Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Indonesian Journal of Artificial Intelligence and Data Mining
Pusparani, Diah Ayu
Unknown Affiliation
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
IDENTIFIKASI TANDA TANGAN MENGGUNAKAN PENGOLAHAN CITRA DIGITAL DAN METODE MACHINE LEARNING Putra, I Kadek Nurcahyo; Dewi, Ni Putu Dita Ariani Sukma; Pusparani, Diah Ayu; Mupu, Dibi Ngabe
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 7 No. 1 (2023)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v7i1.618

Abstract

Signature is used to legally approve an agreement, treaty, and state administrative activities. Identification of the signature is required to ensure ownership of a signature and to prevent things like forgery from happening to the owner of the signature. In this study, data signatures were obtained from 25 people over the age of 50. The signers provided 20 signatures and were free to choose the stationery used to write the signature on white paper. The total data obtained in this study was 500 signature data. The obtained signature was scanned to create a signature image, which was then pre-processed to prepare it for feature extraction, which can characterize the signature images. The HOG method was used to extract features, resulting in a dataset with 4,536 feature vectors for each signature image. To identify the signature image, the classification methods SVM, Decision Tree, Nave Bayes, and K-NN were compared. SVM achieved the highest accuracy, which is 100%. When K=5, the K-NN method achieved a fairly good accuracy of 97.3%. Meanwhile, Naive Bayes and Decision Tree achieved accuracy significantly lower than K-NN (61%). Because the HOG method produced a large feature vector for each signature, it is recommended that important features that represent signatures be optimized or extracted to produce smaller features to speed up computation without sacrificing accuracy, and that the HOG method be compared to other extraction feature methods to obtain a better model in future research.
Identification of Little Tuna Species Using Convolutional Neural Networks (CNN) Method and ResNet-50 Architecture Pusparani, Diah Ayu; Kesiman, Made Windu Antara; Aryanto, Kadek Yota Ernanda
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31620

Abstract

Indonesia is home to a vast array of biodiversity, including various species of little tuna. However, the process of identifying little tuna species is still challenging due to their diversity. The Indonesian Society and Fisheries Foundation (MDPI), which has the task of collecting fisheries data manually, is prone to significant identification errors. Therefore, the author proposes the utilization of Deep Learning, a Machine Learning method due to its ability to model various complex data such as images or pictures and sounds. This approach can facilitate the identification process of little tuna. In this research, the Resnet-50 architecture is utilised in the modelling process with the original dataset of 500 images. In this study, several test scenarios were also applied. The best results obtained are global accuracy of 91% and matrix accuracy value of 95%. These results were obtained using an augmented dataset with some parameter adjustments to the model built. With these good accurate identification, the MDPI Foundation is expected to better manage fisheries data and use it to support sustainable fisheries management.
Co-Authors Dewi, Ni Putu Dita Ariani Sukma Kadek Yota Ernanda Aryanto Made Windu Antara Kesiman Mupu, Dibi Ngabe Putra, I Kadek Nurcahyo