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All Journal Jurnal Informatika Progres
M. Agusalim
Pengairan, Universitas Muhammadiyah Makassar
Computer Science & IT

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PENERAPAN RESNET50 DAN SWIN TRANSFORMER PADA IDENTIFIKASI CITRA PENYAKIT DAUN KELAPA SAWIT Siti Marwa; Muhammad Faisal; Muhyiddin A.M Hayat; Nurnawaty; Andi Makbul Syamsuri; M. Agusalim
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.470

Abstract

This study aims to implement and compare the performance of ResNet50 and Swin Transformer models in classifying palm oil leaf diseases. The decline in palm oil productivity is often caused by disease infections such as Curvularia (leaf spot) and Leaf Rust, necessitating a fast and precise automated identification system. This experimental computational research used a primary dataset of 600 digital images proportionally divided into training, validation, and testing sets. The preprocessing stage included resolution adjustment (resizing), data augmentation to prevent overfitting, and normalization. Model performance evaluation was conducted quantitatively through Confusion Matrix calculations and validated qualitatively through heatmap visualization using the Gradient-weighted Class Activation Mapping (Grad-CAM) method. The test results proved that the ResNet50 architecture outperformed the Swin Transformer with an accuracy of 98.00%, precision of 98.01%, recall of 98.00%, and F1-score of 98.00%, compared to the Swin Transformer's accuracy of 96.00%. Grad-CAM analysis also confirmed that ResNet50 is sharper in specifically localizing local infection areas. Overall, it is concluded that the ResNet50 model is more optimal, stable, and recommended for the palm oil leaf disease classification system in this dataset domain.
KLASIFIKASI PENYAKIT PNEUMONIA MENGGUNAKAN MODEL HYBRID CNN-TRANSFORMER BERBASIS CITRA X-RAY PARU-PARU Nur Milani Hidayah; Muhammad Faisal; Desi Anggreani; Nurnawaty; Andi Makbul Syamsuri; M. Agusalim
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.471

Abstract

This study aims to apply a Hybrid CNN-Transformer model based on Medical Vision Transformer (MedViT) for pneumonia classification using chest X-Ray images. The dataset consisted of 450 images, including 150 pneumonia images, 150 non-pneumonia images, and 150 random images as a control class to test system robustness. The data were obtained from Labuang Baji Hospital, Makassar, during the 2023 to 2025 period. The research stages included data collection, preprocessing, augmentation, dataset splitting, model implementation, training, and performance evaluation. The tested models consisted of CNN, Vision Transformer (ViT), and Hybrid CNN-Transformer. The evaluation used accuracy, precision, recall, F1-score, AUC, confusion matrix, ROC curve, and Grad-CAM visualization. The results showed that the Hybrid CNN-Transformer model achieved the best performance with an accuracy of 95.59%, precision of 96.12%, recall of 95.59%, F1-score of 95.58%, and AUC of 0.9968. The model improved accuracy by 8.83% compared with CNN and produced fewer classification errors. The Grad-CAM visualization also indicated that the model focused on relevant lung areas. These findings indicate that combining CNN local feature extraction with Transformer global context can improve pneumonia classification based on medical images..
PREDIKSI KEBUTUHAN STOK OBAT MENGGUNAKAN METODE HYBRID LONG SHORT-TERM MEMORY (LSTM) DAN CATBOOST Parwati Parwati; Muhammad Faisal; Muhyiddin A.M Hayat; Nurnawaty; Andi Makbul Syamsuri; M. Agusalim
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.472

Abstract

Drug inventory planning in primary health facilities requires an accurate forecasting model because fluctuating demand can trigger stockouts or excess inventory. This study develops and evaluates a hybrid Long Short-Term Memory (LSTM) and CatBoost model for predicting the stock requirements of five essential medicines at Puskesmas Pattingalloang. The dataset consists of monthly drug dispensing records from January 2019 to December 2025. LSTM is applied as a temporal feature extractor with a three-month sliding window, while CatBoost functions as the final nonlinear regression estimator. Model performance is assessed using MAE, RMSE, MAPE, and SMAPE, with a single LSTM model used as the baseline comparison. The results show that model suitability depends on the demand pattern of each medicine. The hybrid LSTM-CatBoost model performs better on highly fluctuating medicines, particularly Paracetamol 500 mg with 24.03% SMAPE and Guaifenesin with 37.28% SMAPE. In contrast, the single LSTM model is more efficient for relatively stable demand, especially Blood Supplement Tablets with 14.09% SMAPE. Forecasting for 2026 also provides annual demand estimates that can support data-driven drug requirement planning. These findings indicate that machine learning-based forecasting is useful for pharmaceutical inventory decision support, but model selection must consider the fluctuation characteristics of each drug.
Co-Authors Andi Makbul Syamsuri Desi Anggreani Muhammad Faisal Muhyiddin A.M Hayat Nur Milani Hidayah Nurnawaty Parwati Parwati Siti Marwa