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All Journal JTERA (Jurnal Teknologi Rekayasa) Jurnal Pendidikan Teknik Bangunan
Pratama, Naufal Ariq
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation Other

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Analisis Perbandingan Biaya dan Waktu pada Pekerjaan Pagar Samping dengan Dinding Pracetak dan Dinding Konvensional Pratama, Naufal Ariq; Sofia, Dewi Ayu
JTERA (Jurnal Teknologi Rekayasa) Vol 9, No 2: December 2024
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v9.i2.2024.169-174

Abstract

Perkembangan pesat di bidang konstruksi mendorong inovasi dalam komponen bangunan untuk meningkatkan efisiensi, daya tahan, dan keberlanjutan. Salah satu inovasi tersebut adalah pengembangan material dinding, yaitu dinding pracetak yang menawarkan keunggulan dalam kekuatan, kemudahan pemasangan, serta efisiensi energi. Pada penelitian ini dianalisis perbandingan penggunaan dinding pracetak dan dinding konvensional pada proyek pembangunan pagar samping pada bangunan X di Kota Sukabumi yang ditinjau dari aspek biaya dan waktu. Hasil analisis menunjukan bahwa penggunaan dinding pracetak lebih hemat sekitar 37% dibandingkan dengan dinding konvensional. Durasi penyelesaian proyek pembangunan pagar samping dengan menggunakan dinding pracetak membutuhkan waktu 67 hari, sedangkan dengan dinding konvensional memerlukan 116 hari. Oleh karena itu dapat disimpulkan bahwa pembangunan pagar samping dengan dinding pracetak menjadi pilihan yang lebih efisien dalam hal biaya dan waktu pelaksanaan proyek dibandingkan dengan metode konvensional.
A Bibliometric Analysis of Artificial Intelligence (AI) Applications in Hydrological Modeling Sofia, Dewi Ayu; Pratama, Naufal Ariq
Jurnal Pendidikan Teknik Bangunan Vol 5, No 1 (2025): Jurnal Pendidikan Teknik Bangunan
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jptb.v5i1.87062

Abstract

The rapid advancement of artificial intelligence (AI) has opened new opportunities for developing hydrological models that are more adaptive, accurate, and efficient. This study aims to examine the research trends and directions concerning the application of AI in hydrological modeling using bibliometric analysis. A total of 136 relevant articles published between 2015 and 2025 were retrieved from the Semantic Scholar database using the Publish or Perish software. These records were then analyzed with VOSviewer to map keyword relationships and identify current research focuses. The results reveal a consistent upward trend in AI-based hydrological modeling publications, particularly since 2020. Among the top 15 cited articles, a total of 2,316 citations were recorded, averaging 17.03 citations per article. Keywords such as “LSTM,” “RNN,” “streamflow,” and “hydrological forecasting” appeared with the highest frequency and recentness, signifying a shift toward more adaptive and predictive modeling approaches. Furthermore, density visualization highlights a strong focus on deep learning models particularly LSTM and Support Vector Machines while showing opportunities for further exploration in hybrid models and climate resilience applications. Although limited to a single database, the study provides a methodologically robust overview of the current research landscape. The findings underscore the transformative role of AI, not merely as a computational tool, but as a key enabler for designing hydrological models that are data-driven, responsive, and capable of supporting sustainable water resource management in the face of environmental uncertaintie.
Co-Authors Dewi Ayu Sofia