Article Per Year (5 Year)
p-Index From 2020 - 2025
3.832
P-Index
This Author published in this journals
All Journal IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) JIEET (Journal of Information Engineering and Educational Technology) InComTech: Jurnal Telekomunikasi dan Komputer Jurnal ULTIMA Computing JURIKOM (Jurnal Riset Komputer) Majalah Ilmiah UNIKOM Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Infortech Computer Science (CO-SCIENCE) IMTechno: Journal of Industrial Management and Technology Simpatik: Jurnal sistem Informasi dan Informatika JUSTIN (Jurnal Sistem dan Teknologi Informasi) International Journal of Advanced Science Computing and Engineering Journal Medical Informatics Technology Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Waeisul Bismi
Universitas Bina Sarana Informatika
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Public Health Social Sciences Other

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jurnal Informatika dan Rekayasa Perangkat Lunak

 1 
Klasifikasi Citra Genus panthera Menggunakan Pendekatan Deep learning Berbasis Convolutional Neural network (CNN) Waeisul Bismi; Muhammad Qomaruddin
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 5, No 2 (2023): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v5i2.8931

Abstract

This research aims to develop an image classification method for the panthera genus using a deep learning approach based on Convolutional Neural network (CNN). The panthera genus includes large species such as tigers, lions, leopards, and jaguars, which share similarities in appearance but also differences in fur patterns, body size, and habitat. Image classification of the panthera genus is important in various applications, including wildlife conservation and biological research. In this study, image datasets of tigers, lions, and leopards were collected from various sources to a total of 6,290 images. The proposed method involves image pre-processing, such as resizing, converting and normalization, and the use of a Convolutional Neural network (CNN) model to perform classification. The CNN model is implemented and trained using training data to recognize specific visual patterns in the images of each species. The results of this study show that the CNN-based deep learning approach can achieve high accuracy in the classification of panthera genus images of 85.21%. This method can correctly distinguish between tiger, lion, and leopard images based on unique visual features. In addition, the deep learning approach also offers advantages in efficiency and scalability to cope with the large number of images in the dataset. This research makes an important contribution to the development of wildlife image classification methods using a CNN-based deep learning approach.
Analisis Perbandingan Klasifikasi Citra Genus Panthera dengan Pendekatan Deep learning Model MobileNet Waeisul Bismi; Deny Novianti; Muhammad Qomaruddin
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

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

Abstract

Wildlife conservation is increasingly becoming a top priority as several species in the Panthera genus have experienced significant declines in their populations since the 1970s, due to illegal hunting activities, loss of natural habitat, and reduced prey. Their protection is therefore of paramount importance. In today's digital age, image processing and artificial intelligence (AI) technologies have changed the way we view and protect wildlife. In this context, the approach of using MobileNet models in deep learning, which is a branch of artificial intelligence, has proven to be very effective in overcoming complex challenges in image processing. However, despite MobileNet's potential in classifying images of the Panthera genus, not many studies have specifically compared it with other existing methods. Therefore, in this study, a comparative analysis of Panthera genus image classification using the deep learning approach of MobileNet model with alternative models from previous studies is conducted. The dataset used consists of 6,460 images with 6 labels: Jaguar, Leopard, Lion, Lioness, Tiger, and Snow Tiger, which are divided into training, validation, and testing sets. Based on the evaluation results, the proposed method using the MobileNetV1 model achieved the highest accuracy of 89.93%, followed by the MobileNetV2 model with 89.78%. This research is expected to provide valuable insights in the development of system implementation in an application on various platforms for image detection, to support species conservation efforts in the Panthera genus.
Co-Authors Ade Setiawan Ade Setiawan Anisa Febriyani Anton . Arina Selawati Deny Novianti Dicky Hariyanto Dwiza Riana Dwiza Riana Fajar Shidiq Farid Ramadhan Firmansyah Firmansyah Gata, Windu Hani Harafani Hewiz , Alya Shafira Jordy Lasmana Putra Jufriadif Na`am, Jufriadif Maysaroh, Maysaroh Mohamad Noor Mugi Raharjo Muhammad Qomaruddin muhammad qomaruddin Musriatun Napiah Nurbaety Nurbaety Putri, Destiana Putri, Halimmatussa’diyah Qomaruddin, Muhammmad Rachmat Adi Purnama Rachmawati Darma Astuti Rachmawati Darma Astuti Raharjo, Mugi Rangga Wardhana Sakata, Yama Sulkhan Sandra Dewi Saraswati Taufik Asra Tommi Alfian Armawan Sandi Wijaya, Aryanata Windu Gata