Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Software Engineering and Information System (SEIS)
Marta, Puji Pramudya
Unknown Affiliation
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
MODEL PREDIKSI PENERIMA BANTUAN SOSIAL BERBASIS ALGORITMA RANDOM FOREST Sukma, Siti Hatmara; Suarna, Nana; Bahtiar, Agus; Marta, Puji Pramudya; Anam, Khaerul
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 6 No. 1 (2026)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v6i1.10526

Abstract

Inaccurate targeting of social assistance beneficiaries remains a critical issue at the village level due to subjective and inconsistent manual verification processes. This study aims to develop a predictive model for determining social assistance eligibility using the Random Forest algorithm based on 2021 SDGs Village microdata from Cibeureum Village. The research involves data preprocessing, model training, and hyperparameter optimization, with performance evaluation using accuracy, precision, recall, and F1-score metrics. The proposed model achieved an accuracy of 94.34%, indicating strong and stable classification performance. Feature importance analysis shows that housing conditions, access to clean water, and asset ownership are the most influential socioeconomic indicators. These findings demonstrate that Random Forest can effectively support data-driven decision-making and improve the accuracy of social assistance distribution at the village level.
Application of Weighted Loss Function in Convolutional Neural Network for Acne Image Classification Abubakar Sidik; Purnamasari, Ade Irma; Pratama, Denni; Marta, Puji Pramudya; Wijaya, Yudhistira Arie
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1885

Abstract

Automated acne image classification using Convolutional Neural Networks (CNN) holds significant potential in dermatological diagnosis but faces a fundamental challenge of class imbalance. This phenomenon causes standard models to be biased towards majority classes and fail to recognize clinically important minority classes. This study aims to address this bias by applying a Weighted Loss Function to the EfficientNetB1 architecture. The research method employs a comparative experimental approach between two scenarios: the Baseline model (Standard Cross-Entropy) and the Proposed model (Weighted Cross-Entropy). The dataset consists of 5 acne classes with an imbalanced distribution. The results show that the Weighted Loss model significantly outperforms the Baseline model. Overall accuracy increased from 80% to 86%. The most significant improvement occurred in the minority class 'Papules', where the F1-Score surged by 0.10 points (from 0.71 to 0.81). It is concluded that the application of Weighted Loss Function effectively overcomes bias due to imbalanced data without the need for synthetic data augmentation, resulting in a fairer and more reliable model for clinical implementation.
Co-Authors Abubakar Sidik Khaerul Anam Pratama, Denni Purnamasari, Ade Irma Suarna, Nana Sukma, Siti Hatmara Yudhistira Arie Wijaya