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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Artificial Intelligence Research J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika)

Ridho Rahmadi

Islamic University of Indonesia

Author-ID : 355455

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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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

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.

Causal Relations of Factors Representing the Elderly Independence in Doing Activities of Daily Livings Using S3C-Latent Algorithm Tou, Nurhaeka; Rahmadi, Ridho; Effendy, Christantie
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

The growth of the elderly population in Indonesia from year to year has always increased, followed by the problem of decreasing physical strength and psychological health of the elderly. These problems can affect the increase in dependence and decrease the independence of the elderly in ADL. In previous studies, various factors affect independence in ADLs such as cognitive, psychological, economic, nutrition, and health. However, In general, these studies only focus on predictive analysis or correlation of variables, and no research has attempted to identify the casual relationship of the elderly independence factors. Therefore, this study aimed to determine the mechanism of the causal relationship of the factors that influence the independence of the elderly in ADLs using a casual method called the Stable Specification Search for Cross-Sectional Data With Latent Variables (S3C-Latent). In this research we found strong causal and associative relationships between factors.The causal relationship of elderly independence in ADLs was influenced by cognitive, psychological, nutritional and health factors and gender with Î± values respectively (0.61; 0.61;1.00, 0.65;0.70). Cognitive factors associated with psychological, economic, nutrition, and health with a value of Î± (0.77; 1.00; 1.00; 0.64). Furthermore, psychological factors associated with economy, nutrition, and health with a value of Î± (0.77; 0.95; 0.63). Bisides, economic factors are associated with nutrition and health with Î± values of ( 0.86; 0.75) and nutrition with health with Î± values of 0.64. The last association was found between nutritional factors and gender with a value of Î± 0.76. This research is expected to increase the independence of the elderly in carrying out daily activities.

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

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 Ahmad Fathan Hidayatullah, Ahmad Fathan Anugrah Galang Persada, Anugrah Galang Christantie Effendy Desyandri Desyandri Fachry Abda El Rahman Fudholi, Dhomas Hatta Ipin Prasojo, Ipin Kurniawardhan, Arrie Nayoan, Royan Abida N Noor Akhmad Setiawan Nur Azizah Rizki Surtiyan Surya Sri Kusuma Dewi, Sri Kusuma Supu, Nisfa Daud Susanto, Prastyo Eko Suyuti, Maghfirah Taufiq Hidayat Tito Yuwono, Tito Tou, Nurhaeka Yurio Windiatmoko, Yurio

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