IMAGE CLASSIFICATION OF HOUSEHOLD BENEFICIARIES OF DIRECT CASH ASSISTANCE USING EFFICIENTNET IN DKI JAKARTA PROVINCE

Dzikri Adam; Hadi Santoso

doi:10.52436/1.jutif.2024.5.4.2121

Dzikri Adam Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Mercu Buana, Indonesia
Hadi Santoso Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Mercu Buana, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.4.2121

Keywords: Deep learning, EfficientNet, government aid, image classification, Jakarta Province, media bias

Abstract

This study investigates the application of the EfficientNet architecture for image classification to determine eligible recipients of direct cash assistance among households in Jakarta Province. As government efforts to provide aid to citizens increase, it becomes essential to have a system that can accurately recognize and classify eligible populations. Misallocation of aid remains a prevalent issue, often leading to undeserving individuals receiving assistance, which has detrimental consequences. The primary focus is on leveraging deep learning, specifically EfficientNet, to address these challenges. The dataset used consists of house images categorized into two classes: "Mampu" and "Tidak Mampu," which were collected through personal photography and web scraping from Google. The research aims to develop an algorithm that accurately classifies and analyzes the types and eligibility of residential buildings within the general population. Data collection and processing challenges are addressed to ensure the training of high-quality, representative image datasets. The model has demonstrated a high accuracy rate of approximately 95.03% on the validation data.

Downloads

Download data is not yet available.

References

M. I. Al-Amin, F. N. Sidiq, D. R. Ramdania, N. Fajar, Y. A. Gerhana, and M. Harika, “Spices Image Classification Using Support Vector Machine,” 2022 10th International Conference on Cyber and IT Service Management, CITSM 2022, vol. 647, pp. 990–993, 2022, doi: 10.1109/CITSM56380.2022.9935856.

Q. Xie, M. T. Luong, E. Hovy, and Q. V. Le, “Self-training with noisy student improves imagenet classification,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 10684–10695, 2020, doi: 10.1109/CVPR42600.2020.01070.

H. Phan, D. Huynh, Y. He, M. Savvides, and Z. Shen, “MoBiNet: A mobile binary network for image classification,” Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, pp. 3442–3451, 2020, doi: 10.1109/WACV45572.2020.9093444.

H. Abdu and M. H. Mohd Noor, “A Survey on Waste Detection and Classification Using Deep Learning,” IEEE Access, vol. 10, no. December, pp. 128151–128165, 2022, doi: 10.1109/ACCESS.2022.3226682.

H. Pan, Z. Pang, Y. Wang, Y. Wang, and L. Chen, “A New Image Recognition and Classification Method Combining Transfer Learning Algorithm and MobileNet Model for Welding Defects,” IEEE Access, vol. 8, pp. 119951–119960, 2020, doi: 10.1109/ACCESS.2020.3005450.

E. Elfatimi, R. Eryigit, and L. Elfatimi, “Beans Leaf Diseases Classification Using MobileNet Models,” IEEE Access, vol. 10, pp. 9471–9482, 2022, doi: 10.1109/ACCESS.2022.3142817.

R. H. Chan and R. Li, “A 3-Stage Spectral-Spatial Method for Hyperspectral Image Classification,” Remote Sensing, vol. 14, no. 16, pp. 1–22, 2022, doi: 10.3390/rs14163998.

B. Zheng, A. Gao, X. Huang, Y. Li, D. Liang, and X. Long, “A modified 3D EfficientNet for the classification of Alzheimer’s disease using structural magnetic resonance images,” IET Image Processing, vol. 17, no. 1, pp. 77–87, 2023, doi: 10.1049/ipr2.12618.

A. Dzieniszewska, P. Garbat, and R. Piramidowicz, “Improving Skin Lesion Segmentation with Self-Training,” Cancers, vol. 16, no. 6, 2024, doi: 10.3390/cancers16061120.

Y. Kaya and E. Gürsoy, “A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection,” Soft Computing, vol. 27, no. 9, pp. 5521–5535, 2023, doi: 10.1007/s00500-022-07798-y.

J. Huang et al., “BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images,” Bioengineering, vol. 9, no. 6, pp. 1–16, 2022, doi: 10.3390/bioengineering9060261.

T. Sanida, I. M. Tabakis, M. V. Sanida, A. Sideris, and M. Dasygenis, “A Robust Hybrid Deep Convolutional Neural Network for COVID-19 Disease Identification from Chest X-ray Images,” Information (Switzerland), vol. 14, no. 6, pp. 1–16, 2023, doi: 10.3390/info14060310.

K. Sabanci, “Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars,” Sustainability (Switzerland), vol. 15, no. 5, 2023, doi: 10.3390/su15054443.

K. Munadi et al., “A Deep Learning Method for Early Detection of Diabetic Foot Using Decision Fusion and Thermal Images,” Applied Sciences (Switzerland), vol. 12, no. 15, 2022, doi: 10.3390/app12157524.

Darmatasia and S. A. Muhammad, “Implementasi Convolutional Neural Network Untuk Klasifikasi Tanaman Rimpang Secara Virtual,” Jurnal INSTEK (Informatika Sains dan Teknologi), vol. 8, no. 1, pp. 122–131, 2023.

W. G. Pamungkas, M. I. P. Wardhana, Z. Sari, and Y. Azhar, “Leaf Image Identification: CNN with EfficientNet-B0 and ResNet-50 Used to Classified Corn Disease,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 2, pp. 326–333, Mar. 2023, doi: 10.29207/resti.v7i2.4736.

A. Pratiwi, and A. Fauzi, "Implementation Of Deep Learning On Flower Classification Using Cnn Method", Jurnal Teknik Informatika (JUTIF), vol. 5, no. 2, pp. 487–495, 2024 DOI: https://doi.org/10.52436/1.jutif.2024.5.2.1674.