Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Terpadu Journal of Soft Computing Exploration
Sihombing, Jeremia Jordan
Unknown Affiliation
Computer Science & IT Engineering

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

Found 2 Documents
Search

 1 
Klasifikasi Jenis Burung menggunakan Metode Transfer Learning Pane, Yeremia Yosefan; Sihombing, Jeremia Jordan
Jurnal Teknologi Terpadu Vol 9 No 2 (2023): Desember, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i2.744

Abstract

Indonesia is known for its abundant natural resources, including its diverse bird fauna. The identification and classification of bird species is essential in maintaining biodiversity as well as for practical habitat management. Therefore, an efficient and accurate approach is needed to identify bird species. This study uses a deep learning approach to test and compare the MobileNetV2 architecture with architectures used in previous studies in recognizing bird species. We use a transfer learning approach that utilizes existing knowledge from pre-trained models and combines it with a Convolutional Neural Network (CNN) algorithm to detect and classify birds based on images with a total image data of 95376. Experimental results show that by using the MobileNetV2 architecture, we achieved an accuracy of 96.4% with a loss value of 0.241. Compared with the architecture used in previous research, our results show a significant improvement in accuracy and efficiency. The time taken to perform the classification at each step is about 646 ms. This study shows that using MobileNetV2 architecture in the transfer learning approach with CNN effectively performs bird species classification.
Implementation of text summarization on indonesian scientific articles using textrank algorithm with TF-IDF web-based Sihombing, Jeremia Jordan; Arnita, Arnita; Al Idrus, Said Iskandar; Niska, Debi Yandra
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.475

Abstract

The development of information technology has significantly changed how information is accessed, necessitating readers to absorb content efficiently and make quick decisions. To address this challenge, this research developed a text summarization system specifically for Indonesian scientific articles using a web-based implementation of the TextRank and TF-IDF algorithms. TextRank was selected for its capability to identify key sentences without requiring training data, while TF-IDF was employed to weight words based on their frequency within the document. The dataset comprised 100 scientific articles in Indonesian from the Unimed Kode Journal, covering the years 2022-2024. The summarization process included several critical stages: text preprocessing, TF-IDF weighting, cosine similarity calculation, and sentence ranking. The resulting summaries were rigorously evaluated by language experts and website specialists using a Likert scale to assess both the quality of the summaries and the usability of the system. The findings demonstrated that the system effectively generated summaries that retained essential information from the original articles, with the highest accuracy observed at a 50% compression rate (88.533%). Additionally, the system achieved good performance at 40% compression (85.133%) and 30% compression (81.26%). The web-based system allows users to input article text and quickly obtain a summary, offering a practical tool for researchers and readers to efficiently comprehend academic content.
Co-Authors Arnita Arnita Niska, Debi Yandra Pane, Yeremia Yosefan Said . Iskandar