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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Teknik Hidro Rancang Bangun NUSANTARA : Jurnal Ilmu Pengetahuan Sosial Bandar: Journal of Civil Engineering Jurnal Rekayasa Infrastruktur Hexagon Nusantara Hasana Journal Buletin Sistem Informasi dan Teknologi Islam Jurnal Informatika Progres Jurnal Karajata Engineering Journal of Embedded Systems, Security and Intelligent Systems Kohesi: Jurnal Sains dan Teknologi Jurnal Mediasas : Media Ilmu Syari'ah dan Ahwal Al-Syakhsiyyah Journal of Muhammadiyah’s Application Technology
Nurnawaty
Universitas Muhammadiyah Makassar
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Nursing Social Sciences Transportation Other

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Eksperimentasi Peran Serat Alam Pada Tanah Lempung Dalam Upaya Peningkatan Infiltrasi Dan Plastisitas Tanah Nurnawaty; Abd Rakhim Nanda; Tendri Ajeng; Intan Fadillah
Jurnal Karajata Engineering Vol. 5 No. 1 (2025): 2025
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/karajata.v5i1.3446

Abstract

Clay soil consists of a type with limited load-bearing ability and low shear strength, thus it is essential to enhance the soil's stability, as this is influenced by the moisture level. The amount of water obtained by the soil depends on the soil's ability to absorb and channel the water received from the soil surface to the lower layers. Therefore, alternative materials are needed to improve the properties of the soil. This study aims to determine the effect of adding natural fibers on the infiltration and plasticity of clay soil. This research was a laboratory based testing, where infiltration testing was carried out using channel media and soil stability testing using the Atterbeg method. The addition of natural fibers to clay soil affects the infiltration discharge. The highest infiltration discharge is clay soil with the addition of rice husks and the lowest infiltration discharge is clay soil with a mixture of coconut fiber. Adding rice husks to clay soil can reduce the plastic index number which is 6%.
Identifikasi Sebaran Intrusi Air Laut Melalui Profil Kualitas Air Sumur Di Pantai Barombong Nurnawaty; M. Agusalim; Muh. Nurul Fitrah S; Ranum Indah Putri
Jurnal Karajata Engineering Vol. 5 No. 2 (2025): 2025
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/karajata.v5i2.3634

Abstract

Rapid development in Makassar City increases the demand for housing, clean water and industry, which encourages massive exploitation of groundwater. If this continues, it could degrade groundwater quality and quantity and trigger seawater intrusion, threatening groundwater availability in the region. The aim of this research is to analyze and identify measurements and values of salinity due to sea water on ground water quality at Barombong Beach. In this research, a research l ocation survey was carried out, sampling stage, sample testing stage, data processing stage, community interview stage. The well water at Barombong Beach has a salinity that is still categorized as fresh to brackish. Well water that has a salinity of <0.5% then the water is categorized as fresh, >0.5-30%, then the water is categorized as brackish and if the water salinity is >40% then the water is detected as salty. From the results of this research calculation, the highest salinity is found in well 15, namely 5% at a distance of 858.76 from the shoreline and the lowest is located at wells 1 and 2, namely 0.2% which is located at a distance of 165.98 m and 201.06 m from the shoreline.
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.
Ensemble Learning for Android Privacy-Risk Flow Pre-Screening Using Permission and Metadata Features Tri Wahyuni; Muhammad Faisal; Titin Wahyuni; Nurnawaty; Rio Prasetyo Lukodono; Titik Khawa Abd Rahman
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13180

