Article Per Year (5 Year)
p-Index From 2021 - 2026
4.936
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Applied Sciences Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab SISFOTENIKA INOVTEK Polbeng - Seri Informatika Jurnal Informatika Universitas Pamulang RESEARCH : Computer, Information System & Technology Management Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Telematika Informatika : Jurnal Informatika, Manajemen dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Elektronika Listrik dan Teknologi Informasi Terapan Jurnal Sistem Informasi dan Informatika (SIMIKA) Amalee: Indonesian Journal of Community Research & Engagement Jurnal Teknika Jurnal Sistem Komputer dan Informatika (JSON) KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Joutica : Journal of Informatic Unisla Jurnal Algoritma Insand Comtech : Information Science and Computer Technology Journal Jurnal Informatika: Jurnal Pengembangan IT Jurnal Ilmiah Sistem Informasi
Nafiiyah, Nur
Prodi Teknik Informatika Fakultas Teknik Universitas Islam Lamongan
Aerospace Engineering Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Neuroscience Physics Public Health Social Sciences Transportation Other

Published : 50 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 50 Documents
Search

 1   2   3   4   5 
Coastline segmentation on Landsat 8 OLI images using majority voting with deep learning models Nur Nafiiyah; Salwa Nabilah; Nur Azizah Affandy; Rifky Aisyatul Faroh; Esa Prakasa
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp588-596

Abstract

Coastlines are highly dynamic due to both natural processes and anthropogenic factors, including global warming and sea level rise. Accurate coastline segmentation is essential for effective monitoring and management. Although previous studies have applied deep learning for coastline detection, many existing models still suffer from instability across scenes, blurred boundaries, and segmentation artifacts, indicating that model generalization remains a challenge. This study aims to develop a more robust coastline segmentation approach by introducing an automated majority voting strategy that integrates three deep learning models: ResNet50, ResNet18, and MobileNet-V2. Landsat 8 OLI imagery is used for training and testing. The Jaccard index results show that ResNet18, ResNet50, and MobileNet-V2 achieved scores of 0.96, 0.98, and 0.95 respectively, while the proposed majority voting method also achieved 0.98. Despite the producing a similar numerical score to the best individual model (ResNet50), the ensemble method improves segmentation consistency by reducing artifacts such as unwanted peripheral shapes and cracks within land areas. These findings demonstrate that combining multiple segmentation outputs yields more stable and reliable coastline detection than using single models. Future work will apply this approach to broader Indonesian coastal regions to further assess its generalizability across diverse shoreline conditions.
Pendekatan Deep Learning untuk Deteksi Kanker Payudara Menggunakan Concatenated ResNet50 dan ResNet152 Nur Nafiiyah; M. Lazuardi Elsony; Agus Harjoko; Achmad Nizar Hidayanto
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3259

Abstract

Breast cancer is one of the leading causes of death in women, so accurate early detection is key to improving patient survival rates. Although mammography is the standard method for breast cancer screening, manual interpretation of mammogram images still depends on the expertise of radiologists and has the potential to lead to misdiagnosis. This study proposes a concatenate transfer learning-based deep learning approach by combining two Residual Network architectures, namely ResNet50 and ResNet152, to improve feature representation capabilities in mammogram image classification. The dataset used is a combination of MIAS and CBIS-DDSM with two classes, namely benign and malignant. The evaluation was carried out using original test data without augmentation to ensure the objectivity of the results. The experimental results show that the proposed model achieves an average accuracy of 97.09% and outperforms several individual transfer learning models. The main contribution of this study lies in demonstrating that combining deep features from similar but different depth CNN architectures can improve classification stability and accuracy. These findings provide a conceptual basis for the development of more reliable deep learning-based medical decision support systems for early breast cancer detection.
ANALISIS ARSITEKTUR CONVOLUTIONAL NEURAL NETWORK DAN TRANSFER LEARNING DALAM KLASIFIKASI KUALITAS BUAH: ANALYSIS OF CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE AND TRANSFER LEARNING IN FRUIT QUALITY CLASSIFICATION Muhammad Yusril Mushoffah Sekarwati; Nur Nafiiyah
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6622

