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All Journal Jurnal Informatika Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika : Jurnal Informatika dan Teknologi Informasi International Journal of Advances in Intelligent Informatics Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah WIDYA LAKSANA SABDAMAS Science in Information Technology Letters International Journal of Robotics and Control Systems Applied Engineering and Technology Bulletin of Social Informatics Theory and Application Journal of Artificial Intelligence in Architecture
Andri Pranolo
Universitas Ahmad Dahlan
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Public Health Social Sciences Other

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Journal : Journal of Artificial Intelligence in Architecture

 1 
Application of Artificial Intelligence in Digital Architecture to Identify Traditional Javanese Buildings Sri Winiarti; Heri Pramono; Andri Pranolo
Journal of Artificial Intelligence in Architecture Vol. 1 No. 1 (2022): Artificial Intelligence for Enhancing Architectural Design
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jarina.v1i1.4916

Abstract

Traditional buildings have a cultural philosophy and characterize the culture of an area. The occurrence of environmental changes, population growth, and the growth of modern buildings impact traditional buildings. Therefore, preserving those traditional buildings is needed to avoid extinction and make as cultural assets. The research aims to develop an application to help architects quantitatively measure the content of traditional architectural styles in their designs. This study uses the Artificial Intelligence (AI) method to identify buildings' similarities, acquiring traditional building data in roofs and ornaments images as a dataset totaling 650 images of roofs and 7,180 ornaments. Data processing was carried out by making architectural models, training, testing accuracy, and creating application interfaces. The algorithm used to identify similarities between building types was the Convolutional Naural Network (CNN) and the Support Vector Machine (SVM). The results of the accuracy-test using the Confusion matrix method reached an accuracy value of 99.5% in identifying building similarities and 85% in classifying building types.
Co-Authors ., Suparman AA Sudharmawan, AA Abdalla, Modawy Adam Ali Achmad Fanany Onnilita Gaffar Adhi Prahara Adhi Prahara Adhi Susanto Afief Akmal Afiqa, Nurul Agung Bella Putra Utama Agus Dianto Agus Salim Aji Prasetya Wibawa Akbari, Ade Kurnia Ganesh Albas, Juan Alin Khaliduzzaman Andiko Putro Suryotomo Anton Satria Prabuwono Anton Yudhana Azhari, Ahmad Azlan, Faris Farhan Ba, Abdoul Fatakhou Bambang Widi Pratolo Camargo, Jair Dani Fadillah Elhindi, Mohamed Fachrul Kurniawan Fadhilla, Akhmad Fanny Felix Andika Dwiyanto Firdaus, Nalendra Firdaus, Nalendra Putra Ghazali, Ahmad Badaruddin Hanafi Hanafi Hariyanti, Nunik Heni Pujiastuti Heri Pramono Hoz, César De La Ismail, Amelia Ritahani Khadir, Mohammed Tarek Leonel Hernandez Leonel Hernandez, Leonel Mao, Yingchi Mirghani, Abdelhameed Mokhtar, Nur Azizah Mohammad Muhammad, Abdullahi Uwaisu Nanang Fitriana Kurniawan Nathalie Japkowicz Nisa, Syed Qamrun Noormaizan, Khairul Akmal Nor Amalina Abdul Rahim Nuril Anwar Nuryana, Zalik Omar, Abdalwahab Omer, Abduelrahman Adam Onie Yudho Sundoro Paramarta, Andien Khansa’a Iffat Prayitno Prayitno Rafal Drezewski Rafał Dreżewski Roman Voliansky Saifullah, Shoffan Sarina Sulaiman Sarina Sulaiman Seno Aji Putra Setyaputri, Faradini Usha Snani, Aissa Sri Winiarti Sularso Sularso, Sularso Suparman Supriadi Supriadi Taqwa Hariguna Tedy Setyadi Triono, Alfiansyah Putra Pertama Uriu, Wako Utama, Agung Bella Putra Wilis Kaswijanti Yingchi Mao Yingchi Mao Yingchi Mao Yingchi Mao Zhou, Xiaofeng