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All Journal Informatics for Educators and Professional : Journal of Informatics Jurnal Kajian Wilayah dan Kota
Zulkarnain Zulkarnain
Universitas Krisnadwipayana
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Engineering Environmental Science Social Sciences Other

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Analisis Daya Dukung Ketersediaan Lahan Untuk Pembangunan Perumahan di Perkotaan (Studi Kasus : Ketersediaan Lahan di Kota Depok) Zulkarnain Zulkarnain; Zefri Zefri; Jenniria Gukguk
Jurnal Kajian Wilayah dan Kota Vol 1 No 2 (2022): Jurnal Kajian Wilayah dan Kota Edisi Oktober 2022
Publisher : Prodi Kajian Pembangunan Perkotaan dan Wilayah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61488/jkwk.v1i2.315

Abstract

The carrying capacity of land availability for housing development in urban areas needs to be taken into account. So it is necessary to have an analysis that can calculate the carrying capacity of land availability for housing development that adjusts population growth in Depok City. The purpose of this research is to achieve land utilization for housing in accordance with spatial rules, with the objectives being land carrying capacity analysis to determine potential land, land capacity analysis and population analysis to determine land capacity for housing and population. The results of the research in sample locations that can represent the Depok City area show that the availability of potential land that can be developed for horizontal housing in 2038 has been greatly exceeded, meaning that there are a number of housing and a number of residents are not accommodated. And at present, based on the results of the site review, there are already several housing areas that are in locations that are not in accordance with spatial planning rules, these findings prove the hypothesis in this study.
Analisis Perbandingan Evaluasi Deep Learning Untuk Klasifikasi Gaya Arsitektur Darusman Darusman; Zulkarnain Zulkarnain
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 11 No. 1 (2026): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v11i1.3785

Abstract

This study compares the performance of several deep learning models in architectural style classification using architectural image datasets. The models used include InceptionResNetV2, VGG16, MobileNetV2, and ResNet50V2. The data is processed through image augmentation techniques to improve model generalization. Evaluation is carried out using accuracy, precision, recall, F1-score, and Confusion Matrix metrics to measure the effectiveness of the classification. The results show that InceptionResNetV2 and ResNet50V2 have the best performance with an accuracy of 84%, followed by MobileNetV2 (79%) and VGG16 (71%). More complex models show better ability in capturing visual patterns than lighter models. The results indicate that the use of deeper deep learning models can improve the accuracy of architectural classification. This research is expected to contribute to the development of more accurate and efficient architectural classification systems for various applications, including cultural conservation and architectural design.
Co-Authors Darusman Darusman Jenniria Gukguk Zefri Zefri