Abstract

The quality of fresh fruits and vegetables plays a crucial role in consumer health. Manual assessment of product freshness is often ineffective because it is subjective and time-consuming. This study implements a Convolutional Neural Network (CNN) architecture and transfer learning using VGG16 and ResNet50 to classify the condition of fruits (apples and bananas) as fresh or rotten. The model design adapts previous research by modifying the input image size, restricting the target labels to four classes (freshapples, freshbanana, rottenapples, and rottenbanana), and removing the Dropout layer. The dataset, obtained from Kaggle, includes two fruit types (apples and bananas) and two conditions (fresh and rotten). The experimental results show that VGG16 achieved the highest accuracy at 93.9%, outperforming both ResNet50 and the custom CNN model. The custom CNN exhibited notably lower performance, indicating its limited ability to extract deep hierarchical features compared to pretrained architectures. These findings highlight the effectiveness of CNN-based transfer learning for supporting automated classification of fresh agricultural products.  
PERBANDINGAN METODE CLUSTERING K-MEANS++, DBSCAN, DAN AGGLOMERATIVE PADA DATA KUESIONER DASS-21 Nur Nafiiyah
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6980

Abstract

This study aims to compare the performance of three clustering methods k-Means++, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Agglomerative Clustering in grouping data obtained from the Depression Anxiety Stress Scale (DASS-21) questionnaire. The questionnaire data represent three primary psychological dimensions: depression, anxiety, and stress. Performance evaluation was conducted by calculating the degree of agreement between the clustering results and the actual classes determined based on DASS-21 scores. The analysis results show that k-Means++ achieved the highest agreement rate of 35.63%, followed by Agglomerative Clustering at 34.39% and DBSCAN at 26.81%. These findings indicate that k-Means++ is more effective in forming clusters that align with the psychological dimensions present in the DASS-21 data. However, the relatively low agreement rates suggest overlapping emotional dimensions and the inherent complexity of psychological data. Therefore, further research is required through parameter optimization, data preprocessing, or the application of hybrid approaches to improve the quality and representativeness of the clustering results.  
PERBANDINGAN VGG16, VGG19, MOBILENET DAN MOBILENETV2 UNTUK KLASIFIKASI BERAS BERDASARKAN CITRA Nur Nafiiyah; Dwi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7338

Abstract

Rice is the main staple food for the majority of the Indonesian population; therefore, rice quality plays an important role in maintaining national food security. However, rice quality assessment in Indonesia is still largely conducted manually, making it subjective and inconsistent. Consequently, the application of image processing techniques and deep learning algorithms is required to achieve a more objective and accurate classification process. This study aims to compare the performance of four Convolutional Neural Network (CNN) architectures, namely VGG16, VGG19, MobileNet, and MobileNetV2, in classifying five types of rice: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The dataset used consists of 4,000 images that were processed directly without additional preprocessing stages. The experimental results show that MobileNet achieved the highest accuracy of 99% with a training time of 756 seconds. Meanwhile, MobileNetV2 and VGG16 achieved accuracies of 98% with training times of 728 seconds and 1,502 seconds, respectively, while VGG19 produced the lowest accuracy of 97% with a training time of 1,038 seconds. Based on these results, it can be concluded that the MobileNet architecture demonstrates the best performance in classifying the five rice varieties in the dataset used.
KLASIFIKASI BUAH SAYUR FRESH DAN ROTTEN MENGGUNAKAN MOBILENETV2 DAN XCEPTION Nur Nafiiyah; Ahmad Fauzil Adhim Febrian
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7664

