Article Per Year (5 Year)
p-Index From 2021 - 2026
1.879
P-Index
This Author published in this journals
All Journal MEDIA KOMUNIKASI TEKNIK SIPIL Jurnal Sosial Budaya Prosiding Semnastek Semesta Teknika Teknisia Jurnal Teknik Sipil Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil Jurnal Arsitektur dan Perencanaan (JUARA) KURVATEK International Journal of Engineering, Technology and Natural Sciences (IJETS) Jurnal Teslink : Teknik Sipil dan Lingkungan Construction and Material Journal APPLIED RESEARCH ON CIVIL ENGINEERING AND ENVIRONMENT (ARCEE) Jurnal Sosial dan Teknologi Jurnal Penelitian Inovatif STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer
Fitri Nugraheni
Universitas Islam Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Public Health Social Sciences Transportation

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Implementation of Artificial Neural Network (ANN) for identifying design indicators of temporary modular shelters Sari, Sely Novita; Sarwidi; Nugraheni, Fitri; Musyafa', Albani
Teknisia Vol 30 No 2 (2025): Teknisia
Publisher : Jurusan Teknik Sipil, Fakultas Teknik Sipil dan Perencanaan, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/teknisia.vol30.iss2.art6

Abstract

The demand for fast, efficient, and adaptive emergency housing continues to increase, especially in disaster-prone areas and large-scale displacement situations. The determination of the design of Temporary Modular Shelter (TMS) so far still depends a lot on subjective considerations, so a more systematic and data-based approach is needed. This study develops and validates an Artificial Neural Network (ANN) model to identify the most suitable TMS design based on performance indicators and expert assessment. The approach was carried out through the Systematic Literature Review (SLR) stage, the determination of eight key design indicators, and assessment by 150 multidisciplinary respondents. The ANN model was built using a dense four-layer architecture with a total of 1,780 parameters and trained for 400 epochs using the TensorFlow and Keras libraries. The results showed a validation accuracy of 96% and a macro F1-score of 0,9146, indicating the stability and reliability of the model. Analysis of the contribution of features with the SHAP method revealed that the indicators of assembly methods, availability of human resources, and availability of local materials had the greatest influence on the classification results. This model has proven to be effective as a decision support system that is able to increase objectivity and efficiency in the TMS design process. Further development is suggested through integration into web-based digital platforms or mobile applications to support rapid and adaptive emergency response planning.
Co-Authors - Istikhomah Abma, Vendie Adwitya Bhaskara Albani Musyafa Albani Musyafa, Albani Arasya Maharika, Raka Affa Armansyah, Bagos Fadillah, Titania Nur Faisol A.M Faisol AM Hanafi, N. Faried HARI PURNOMO Jamal Nur Alim Lalu Makrup Lutfi Andriawan Putra Maharika, Ilya Fadjar Makrup, Lalu Medilla Kusriyanto, Medilla Metalindra Metalindra Metalindra, Metalindra Muhammad, Faiz Ihsan Muntafi, Yunalia Musyafa', Albani Musyafa’, Albani N. Faried Hanafi Nurmianto, Awaluddin Panca Winahyuningsih Risdiyono Risdiyono Rudatin, Ari Saputra, Elvis Sarwidi Sarwidi Sarwidi, Sarwidi Sely Novita Sari Setya Winarno Sigit Yasien Sujarwo, Ari Sulistio, Adi Suswardana, Suswardana Tri Nugroho Sulistyantoro Vendie Abma Widodo . Widodo Pawirodikromo Yasien, Sigit