Sudianto Sudianto; Asti Dwi Sripamuji; Imada Riski Ramadhanti; Risa Riski Amalia; Julian Saputra; Bagas Prihatnowo
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 11 No. 2 (2022)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Salah satu aktivitas masyarakat Indonesia di internet adalah mengakses berita online dengan persentase 15,5%. Dalam mencerna suatu informasi berita online, masyarakat Indonesia masih memiliki kebiasaan yang buruk ketika membaca lebih dari satu sumber media online pada kasus yang sama. Sehingga perlu adanya klasifikasi berita secara otomatis. Penelitian sebelumnya menggunakan Mutual Information and Bayesian Network untuk mengkategorikan suatu berita, dalam format data tekstual dengan akurasi 75.34%. Penelitian ini memberikan suatu inovasi baru berupa pengimplementasian algoritma Support Vector Machine dan Multi-Layer Perceptron untuk klasifikasi menggunakan pembelajaran supervised atau yang disebut backpropagation. Tujuan dari pengklasifikasian topik berita menggunakan Algoritma Support Vector Machine memudahkan pembaca untuk menemukan informasi yang pembaca butuhkan, dengan secara otomatis mengelompokkan berita ke dalam kategori tertentu sesuai informasi yang terkandung. Metode yang digunakan yaitu metode Term Frequency – Inverse Document Frequency untuk feature selection data pelatihan dan algoritma Support Vector Machines dan Multi-Layer Perceptron untuk klasifikasi. Hasil yang diperoleh menunjukan skor akurasi sebesar 74% pada SVM dan 78% pada MLP. Sementara itu, nilai precision dan recall yaitu 76% dan 74% pada SVM serta 79% dan 78% pada MLP.
Deep Learning Approaches for Nusantara Scripts Optical Character Recognition Agi Prasetiadi; Julian Saputra; Iqsyahiro Kresna; Imada Ramadhanti
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 3 (2023): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.86302

Abstract

The number of speakers of regional languages who are able to read and to write traditional scripts in Indonesia is decreasing. If left unaddressed, this will lead to the extinction of Nusantara scripts and it is not impossible that their reading methods will be forgotten in the future. To anticipate this, this study aims to preserve the knowledge of reading ancient scripts by developing a Deep Learning model that can read document images written using one of the 10 Nusantara scripts we have collected: Bali, Batak, Bugis, Javanese, Kawi, Kerinci, Lampung, Pallava, Rejang, and Sundanese. While previous studies have made efforts to read traditional Nusantara scripts using various Machine Learning and Convolutional Neural Network algorithms, they have primarily focused on specific scripts and lacked an integrated approach from script type recognition to character recognition. This study is the first to comprehensively address the entire range of Nusantara scripts, encompassing script type detection and character recognition. Convolutional Neural Network, ConvMixer, and Visual Transformer models were utilized and their respective performances were compared. The results demonstrate that our models achieved 96% accuracy in classifying Nusantara script types, with character recognition accuracy ranging from 93% to approximately 100% across the ten scripts.
YOLOv5 and U-Net-based Character Detection for Nusantara Script Agi Prasetiadi; Julian Saputra; Iqsyahiro Kresna; Imada Ramadhanti
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1180

Abstract

Indonesia boasts a diverse range of indigenous scripts, called Nusantara scripts, which encompass Bali, Batak, Bugis, Javanese, Kawi, Kerinci, Lampung, Pallava, Rejang, and Sundanese scripts. However, prevailing character detection techniques predominantly cater to Latin or Chinese scripts. In an extension of our prior work, which concentrated on the classification of script types and character recognition within Nusantara script systems, this study advances our research by integrating object detection techniques, employing the YOLOv5 model, and enhancing performance through the incorporation of the U-Net model to facilitate the pinpointing of fundamental Nusantara script's character locations within input document images. Subsequently, our investigation delves into rearranging these character positions in alignment with the distinctive styles of Nusantara scripts. Experimental results reveal YOLOv5's performance, yielding a loss rate of approximately 0.05 in character location detection. Concurrently, the U-Net model exhibits an accuracy ranging from 75% to 90% for predicting character regions. While YOLOv5 may not achieve flawless detection of all Nusantara scripts, integrating the U-Net model significantly enhances the detection rate by 1.2%.
YOLOv5 and U-Net-based Character Detection for Nusantara Script Agi Prasetiadi; Julian Saputra; Iqsyahiro Kresna; Imada Ramadhanti
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1180

Abstract

Indonesia boasts a diverse range of indigenous scripts, called Nusantara scripts, which encompass Bali, Batak, Bugis, Javanese, Kawi, Kerinci, Lampung, Pallava, Rejang, and Sundanese scripts. However, prevailing character detection techniques predominantly cater to Latin or Chinese scripts. In an extension of our prior work, which concentrated on the classification of script types and character recognition within Nusantara script systems, this study advances our research by integrating object detection techniques, employing the YOLOv5 model, and enhancing performance through the incorporation of the U-Net model to facilitate the pinpointing of fundamental Nusantara script's character locations within input document images. Subsequently, our investigation delves into rearranging these character positions in alignment with the distinctive styles of Nusantara scripts. Experimental results reveal YOLOv5's performance, yielding a loss rate of approximately 0.05 in character location detection. Concurrently, the U-Net model exhibits an accuracy ranging from 75% to 90% for predicting character regions. While YOLOv5 may not achieve flawless detection of all Nusantara scripts, integrating the U-Net model significantly enhances the detection rate by 1.2%.
Co-Authors Agi Prasetiadi Asti Dwi Sripamuji Bagas Prihatnowo Imada Ramadhanti Imada Riski Ramadhanti Iqsyahiro Kresna Risa Riski Amalia Sudianto