Abstract

The advancement of artificial intelligence and computer vision has enabled automated quality assessment in agricultural products to reduce subjectivity and inefficiency in manual inspection. This study aims to compare the performance of two transfer learning architectures, MobileNetV2 and Xception, for fruit and vegetable freshness classification. The dataset was obtained from Kaggle and focused on eight classes: fresh and rotten apples, bananas, cucumbers, and tomatoes. To address data imbalance and improve model generalization, data augmentation techniques including rotation, horizontal flipping, and vertical flipping were applied. Both pre-trained models were enhanced by adding a Global Average Pooling layer, a 128-neuron Dense layer, and Batch Normalization. The models were trained using the Adam optimizer with a learning rate of 0.0001 for 20 epochs. Performance evaluation was conducted using accuracy, precision, and recall metrics. Experimental results show that MobileNetV2 achieved an accuracy of 99.43%, while Xception obtained 99.39%, with consistently high precision and recall values. These findings demonstrate that the transfer learning approach is highly effective for fruit and vegetable freshness classification and provides competitive performance compared to previous studies.
KLASIFIKASI ULKUS KAKI DIABETIK MENGGUNAKAN CONVNEXT-TINY, DENSENET201, MOBILENETV2, RESNET50 Nur Nafiiyah; Ahmad Farish Subbanuddin Alawy
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7666

Abstract

Diabetic Foot Ulcer (DFU) is a serious complication of diabetes that may lead to amputation if not diagnosed early. Manual visual identification of DFU is challenging due to the heterogeneous characteristics of ulcer shape and texture. This study proposes an automatic DFU classification approach based on transfer learning using four Convolutional Neural Network (CNN) architectures: ConvNeXt-Tiny, DenseNet201, MobileNetV2, and ResNet50. The dataset consists of 1,055 original images (512 ulcer and 543 normal) used as testing data, while the training data were generated through augmentation techniques including rotation, horizontal flip, vertical flip, contrast adjustment, and brightness enhancement, resulting in 5,275 training images. All models were trained with 224×224×3 image input, using the SGD optimizer with a learning rate of 0.0001 for 30 epochs and a batch size of 2. Model performance was evaluated using accuracy, precision, and recall metrics. Experimental results indicate that ResNet50 achieved the best performance with 99.60% accuracy, 99.33% precision, and 99.88% recall. ConvNeXt-Tiny and DenseNet201 also demonstrated competitive performance with accuracy above 98%, while MobileNetV2 achieved 92.85% accuracy. Comparative analysis with previous studies shows that the proposed approach achieves competitive results. The findings demonstrate that transfer learning combined with appropriate data augmentation can produce an accurate and reliable DFU identification system to support computer-aided diagnosis.
Sea Land Segmentation of East Java’s North Coast Using Landsat 9 and ResNet50 Nur Nafiiyah; Ilyas; Rifky Aisyatul Faroh; Salwa Nabilah; Nur Azizah Affandy
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7435

Abstract

Coastal regions are among the most vulnerable ecosystems due to the combined impacts of natural processes and human activities. Climate change, population growth, and coastal development accelerate shoreline dynamics, increasing the need for accurate and efficient coastal monitoring. Satellite-based remote sensing, combined with deep learning techniques, provides a promising solution for large-scale and continuous shoreline analysis. This study proposes a deep learning–based approach for coastal land–sea segmentation using the ResNet50 architecture applied to Landsat 9 OLI imagery of the North Coast of East Java, Indonesia. The dataset consists of multispectral images processed into 224×224 pixel tiles, accompanied by manually generated ground truth segmentation maps. Two optimization strategies, Adam and Stochastic Gradient Descent (SGD), are evaluated to determine the most effective optimizer for improving segmentation performance. Experimental results demonstrate that the Adam optimizer outperforms SGD across multiple training epochs, achieving the highest segmentation accuracy with mean Intersection over Union (IoU) and Dice coefficient values of 0.888 and 0.934, respectively. These findings indicate that optimizer selection significantly influences the performance of ResNet50-based coastal segmentation. The proposed approach shows strong potential for supporting automated and large-scale coastal monitoring applications using medium-resolution satellite imagery.
Performance of majority voting transfer learning deep learning monkeypox disease detection Nur Nafiiyah; Muhammad Nurul Huda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3782-3791

