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All Journal Indonesian Journal of Computing, Engineering, and Design Journal of Artificial Intelligence in Architecture
Nur Abdillah Siddiq
Department Of Nuclear Engineering And Engineering Physics, Faculty Of Engineering, Universitas Gadjah Mada
Arts Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Random Forest-based Fingerprinting Technique for Device-free Indoor Localization System Dwi Joko Suroso; Refa Rupaksi; Aditya Bagus Krisnawan; Nur Abdillah Siddiq
Indonesian Journal of Computing, Engineering, and Design (IJoCED) Vol. 3 No. 2 (2021): IJoCED
Publisher : Faculty of Engineering and Technology, Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/ijoced.v3i2.172

Abstract

The device-free indoor localization (DFIL) research is gaining attention due to the popularity of location-based service (LBS)-based advertisement. In DFIL, a user or an object does not need to bring any device to be localized. In this paper, we propose the Wi-Fi-based DFIL and the random forest algorithm for the fingerprint-based technique. The simple parameter commonly used in indoor localization is the Received Signal Strength Indicator (RSSI). We apply the fingerprint technique because of its reliability to handle the RSSI fluctuation and time-varying effect in a static indoor environment. We conducted an actual measurement campaign to observe the DFIL's implementation visibility. The DFIL system works by comparing the database fingerprint in an empty open office with the database in which a person is inside the measurement area without bringing any devices. Thus, we have the device-free RSSI database for fingerprint technique from both empty rooms and RSSI affected by a person inside the room. We validated the random forest algorithm results by comparing them with the k-nearest neighbor (kNN) and artificial neural network (ANN). The results show that our proposed system's accuracy is better than kNN and ANN with a mean error of 0.63 m than kNN with 0.80 m and ANN with 1.01 m. Meanwhile, the precision of the random forest is 0.63 m, whereas kNN and ANN are 0.67 m and 0.80 m, showing that the random forest performed better. We concluded that our simple DFIL system is visible to apply with acceptable accuracy performance.
Passive Window Energy Performance in Buildings: Modeling of Apartment Buildings in Indonesia Laurentius Kevin Hendinata; Nur Abdillah Siddiq; Ahmad Ilham Rokhul Fikri; Michael Alfano Suprapto; Ribka Prilia
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.v2i2.6729

Abstract

Along with urban growth in urban areas and energy consumption, which continues to increase every year, the selection of windows in the initial design is essential to obtain buildings that are not energy intensive. Selecting the correct window in the blueprint design reduces building energy consumption. Smart windows, especially thermochromic windows, are one of the most promising window technologies because they are the most economical and have passive control with zero energy input, which holds good promise for energy-saving applications. Apart from that, double-glazing windows are also frequently used in energy-saving applications. Therefore, a study compared the energy-saving potential of thermochromic and double-glazing windows to clear glass windows, using computer modeling through EnergyPlus, in high-rise apartment buildings in cities throughout Indonesia's diverse climates. From the modeling results, total energy consumption can be reduced by around 8.91% to 10.96% of total building energy consumption by replacing the conventional clear glass with double-glazing windows or more able to reduce about 20.22% to 24.19% by replacing the conventional clear glass with thermochromic windows. Furthermore, this potential varies depending on geometric shapes, materials, building facades, local climate, and building orientation. Nevertheless, considering the potential benefits, these windows are highly suitable for application in buildings seeking to reduce their energy consumption and improve energy efficiency.
Co-Authors Aditya Bagus Krisnawan Ahmad Ilham Rokhul Fikri Dwi Joko Suroso Laurentius Kevin Hendinata Michael Alfano Suprapto Refa Rupaksi Ribka Prilia