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Journal : Journal of Artificial Intelligence in Architecture

Utilizing the Use of Google Trends to Discover The Architectural Attractiveness of a Place in Indonesia Onie Dian Sanitha; Novera Kristianti; Theo Fransisco; Yunida Iashania
Journal of Artificial Intelligence in Architecture Vol. 2 No. 2 (2023): Artificial Intelligence for Enhancing Building Performance, Design, and Analysi
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jarina.v2i2.7483

Abstract

A person's memory of the image of a place is powerful, which is influenced by a uniqueness supported by continuity of information. Every visitor's image will be very subjectively evaluated through the internet information media. The challenge lies in subjective perception through internet media information which only sometimes leads to real quality due to perceptions formed from the continuity of information received. A shift in meaning allegedly led to a phenomenon of similar motivation between video game users and traveling for healing purposes. From the perspective of a Google search, the word "traveling" refers to buildings and places with ecological and natural concepts. Meanwhile, video games also offer a natural concept but are more competitive. Using Google Trends, this study attempts to formulate city popularity data using the keyword "traveling." Natural Ecology is the highest choice for social media users, and "healing" is the primary motivation. The value of natural architectural instruments can be a focus to support a sustainable development process for a place based on the popularity of today's needs.
Operational Energy Assessment and Selective Retrofit Strategy for a 24-Hour Cafe Using EDGE-Based Scenario Analysis Sanitha, Onie Dian; Iashania, Yunida; Kristianti, Novera; Rahayu, Elis Sri; Apriliyanti, Nia; Taufiqurahman, Taufiqurahman; Sompotan, Audy Mirelia Wirly; Sihombing, Yusuf Aditya
Journal of Artificial Intelligence in Architecture Vol. 5 No. 1 (2026): Artificial Intelligence for Human-Centric Performance: Integrating Neuroarchite
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jarina.v5i1.12400

Abstract

This research evaluates the energy performance of a 24-hour cafe in a tropical context using the EDGE decision-support platform. Cafe Oregano in Palangka Raya, Indonesia, was selected as a case study due to its continuous operation, refrigeration systems, and kitchen appliances, which generate persistent internal loads that challenge conventional assumptions about commercial building energy use. The EDGE baseline simulation produced a very high Energy Performance Index (EPI) of 778.53 kWh/m²/year, corresponding to a 32.50% relative performance, indicating a mismatch between standardised IFC assumptions and actual cafe operational behaviour. After parameter refinement and validation using monthly electricity bills, performance improved to 674.55 kWh/m²/year (+13.36%), reducing annual electricity consumption from 420,858 to 275,215 kWh. Sensitivity analysis showed that HVAC efficiency and zoning delivered the greatest performance gains, followed by envelope and lighting improvements, while refrigeration loads remained structurally dominant. To evaluate real-world feasibility, a selective retrofit scenario based on local Indonesian market costs was developed. The resulting package—roof and partial wall insulation, LED retrofitting, high-efficiency 1 HP HVAC replacement, and basic zoning controls—requires an estimated capital investment of IDR 310–320 million and achieves a simple payback of 1.5–2 years. Overall, the findings confirm that while EDGE effectively identifies relative performance trends, achieving meaningful energy efficiency in cafe typologies requires calibrated scenarios, realistic operational assumptions, and economically grounded interventions.