Article Per Year (5 Year)
p-Index From 2021 - 2026
4.596
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) snimed Jurnal Ilmiah Farmasi Scientific Journal of Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah Jiko (Jurnal Informatika dan komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) Indonesian Journal of Business Intelligence (IJUBI) PREPOTIF : Jurnal Kesehatan Masyarakat JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI Jurnal Sistem Informasi dan Sistem Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal Research of Social Science, Economics, and Management Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Keilmuan dan Aplikasi Teknologi Industri
Fudholi, Dhomas Hatta
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

Published : 36 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : International Journal of Artificial Intelligence Research

 1 
Mi-Botway: a Deep Learning-based Intelligent University Enquiries Chatbot Windiatmoko, Yurio; Hidayatullah, Ahmad Fathan; Fudholi, Dhomas Hatta; Rahmadi, Ridho
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (478.614 KB) | DOI: 10.29099/ijair.v6i1.247

Abstract

Intelligent systems for universities that are powered by artificial intelligence have been developed on a large scale to help people with various tasks. The chatbot concept is nothing new in today's society, which is developing with the latest technology. Students or prospective students often need actual information, such as asking customer service about the university, especially during the current pandemic, when it is difficult to hold a personal meeting in person. Chatbots utilized functionally as lecture schedule information, student grades information, also with some additional features for Muslim prayer schedules and weather forecast information. This conversation bot was developed with a deep learning model adopted by an artificial intelligence model that replicates human intelligence with a specific training scheme. The deep learning implemented is based on RNN which has a special memory storage scheme for deep learning models, in particular in this conversation bot using GRU which is integrated into RASA chatbot framework. GRU is also known as Gated Recurrent Unit, which effectively stores a portion of the memory that is needed, but removes the part that is not necessary. This chatbot is represented by a web application platform created by React JavaScript, and has 0.99 Average Precision Score.
A Mobile Deep Learning Model on Covid-19 CT-Scan Classification Susanto, Prastyo Eko; Kurniawardhan, Arrie; Fudholi, Dhomas Hatta; Rahmadi, Ridho
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (386.607 KB) | DOI: 10.29099/ijair.v6i1.257

Abstract

COVID-19 pandemic is currently happening in the world. Previous studies have been done to diagnose COVID-19 by identifying CT-scan images through the development of the novel Joint Classification and Segmentation System models that work in real-time. In this study, the author focuses on a different motivation and innovation focused on the development of mobile deep learning. Mobile Net, a deep learning model as a method for classifying the disease COVID-19, is used as the base model. It has a good level of efficiency and reliability to be implemented on devices that have small memory and CPU specifications, such as mobile phones. The used data in this study is a CT-scan image of the lungs with a horizontal slice that has been classified as positive or negative for COVID-19. To give a broader analysis, the author compares and evaluates the model against other architectures, such as MobileNetV3 Large, MobileNetV3 Small, MobilenetV2, ResNet101, and EfficientNetB0. In terms of the developed mobile architecture model, the classification of COVID-19 using MobileNetV2 obtained the best result with 0.81 accuracy.
Co-Authors Achmad Zahra, Rizqatasyaa Ahmad Fathan Hidayatullah, Ahmad Fathan Al-Faruq, Umar Abdul Aziz Al-Sabahi, Abdullah Yahya Moqbel Alhanafi, Ahmad Azzam Andaru, Gabriel Imam Arifin, Dimastyo Muhaimin Arrie Kurniawardhani Arrochman, Ibnu Fajar Avilba, Pandu Bagus W, Alan Tusa Basbeth, Faishal Bauravindah, Achmad Chairullah Naury Dewa, Chandra Kusuma Eka Handayani Eko Prasetio Widhi Fahmi, Yunizar Fikri, Khairul Hardiyanti, Mawar Kurniawardhan, Arrie Lizda Iswari Manoppo, Tommy Nugraha Mustofa, M Najmudin, Nabil Naury, Chairullah Nayoan, Royan Abida N Nayoan, Royan Abida N. Nur Iman Pailus, Muhammad Ridho Rahmadi Rima Tri Wahyuningrum Saefudin Saefudin Satyatama, Mochamad Rezky Septia Rani Siti Mutmainah Suci Hanifah Suryatin, Suryatin Susanto, Prastyo Eko Suyuti, Maghfirah Syarif Hidayat Wijayanti, Shofia Yasmin, Saarah Muthiah Yoga Sahria Yudi prayudi Yurio Windiatmoko, Yurio Zahra, Annisa