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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences
Kharismawardani, Aqila
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Enhancing Apple Leaf Disease Detection with Deep Learning: From Model Training to Android App Integration Santoso, Cahyono Budy; Singadji, Marcello; Purnama, Denny Ganjar; Abdel, Saimam; Kharismawardani, Aqila
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.507

Abstract

This study presents an innovative approach to enhance apple leaf disease detection using deep learning by comparing three models: ReXNet-150, EfficientNet, and Conventional CNN (ResNet-18). The objective is to identify the most accurate and efficient model for real-world deployment in resource-constrained environments. Utilizing a dataset of 1,730 high-quality images, the models were trained using transfer learning, achieving significant results. ReXNet-150 outperformed other models with an F1-score of 0.988, precision of 0.989, and recall of 0.989. EfficientNet and ResNet-18 demonstrated competitive performances with F1-scores of 0.966 and 0.977, respectively. The integration of the ReXNet-150 model into a TensorFlow Lite-based Android application ensures real-time detection, enabling farmers and researchers to capture or upload images for immediate classification. The findings highlight ReXNet-150's robustness, achieving a test accuracy of 98.9% and minimal misclassification, making it ideal for practical agricultural applications. The novelty lies in bridging advanced deep learning with mobile deployment, addressing real-world constraints. Future work could extend this framework to multi-crop disease detection and real-time video analysis, providing scalable solutions for precision agriculture.
Analisis Prediktif Faktor Kematian Balita di Bandung menggunakan Logistic Regression, Random Forest, dan XGBoost Kharismawardani, Aqila; Purnama, Denny Ganjar
TIN: Terapan Informatika Nusantara Vol 6 No 6 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i6.8594

Abstract

The Under-Five Mortality Rate (UFMR) is a crucial issue in Indonesia that requires data-driven interventions. This study aims to develop a predictive model to identify the most influential risk factors for under-five mortality in Bandung City and to compare the performance of three machine learning algorithms. This research utilizes secondary data from the Bandung City Open Data portal for the period 2019-2021. The method employed is a comparative analysis of Logistic Regression, Random Forest, and XGBoost. To address the significant class imbalance in the data, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training data. The evaluation results show that all three models achieve high accuracy, however, performance on the minority calss (mortality cases) remains challenging, indicated by low F1-scores (0.12 for Random Forest and 0.17 for XGBoost). Nonetheless, the feature importance analysis from the Random Forest model successfully identified 'other causes' (penyebab_LAIN-LAIN), 'fever' (penyebab_DEMAM), and the availability of healthcare professionals (PERAWAT, BIDAN) as the most significant predictors. This study highlights the insight from feature importance in identifying risk factors in imbalanced medical data, providing a basis for more targeted health policy recommendations.
Co-Authors Abdel, Saimam Marcello Singadji Purnama, Denny Ganjar Santoso, Cahyono Budy