Abstract

Purpose - This study develops a privacy-oriented pre-screening framework for identifying Android applications with potential Sensitive Data Exposure by combining lightweight permission and metadata features with ensemble learning. Design/methods/approach - Android applications obtained from the AndroZoo repository were analyzed using FlowDroid to construct reference labels based on sensitive source–sink flows. Privacy-oriented features were derived from permissions, application metadata, source and sink indicators, and interaction patterns. An Ensemble Stacking model integrating Random Forest, Support Vector Machine, and Extreme Gradient Boosting with Logistic Regression as the meta-classifier was evaluated under class imbalance. Additional circularity, ablation, repeated validation, and clean-feature experiments were conducted to assess robustness and deployment feasibility. Findings - The proposed framework demonstrated strong capability in distinguishing applications containing FlowDroid-defined potential privacy-risk flows. FlowDroid-derived source and sink indicators were highly discriminative, while permission-only features were less effective. Importantly, the clean-feature configuration retained strong discriminatory capability without requiring FlowDroid at inference time, supporting its use as a lightweight first-stage screening mechanism before more computationally intensive taint analysis. Research implications/limitations - The framework can support developers, security auditors, and platform administrators in prioritizing applications for deeper privacy inspection. However, the study relies on static analysis, a single application repository, and FlowDroid-derived reference labels. Originality/value - This study contributes a two-stage Android privacy-risk screening framework that combines lightweight deployable features with targeted static taint analysis while explicitly addressing label-feature circularity and inference-time feasibility.
Co-Authors A.M Radinal Mukhtar Aan Ardiansyah Abdul Hakim Abdul Rakhim Nanda Agusalim, Agusalim Ahmad Fajrin Alamsyah - Amrullah Mansida Anas, Andi Bunga Tongeng Andi Alyah Ayu Mariska Waris Andi Asmi Rani Andi Bunga Tongeng Andi Bunga Tongeng Anas Andi Makbul Syamsuri Andi Nurannisa Andi Reza Gifari Andi Rini Septiani Andi Ulfa Mutiah Asrul Aswandi Ismail Bakti, Rizki Yusliana Berni Satria Gemilang Besse Emmy Saphira Desi Anggreani Erika Yanti Ery Sanbrualim Syawal Fausiah Latief Fausiah Latief Fausiah Latif Fauzan Hamdi Fauzan Hamdi Fauzan Hamdi, Fauzan Fenty Daud S Fithriyah Arief Wangsa Fitrida, Fitrida Gaffar, Farida Gemilang, Berni Satria Habibur Fathur Rahman Ihwan Ihwan Indah Fadhilah Isha Indriyanti Indriyanti Indriyanti Indriyanti Indriyanti, Indriyanti Intan Fadillah Isha, Indah Fadhilah Ismail S itha, Nur Masita Karim, Nenny Kasmawati Kasmawati Khafifa Khafifa Khafifa, Khafifa Latief, Fausiah M Agusalim M Agusalim Mahmuddin Mahmuddin Mahmuddin Mansida, Amrullah Marupah Marupah Marupah, Marupah Marwah Ahmad Mirna Safitri Mohamad Munawir Muh. Nurul Fitrah S Muhaemina Muhamemina Muhammad Faisal Muhammad Hasraddin Hasnan Muhammad Syafaat S. Kuba Muhyiddin A.M Hayat Muhyiddin A.M Hayat Munawir, Mohamad Mutiah, Andi Ulfa Nenny Karim Nirwana Nilan Ramdhani Nur Isra Nur Milani Hidayah Parwati Parwati Ramdhani, Nirwana Nilan Ranum Indah Putri Rian Sophian Rio Prasetyo Lukodono Rizky Yusliana Bakti Septiani, Andi Rini Siti Marwa Suhardiman Suhardiman Sukmasari Antaria sumardi Sumardi Syamsiar, Syamsiar Tahir, Sulqadri Tendri Ajeng Titik Khawa Abd Rahman Titik Khawa Abd Rahman Titik Khawa Abdul Rahman Titin Wahyuni Tongeng, Andi Bunga Toni Aprilian Putra Tri Wahyuni Wahyudi Wahyudi Wangsa, Fithriyah Arief Yunita Afliani Rahayu Yusril Yusril Yusril Yusril