Abstract

Global health concerns have been raised by the advent of monkeypox following the COVID-19 epidemic, highlighting the need for reliable automated systems to support early skin disease screening. This study proposes a monkeypox skin disease classification framework using a majority voting ensemble based on transfer learning. The ensemble combines predictions from multiple pretrained convolutional neural networks (CNNs) to improve classification robustness. A publicly accessible dataset comprising four classes: monkeypox, chickenpox, measles, and normal skin was used for the experiments. The results indicate that while a single ResNet50 model achieved the highest overall accuracy (99.15%), the majority voting approach yielded higher precision than several individual models, demonstrating improved reliability in distinguishing monkeypox cases. These findings suggest that ensemble-based majority voting can enhance the robustness of monkeypox skin disease classification and may support computer-aided screening systems.
KLASIFIKASI KEMATANGAN BUAH KELAPA SAWIT BERBASIS TRANSFER LEARNING CONVOLUTIONAL NEURAL NETWORK Nur Nafiiyah; Mohamad Habib Havito Ihzami; Agus Harjoko; Achmad Nizar Hidayanto
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4539

Abstract

The ripeness level of oil palm fruit directly affects the quality and yield of palm oil, making accurate and objective classification methods essential. This study aims to evaluate and compare the performance of several transfer learning Convolutional Neural Network (CNN) architectures for oil palm ripeness classification. The dataset used is a secondary dataset obtained from previous research and consists of four ripeness classes: under-ripe, unripe, ripe, and over-ripe. Data augmentation was applied only to the training data to increase data variability, while the original, non-augmented data were used for testing to ensure an objective evaluation. Seven CNN architectures were evaluated, namely ConvNeXt-Tiny, DenseNet121, InceptionV3, MobileNetV2, NASNetLarge, ResNet50, and Xception, using the same training configuration. The results show that ConvNeXt-Tiny and ResNet50 achieved the best performance, with accuracy and F1-scores of 97.73%. These findings indicate that efficient CNN architectures can provide optimal performance for oil palm ripeness classification.  
Co-Authors ., Ichsan Yudistura Achmad Nizar Hidayanto Agus Harjoko Agus Setia Budi, Agus Setia Agus Subhan Akbar, Agus Subhan Ahmad Farish Subbanuddin Alawy Ahmad Fauzil Adhim Febrian Alhidayati Asymal Ali Mokhtar Anny Yuniarti Arif Try Hidayatullah Ayu Ismi Hanifah Bagus Nur Bakti Aji Chastine Fatichah Dwi edy susanto Eha Renwi Astuti Eko Sulistiono Sulistiono Esa Prakasa Esa Prakasa, Esa Husnul Khotimah Ika Purnama Sari Ilyas Irma Dwi Rohmawati jacinda, nisrina Jauharul Maknun Khoiruddin, Muhammad Ubay Khotim Hidayati Hidayati M Sholihul Hadi M. Lazuardi Elsony Masruroh Masruroh Miftahus Sholihin Moh Muslikh Mohamad Habib Havito Ihzami Muchammad Khudori Muhammad Ali Haydar Muhammad Nurul Huda Muhammad Nurul Huda Muhammad Yusril Mushoffah Sekarwati Munir Munir, Munir Nabilah, Salwa Nela Nevrivanti Aulia Prasetyo, Puguh Rouf Purnomo Hadi Susilo, Purnomo Hadi Putra, Ramadhan Hardani Retno Wardhani Rifky Aisyatul Faroh Rifqi Firdaus Al Jauhari Rizki Ardhian Ahmad Setyan, Ardath Prahara Sholikah Indrawati Sholikha, Ni'matus Siti Mujilahwati tsabit yunan al rajab Yusuf Eka Yana Yusuf Permadi Zuhri, Ahmad Syafiq Maulana