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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Pilar Nusa Mandiri Faktor Exacta JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Riset Informatika Jurnal SITECH : Sistem Informasi dan Teknologi Jusikom: Jurnal Sistem Informasi Ilmu Komputer JURSIMA (Jurnal Sistem Informasi dan Manajemen) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences Jurnal AbdiMas Nusa Mandiri RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Jurnal Responsif : Riset Sains dan Informatika Mediastima JURSIMA Journal Medical Informatics Technology Journal Information & Computer (JICOM) Jurnal Sistem Informasi dan Manajemen
Sulistyowati, Daning Nur
Universitas Nusa Mandiri
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : Journal of Applied Data Sciences

 1 
Evaluating Deep Learning Architectures for Potato Pest Identification: A Comparative Study of NasNetMobile, DenseNet, and Inception Models Hadianti, Sri; Riana, Dwiza; Sulistyowati, Daning Nur
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.v6i1.545

Abstract

Manual potato pest identification that is still applied today is often time-consuming and highly dependent on farmer skills in the field. This causes delays in taking action and inaccurate reporting, especially in pest emergencies. In addition, these limitations slow down the response to pest control which ultimately risks reducing crop yields and farmer income. This study aims to develop a more accurate, fast, and consistent deep learning-based approach to identify potato pests, in order to support practical solutions that farmers can implement independently. This study contributes by comparing three deep learning architecture models, namely NasNetMobile, DenseNet, and Inception which are designed to identify pest images. The potato pest image dataset used was collected from various sources equipped with an augmentation process to increase data diversity. The model was drilled using transfer learning techniques to utilize previously learned features on a large dataset. The evaluation model was carried out comprehensively based on accuracy, precision, and inference time efficiency. The results showed that the DenseNet model achieved the highest accuracy of 97% with an inference time of 11 seconds, and this model maintained a relatively stable performance and was superior several times compared to other models. Based on these results, DenseNet was chosen as the most effective and reliable model to be developed for practical applications in the field. This study provides practical implications in the form of providing a model that can be integrated into a mobile-based application that is easy to use by farmers, including in remote areas. This allows farmers to identify pests independently without requiring in-depth technical expertise. In addition, this study is a new benchmark for the development of artificial intelligence-based pest identification systems in other crops and opens up opportunities for integration with IoT-based technologies to support sustainable agricultural practices.
Improving Early Detection of Cervical Cancer Through Deep Learning-Based Pap Smear Image Classification Merlina, Nita; Prasetio, Arfhan; Zuniarti, Ida; Mayangky, Nissa Almira; Sulistyowati, Daning Nur; Aziz, Faruq
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.576

Abstract

Cervical cancer is one of the leading causes of death in women worldwide, making early detection of the disease crucial. This study proposes a deep learning-based approach that has the advantage of leveraging pre-trained models to save data, time, and computation to classify Pap smear images without relying on segmentation, which is traditionally required to isolate key morphological features. Instead, this method leverages deep learning to identify patterns directly from raw images, reducing preprocessing complexity while maintaining high accuracy. The dataset used in this study is a public data repository from Nusa Mandiri University (RepomedUNM), which has a wider variety of data. This dataset is used to classify images into four categories: Normal, LSIL, HSIL, and Koilocytes. The dataset consists of 400 images evenly distributed, ensuring class balance during training. Transfer learning is applied using five Convolutional Neural Network (CNN) architectures: ResNet152V2, InceptionV3, ResNet50V2, DenseNet201, and ConvNeXtBase. To prevent overfitting, techniques such as data augmentation, dropout regularization, and class weight adjustment are applied. The evaluation results in this study showed the highest accuracy with a value of ResNet152V2 = 0.9025, InceptionV3 = 0.8953 and DenseNet201 = 0.8845. ResNet152V2 excelled in extracting complex features, while InceptionV3 showed better computational efficiency. The study also highlighted the clinical impact of misclassification between Koilocytes and LSIL, which may affect diagnostic outcomes. Data augmentation techniques, including horizontal and vertical flipping and normalization, improved the model's generalization to a wide variety of images. Specificity was emphasized as a key evaluation metric to minimize false positives, which is important in medical diagnostics. The findings confirmed that transfer learning effectively overcomes the limitations of small datasets and improves the classification accuracy of pap smear images. This approach shows potential for integration into clinical workflows to enable automated and efficient cervical cancer detection.
Co-Authors Aditya, Mulyadi Ahmad Setiadi Angelia, Helen Ani Oktarini Sari Aziz, Faruq Dwi Arum Ningtyas Dwi Arum Ningtyas Dwiza Riana Eni Heni Hermaliani Fachri Amsury Fahrul Rozi Frieyadie Gata, Windu Haider Haider Hery, Moch Ida Zuniarti, Ida Imam Budiawan Irwansyah Saputra Ismail Hasan Jansen Pratama Setiawan Lumbantobing Jilan Jamilah Kadafi, Abdul Rahman Laureta, Wika Widya Mahendra Gilang Ramadhynara Maruloh Maruloh Mayangky, Nissa Almira Mohammad Badrul Muhamad Angga Maulana Muhammad Salbiyath Munawaroh, Fiqrotul Nanang Ruhyana Nanang Ruhyana Nita Merlina, Nita Norma Yunita Nurajijah Nurajijah Nurman Machmud Prasetio, Arfhan Reza Ema Furi Rifky Permana Ris yanto Risca Lusiana Pratiwi Risma Purnama Sari Sandra, Gardika Santoso Setiawan Saputra, Surya Fajar Septian Septian Septian, Septian Siti Fauziah Siti Fauziah Siti Fauziah Sri Hadianti Tammayyusdin, Aulia Salma Tati Mardiana Taufik Asra Tika Adilah M Yunita, Norma