Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Bulletin of Computer Science Research Jurnal Pengabdian
Ricko Andreas Kartono
Unknown Affiliation
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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

Found 2 Documents
Search

 1 
Kegiatan Pelatihan dan Pengabdian kepada Masyarakat dalam Pemahaman Penggunaan Tools AI untuk Membuat Konten Christy, Christy; Jennifer Jocelyn; Jennifer Velensia Santoti; Nicholas Edison; Ricko Andreas Kartono; Hartati, Ery
Jurnal Pengabdian Vol. 4 No. 1 (2025): Januari-Juni
Publisher : Bengkulu Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58222/jp.v4i1.1283

Abstract

This training activity aims to improve the understanding of students of SMP Xaverius Maria Palembang regarding the use of artificial intelligence (AI) tools to create digital content, such as graphic design, video, and music. The hands-on practice-based training method is used to provide students with real experience in using AI applications, such as ChatGPT, Microsoft Designer, Luma AI, and Udio AI. The results of the training show that students not only understand the basic concepts of AI, but are also able to produce more innovative and creative content. Evaluation through interactive quizzes shows an increase in student understanding, although there are some challenges in understanding certain technical terms. This training has succeeded in encouraging students to become active and ethical digital creators, supporting their digital literacy to face the era of increasingly developing technology.
Perbandingan Kinerja Arsitektur MobileneTV2 dan MobileneTV3 Dalam Klasifikasi Penyakit Retina pada Citra Optical Coherence Tomography (OCT) Menggunakan Optimizer AdamW dan SGD Ricko Andreas Kartono; Nur Rachmat
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.915

Abstract

Retinal diseases are serious visual disorders that can lead to decreased visual function and even blindness. The diagnosis of retinal diseases is generally still performed manually by medical professionals through the examination of Optical Coherence Tomography (OCT) images, a process that requires considerable time, high precision, and is prone to diagnostic errors. Previous studies have mostly employed larger and more complex CNN architectures, with optimization limited to a few commonly used optimizers. This study aims to develop an automatic retinal disease classification model using Convolutional Neural Network (CNN) methods by leveraging the lightweight and efficient MobileNetV2 and MobileNetV3 architectures, enabling faster applications that can be deployed on resource-constrained devices. The architectures evaluated include MobileNetV2, MobileNetV3-Large, and MobileNetV3-Small, along with a comparison of two optimizers, namely AdamW and Stochastic Gradient Descent (SGD). The dataset used consists of 4,000 OCT images divided into four classes: Normal, Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen. The training process was conducted using a transfer learning approach, and model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that the combination of the MobileNetV2 architecture with a batch size of 16 and either the AdamW or SGD optimizer achieved the best performance, reaching an accuracy of 85.75%, which is the highest among all tested configurations. These findings highlight the strong potential of lightweight architectures to be developed into fast, accurate, and field-deployable retinal disease diagnostic applications on mobile devices using deep learning.
Co-Authors Christy, Christy Hartati, Ery Jennifer Jocelyn Jennifer Velensia Santoti Nicholas Edison Nur Rachmat