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All Journal Applied Information System and Management Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Sukma Evadini
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Computer Science & IT Languange, Linguistic, Communication & Media

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Analisa Faktor yang Mempengaruhi Kondisi Kesehatan Menggunakan Algoritma Frequent Pattern Growth Sukma Evadini; Alwis Nazir; Yusra Pizaini
Applied Information System and Management (AISM) Vol 1, No 1 (2018): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1113.969 KB) | DOI: 10.15408/aism.v1i1.8646

Abstract

Health is an important factor in human life that have to be guarded, both physically and mentally. This study aimed to analyze the factors that affect health condition using medical check up data. Factors analyzed were consuming alcohol, smoking, exercise, age and gender. The method was the association rule using FPGrowth. The result of this study was factors that affect the health condition is alcohol, exercise and age. This result evidenced by the rules A3→K3, which means that if a person consumes more alcohol than 4 days/week with the amount of alcohol is less than 180ml/day, then health condition was poor with 11% support and 67% confidence. E1→K3, which means that if one rarely exercise then health condition was poor with 24% support and 99% confidence. G2→K3, which means that if a person in middle age group, then the condition of health was poor with 24% support and 99% confidence.
Comparative Analysis of Deep Learning Models for Predicting Undernourishment Prevalence in Indonesia Sukma Evadini; Nadya Satya Handayani
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Undernourishment constitutes a critical public health challenge in Indonesia with significant impacts on human resource quality and economic productivity. Accurate prediction of undernourishment prevalence is essential for supporting early warning systems and evidence-based food security policy planning. This study conducted comprehensive comparative analysis of three deep learning architectures—Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer—for predicting Prevalence of Undernourishment (PoU) using longitudinal data from 38 Indonesian provinces spanning 2018-2024 with 242 observations. Methodology encompasses systematic preprocessing with minmaxscaler normalization, 70:15:15 dataset split, implementation of three models with hyperparameter tuning via grid search, and evaluation using RMSE, MAE, R², and MAPE. Results demonstrate Transformer achieves superior performance with RMSE 286.02, MAE 217.79, R² 0.8822, and MAPE 48.11%, outperforming GRU (RMSE 315.19, R² 0.8570) and LSTM (RMSE 356.81, R² 0.8167). Learning curves analysis reveals Transformer exhibits faster convergence and smaller training-validation gap (0.075) compared to LSTM (0.10) and GRU (0.105), indicating superior generalization. Although Transformer exhibits higher computational complexity (125,248 parameters), the accuracy-efficiency trade-off remains favorable with inference time of 8.6ms per sample. Transformer superiority stems from its multi-head self-attention mechanism effectively capturing long-term temporal dependencies and complex non-linear patterns. Findings provide evidence-based recommendations for implementing Transformer in food security early warning systems, supporting targeted resource allocation, and contributing to Sustainable Development Goals achievement related to zero hunger.
Co-Authors Alwis Nazir Nadya Satya Handayani Pizaini Pizaini