Pemanfaatan Metode Convolutional Neural Network (CNN) Dengan Arsitektur MobileNetV2 Untuk Penilaian Kelayakan Rumah

In the digitalization era, efforts to enhance efficiency and accuracy in assessing household eligibility for social assistance have become important. We plan to develop a household eligibility assessment system using convolutional neural network methods. Our system uses artificial intelligence to analyze uploaded house photos and provide eligibility assessments based on predefined criteria. The goal is to automate the assessment process, increase efficiency and accuracy, ensure social assistance is well-targeted, and reduce administrative workload. Through the implementation of this system, we hope to improve the effectiveness of social assistance and community development. The result of this research is a household eligibility assessment program that can be implemented in the future.

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Publication Details

Journal
ALINIER Journal of Artificial Intelligence & Applications
Published
2024-11-04
DOI
https://doi.org/10.36040/alinier.v5i2.11061
Primary Topic
Computer Science and Engineering
Type
article
Field-Weighted Citation Impact
0.00
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article

Pemanfaatan Metode Convolutional Neural Network (CNN) Dengan Arsitektur MobileNetV2 Untuk Penilaian Kelayakan Rumah

Anggraini Puspita Sari, Muhammad Rahman, Ahmad Zidan
ALINIER Journal of Artificial Intelligence & Applications
Computer Science and Engineering
article

Pemanfaatan Metode Convolutional Neural Network (CNN) Dengan Arsitektur MobileNetV2 Untuk Penilaian Kelayakan Rumah

Anggraini Puspita Sari, Muhammad Rahman, Ahmad Zidan
article en

Abstract

In the digitalization era, efforts to enhance efficiency and accuracy in assessing household eligibility for social assistance have become important. We plan to develop a household eligibility assessment system using convolutional neural network methods. Our system uses artificial intelligence to analyze uploaded house photos and provide eligibility assessments based on predefined criteria. The goal is to automate the assessment process, increase efficiency and accuracy, ensure social assistance is well-targeted, and reduce administrative workload. Through the implementation of this system, we hope to improve the effectiveness of social assistance and community development. The result of this research is a household eligibility assessment program that can be implemented in the future.

ALINIER Journal of Artificial Intelligence & ApplicationsVol. 5(2)
University of Pembangunan Nasional Veteran Jawa Timur (ID)
Reduced inequalities
Openalex Percentile: Top 82%
Computer Science and Engineering
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Pemanfaatan Metode Convolutional Neural Network (CNN) Dengan Arsitektur MobileNetV2 Untuk Penilaian Kelayakan Rumah — Anggraini Puspita Sari, Muhammad Rahman, et al. · ALINIER Journal of Artificial Intelligence & Applications (2024) | TGRS Research Map | TGRS